The Professional Constructor Journal

IN THIS ISSUE:

● A Framework for Small-Scale Construction Projects by NGOs in Developing Countries

● Influence of Climatic Parameters on the Performance of Solar Photovoltaic Modules in a Subtropical Area

● Project Delivery Methods for the Construction of Public Schools in the Southeastern United States

● Construction Degree Graduates: An Evaluation of Depth of Skill Understanding and Skill Priority by Construction Industry Professionals

● Accident Patterns in Road Construction Work Zones

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The Professional Constructor (ISSN 0146-7557) is the official publication of the American Institute of Constructors (AIC), 700 N. Fairfax St. Suite 510 Alexandria, VA 22314. Telephone 703.683.4999, Fax 703.683.5480, www.professionalconstructor.org.

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F A L L 2 0 1 5 | V O L U M E 3 9 | N U M B E R 0 2

EDITORJason D. Lucas, Ph.D., Assistant Professor, Clemson University A Framework for Small-Scale Construction Projects

by NGOs in Developing Countries ..........................................................................5

Goedert and Cory

Influence of Climatic Parameters on the Performance of Solar Photovoltaic Modules in a Subtropical Area .......................................................17

Jiang, Buzaianu, and Malek

Project Delivery Methods for the Construction of Public Schools in the Southeastern United States ......................................................................27

Carpenter and Bausman

Construction Degree Graduates: An Evaluation of Depth of Skill Understanding and Skill Priority by Construction Industry Professionals ......................................................................................................37

Bigelow, Escamilla, and Kuecker

Accident Patterns in Road Construction Work Zones ..........................................46

Shehab and Phu

Reviewer Thank You ............................................................................................58

Fall 2015 — Volume 39, Number 02The American Institute of Constructors | 700 N. Fairfax St., Suite 510 | Alexandria, VA 22314 | Tel: 703.683.4999 | www.professionalconstructor.org

5

A Framework for Small-Scale Construction Projects by NGOs in Developing Countries

James D. Goedert, Ph.D., P.E., F.NSPEUniversity of Nebraska | [email protected]

Bradley Cory, Ph.D. CandidateLEED AP BD&C, University of Nebraska | [email protected]

Keywords: Frameworks, Developing Countries, Construction, and Sustainability

INTRODUCTION

The number of small-scale construction projects by non-government organizations (NGOs) in developing countries has risen dramatically in the last few decades. There was a 450 percent increase in the number of international NGOs from 1990-2000 (Yaziji 2004). The desire of Western nations to aid developing countries is evidenced by extensive funding. Nearly 2.5 trillion dollars has been given in foreign aid by Western nations since World War II (Easterly 2006). However, inadequate planning and coordination with local partners is

evidenced in a multitude of half-finished projects, unused or repurposed buildings, and donated equipment rusting away in many developing countries (Corbett and Fikkert 2009). An integrated and multidisciplinary approach is needed to design, construct and manage these projects for long-term operational sustainability.

This paper describes a theoretical framework for sustainable small scale construction projects based on the integrative project delivery (IPD) approach by the American Institute of Architects (AIA) and illustrated by actual project. The framework guides a process that supports locally sustainable operations long after NGO support is withdrawn. This is consistent with the construction research agenda in developing nations as

ABSTRACT: This paper outlines a theoretical framework for NGOs who are building small-scale construction projects in developing countries. The framework is a step-by-step procedure to guide NGOs from developing project parameters at the start of the project with the partner to a locally controlled sustainable project closeout. The authors used the integrative project delivery approach along with past construction research models and personal experience to guide the development of the framework. Steps within the framework are illustrated using five projects from three countries. This work assumes that the ultimate objective of the project is local operational sustainability with the NGO’s presence and support withdrawn as appropriate. NGOs that utilize the framework will find its guiding principles grounded in both theory and practice.

Dr. James D. Goedert, P.E., F.NSPE, is a Professor at the University of Nebraska (UNL), He has a B.S. in Construction Engineering Tech-nology from the University of Nebraska Omaha, an MBA from Indiana University, and a Ph.D. in the Interdisciplinary area of Business Administration from UNL. He is a licensed Professional Engineer in Nebraska and Indiana. His research interest includes residential en-ergy efficient construction and sustainable construction in developing countries. He has been involved in development project in Haiti, Mali, Togo, Uganda, and Tanzanian.

Bradley Cory, LEED AP BD&C, is a graduate student at the University of Nebraska (UNL). He has a B.S. in Engineering with a major in Construction Management from UNL. He is currently pursuing his Doctoral degree in sustainable construction. His research interests are net zero energy homes, testing the effectiveness of energy improvements in existing residential homes, and to design more successful sustainable construction methods in developing countries.



6 A Framework for Small-Scale Construction Projects by NGOs in Developing Countries

outlined by Ofori (2006) and DuPlessis (2002). Small-scale construction projects for purposes of this document are defined as projects less than 1 million United States (US) dollars (Long et al. 2004) and ranging from 1-2 weeks to 2 years (Corbett and Fikkert 2009).

NGO employees and stakeholders are often involved for altruistic intentions and may dedicate their lives to this service. However, they may lack the experience and/or expertise needed to increase their effectiveness during the project process. This theoretical framework provides guidance based on the experience, expertise, and failures in an effort to improve future project outcomes.

The author incorporates five projects from three countries to illustrate the various phases of the IPD and steps of the framework to providing the reader with a more thorough grounding of the theory in practice. Three projects in Haiti include an elementary school, a solar installation at an orphanage and a composting latrine. A hospital in Mali, West, Africa and a composting latrine in Togo, West Africa are also mentioned. Each project enjoyed successes and each some failures. The successes and failures are used in this document to illustrate a point within the theoretical framework and should not be considered indicative of a specific NGO, country or project.In addition to the cases mentioned, references will be made to Engineers without Borders protocols as applicable. Engineers Without Borders (EWB) is an NGO that has over 12,000 members in the United States and 350 projects in 45 developing countries that are predominantly small-scale construction projects. A review of the literature demonstrates the need for a theoretical framework that more clearly develops small-scale construction project for NGOs in developing countries with a focus on both construction and operational sustainability from concept through completion.

LITERATURE REVIEW

Construction research is often under-utilized as a means for improving processes even when the improvements have monumental implications. Ofori (2007) states, “researchers today tend to cover the same ground that was comprehensively dealt with many years ago, and little that is new has emerged for many years.” The research applicable to this study is summarized in the following subsections Developing

Country Frameworks, Sustainability in Developing Countries, Codes and Regulations, and Performance Measures.

Developing Country FrameworksA 1984 World Bank publication was one of the earliest to provide a comprehensive set of topics for organizing a study within a developing country (World Bank 1985). Rather than providing general parameters, like the World Bank Study, a host of other studies by Wekesa and Steyn (2010), Hill and Bowen (1997), Wang et al. (2004), give more specific frameworks relating to sustainability, risk, and performance criteria.

Ofori (2007) notes that the cultural differences in developing countries are critical and, as a result, most studies and/or framework designs are specific to an individual country. For example, a study by Ali and Nsairat (2009) developed a green building assessment tool specifically for Jordan. Furthermore, resource availability, geographic differences and a host of other factors vary from one country to another and play an important role in the success of the construction project. A dissertation by Ozolins (2010) determined the applicability of green building rating system that is heavily weighted on local materials in Madagascar and Tanzania. It is important when developing a framework to consider the local context and regional factors specific to the area to ensure sustainability.

Sustainability FrameworksResearch points to the growing necessity for more sustainable construction solutions in developing countries (Ofori 2007). The World Summit on Sustainable Development (2002) refers to sustainability as a key strategy for the elimination of poverty (United Nations 2002). Additionally, Hill and Bowen (1997) provide a detailed review of sustainable construction, and describe it as a set of four processes of social, economic, biophysical, and technical. These processes guide those in developed nations to maximize resources, minimize environmental impact, preserve energy, and create more self-sustaining structures that can be easily operated by locals (Hill and Bowen 1997). As a result, these principles of sustainable construction should serve as the underpinnings to any twentieth century construction research framework.

Wekesa and Steyn (2010) expounded upon the processes developed by Hill and Bowen (1997) to create


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a framework using three factors of socio-economic criteria, technical criteria, and environmental criteria to help the urban poor with optimal material selection. The framework uses an objective hierarchy model and a combination of qualitative and quantitative data to analyze different materials and building systems. Varying technologies were defined for foundation, ground floor, external wall, and roof. The study established its own criteria and building technologies based on the regional context of the area.

Several researchers including Chelishe and Yirenkyi-Fianko (2011), Striebig et al. (2012), and Ali and Nsairat (2009) integrated their frameworks into a computer-based software called Aspire. ASPIRE was the result of a rigorous two year consultation and testing program by a number of well-established parties (Aspire R&D 2011). The software is grouped under four key sustainability metrics of environments, society, economics and institutions. The themes allow the user to make decisions about best case and worst-case scenarios. It provides an effective conceptual framework for evaluating and rating different metrics of sustainability. The four primary metrics presented coincide with well-established sustainability principles such as the four pillars outlined in Hill and Bowen (1997). Because Aspire is primarily an assessment tool used for meeting sustainability objectives, it does not provide a methodology for the design and construction procurement stages as noted by Striebig et al. (2012).

Successfully implementing computer-based models in developing country can be a difficult undertaking, especially where little infrastructure exists. It is unlikely that the developing country will have the proper local training and access to computers to successfully utilize a software program for computer-based models without the NGO providing it.

Building Codes and RegulationsMany developing countries do not have well-established building codes and governmental regulations, making it more difficult to establish performance standards and evaluate risk at the beginning stages of construction projects. Becker (2002) created a methodology using the South African building standards to establish performance criteria. Industry professionals from the area were asked to rate the different technologies in order to arrive at a consensus. A simple additive weighting (SAW) was used to aggregate the scores

based on a weighting criteria of 1-5. The performance criteria determined what materials and building systems would be included in the construction process based on the weighted criteria rating each received from the survey.

Three general categories of risk were established by Hastak and Shaked (2000): country level, market level, and project level. Smaller scale NGO projects primarily deal with risks on the project level, but some risks within the country and market level should also be considered (Chileshe and Yirenkyi-Fianko 2011). Becker (2002), Hastak and Shaked (2000), and Chileshe and Yirenkyi-Fianko (2011) provide frameworks for improving building standards and risk. The work of these authors help assess the level of design needed on a project for it to be built in a structurally sound and safe manner. Most NGO designs for buildings in developing countries will exceed the local performance standards simply because the standards do not exist or are extremely low by comparison. In addition, the NGOs home country will likely have access to well-established building codes that will necessarily influence design decisions. One challenge is to balance the code requirements from the NGO’s country with the reality of resource constraints in the under developed nation. Understanding the risks and developing performance standards will increase the probability of project success including safer building practices.

Performance MeasurementsSpecifying project criteria within a framework that defines success, results in better resource distribution and provides a baseline for measuring the project’s success (Ahadzie, 2008). Other researchers (Odusami 2003; Mbach and Nkado 2007; Shen and Liu 2003; Lim and Mohammed 1999) identified the importance of success criteria in developing countries. Chan (2004) built upon previous performance frameworks to develop a Key Performance Index (KPI). The KPI outlines key objective measures and subjective measures using a combination of a qualitative seven point scale and quantitative methods using formulae defined in the study for assessment. It is effective at providing a well-defined process for determining project success that includes a logical research-based process.

Some frameworks like Wekesa and Steyn (2010), and Strieberg et al. (2012) outline a framework for designing



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and selecting appropriate materials, however, they do not layout a holistic approach from project start to project finish. None of the frameworks investigated focused specifically on NGOs in developing countries. Neither do they include the full range of development activities from conception to operation and, therefore, did not include a mechanism for clearly defining the project objective. The construct for such a theoretical framework is based on the research described in this section and the integrative project delivery approach presented in the following section.

FRAMEWORK DEVELOPMENT AND ILLUSTRATION

Integrative Project Delivery is a collaborative project delivery method that involves the talents and insights of key project stakeholders during all phases of the design and construction process (American Institute of Architects 2013). A more thorough explanation of IPD is outlined in the Integrated Project Delivery: A Guide (2007) developed by the AIA. AIA has been a developer and advocate for IPD to more clearly define project outcomes specifically for achieving goals in sustainability. IPD also plays a major role within the Leadership in Energy and Environmental Design (LEED) Rating System developed by the U.S. Green Building Council (USGBC 2013). IPD plays an integral part in the LEED rating system because it brings all the key team members together during the early stages of the project, helping to clearly define sustainability goals, and determine the best means and methods for achieving them.

IPD will guide the corresponding framework steps in a manner that unites the NGO with the Local Owner/Partner (LOP) early in the design and building process. Using IPD for small-scale construction projects will direct the process to reduce some of the social and cultural misunderstandings that occur between the NGO and the LOP by forcing collaboration and goal setting during the initial phase of the project. The Integrated Project Delivery: A Guide (2007) defines IPD as a process that occurs in seven different phases including: 1. Conceptualization, 2. Criteria Design, 3. Detailed Design, 4. Implementation Documentation, 5. Agency Coordination and Buyout, 6. Construction Monitoring Project, and 7. Closeout (AIA IPD 2007). Figure 1 shows the seven phases of the IPD on the left side of chart with the corresponding steps of the proposed theoretical framework on the right side.



9A Framework for Small-Scale Construction Projects by NGOs in Developing Countries

Figure 1: Theoretical Framework


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The following sections describe the framework steps and actions necessary within each AIA phase. The decisions, sequences, and relationships between the framework actions are communicated by shapes and arrow directions. The rounded rectangular boxes indicate beginning inputs. The rectangular boxes communicate specific steps and or tasks within the process. A diamond shape indicates a decision while the arrows notate the path that must be followed from a steps, tasks and/ or decisions.

Phase 1: ConceptualizationConceptualization is the first phase in the IPD approach and determines, what is to be built, who will build it, and how to build it (AIA 2007). Figure 2 shows the first action step of the theoretical framework within the Conceptualization phase of the IPD. Here the NGO and the LOP provide input to determine and develop the project parameters. One part of the EWB mission is to create basic community infrastructure that will remain sustainable long after their involvement with the project.

EWB developed protocols to guide their Chapters during this stage of the project (EWB 2013). The New Project Application Form (#501) includes goals and objectives that are clearly articulated. The initial visit results in a Pre-Assessment Report (#521) that includes a Community Agreement/Contract. Instruments, such as these, are valuable to operationalize and develop project parameters and scope. Completing these documents requires input from both the NGO and LOP and helps to create a “meeting of the minds” that is essential before moving to the Criteria Design Phase.

Decisions at this stage can have the greatest impact on the overall project but cost the least because little is invested at this stage. A well-funded, NGO sponsored hospital in Mali, West Africa served women and children. It had two to three missionary doctors and a similar number of missionary nurses from the United States (US). Money was raised for an expansion of the facilities beyond that which was needed so the

excess funding was used to develop private delivery rooms, two additional operating rooms, and space for a future nursing school. The number of staff remained fairly constant in keeping with patient load. This very successful small hospital was tripled in size within a few years. At this point the LOPs were, at best, passive participant/observers but more likely victims of an “NGO Gone Wild” in an effort to keep up with a successful fundraising campaign.

Phase 2: Criteria DesignCriteria Design is the second phase in the IPD approach and begins to refine and shape the project by selecting, testing and evaluating the optimal building systems to be used in the project. Figure 3 shows the second and third steps of the theoretical framework within the Criteria Design phase of the IPD. The NGO and LOP develop objective and subjective KPI after the project parameters and scope are established.

“How do you define success,” is the question that must be answered by the NGO and LOP at this important step. It is important to define standards for project success at the beginning stages of the project so that they can be judged and evaluated throughout the project. The previously discussed EWB #501 calls for identification of metrics to assess the project outcomes.

Chan (2004) developed a core set of success indicators, illustrated in Table 1, that provide a reference list for the NGO and LOP to develop their own objective and subjective measures in determining the overall success of the construction project. The output from this step is a documented set of project goals and objectives clearly outlining the intentions of both parties.

Figure 2: Step OneFigure 3: Step Two and Three



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Table 1: Key Performance Indicators

All of the right project goals and objectives were developed to build an elementary school in the Central Plateau of Haiti for a community of subsistence farmers. The LOP had already built a school for over 200 children with a tree branch structure, tree bark walls and a palm frond roof before partnering with a construction NGO. A concrete post and beam construction, masonry infill, and a metal roof design was developed for a typical classrooms to allow for modular construction as funding became available. A couple of classroom were built and funds were being raised for a second phase for three more classrooms. During this time, another NGO that was providing one meal a day for each student in the school made a strategic decision to redirect their resources elsewhere.

The construction NGO was focused on the building and infrastructure, operating under the principle “Give a man a fish and he eats for a day. Teach a man to fish and he eats for a lifetime.” The food issue was clearly not in keeping with the original goals and objectives defined for the construction project. However, this meal was, for most of these children, their biggest meal of the day and for some the only meal. While the construction NGO was unwilling to deviate from the original goals they understood that a starving child makes a poor student. So they reorganized their efforts under a different but parallel project scope to identify partners to support a one meal a day program. Keeping a project focused is a common problem since the needs are often so overwhelming that goals like constructing a school are trumped by starvation, sickness, and death.

The goals and objectives established in Step 2: Objective and Subjective Key Performance Indicators direct the optimal technological, environmental, and

social building systems decisions as indicated by the arrow pointing directly downward to Step 3: Optimal Technological, Environmental, and Social Building Systems. The other arrow leads out to the right as inputs to Step 8: Rate Project Success. It is in Step 8, upon completion of the project, that these established criterion will be evaluate for overall project success.

The step subsequent to completing the KPI is selecting and evaluating the optimal building systems available in the area. This is an important consideration in many developing countries due to the difficulty getting materials in many locations. For example, it is quite common to ship containers filled with construction equipment, supplies, and materials to Mali but the same container might sit in Port au Prince, Haiti indefinitely and eventually cost more than the value of the contents to get released. NGOs and designers must also take into consideration how different building systems will impact the social economic and environmental aspects of the project.

Wekesa and Steyn (2010) outline a detailed methodology for determining which building items to select within the categories of technical, environmental, and social economic. Technical objectives focus on the quality and durability of the structure. Environmental objectives are the efficient utilization of materials and/or their effect on the surroundings. The social economic objectives center on finding ways to stimulate the local economy, involve members of the community, develop skills and capacity, and ensure a healthy and safe environment. Referencing methods used by Wekesa and Steyn (2010) the various building elements common to the area are identified in the conceptualization stage and then listed as conventional, traditional, or innovative. A Simple Additive Weighting (SAW) is then used to rate each system numerically based on the ratings given by the local industry professionals in the area.

The walls and the foundation of both phases of the school in Haiti were consistent with technical, environmental, and socio-economic objectives to different degrees. The first phase used concrete masonry units (CMU) purchased from a vendor 15 miles away. The CMUs were of such poor quality that many fell apart in transit. The second phase used CMUs fabricated on site. This stimulated the local economy, developed skills, and increased capacity. The steel trusses and metal roof on the first phase was delivered, fabricated, and installed



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by a mission group while the LOP watched from the sidelines. The LOP was quite insistent on a concrete slab roof for the second phase noting their inability to replicate the efforts on the first. The NGO was equally adamant against such a design for safety reasons. The compromise included a wood joist design that would bear on bond beams to be fabricated on site and covered with metal decking. Carpenters, masons, and local farmers completed the second phase roof under the guidance of a representative of the NGO.

Phase 3: Detailed DesignDetailed Design is the third phase in the IPD approach and finalizes the design decisions of the project (AIA 2007). Figure 4 shows the fourth step in the theoretical framework, Design Building Model, followed by a decision. This corresponds to the Detailed Design phase of the IPD. Building models are typically two-dimensional drawings or three dimensional models. Two-dimensional models may be more difficult to understand for workers in developing countries but can be easily delivered on paper. Three-dimensional models are more intuitive to understand but must be viewed on a computer. This can be particularly difficult in remote locations and it is unlikely that a computer would be available unless the NGO provides it, however, mobile technologies such as IPads make the use of these models less difficult. Three-dimensional renderings can provide an intuitive option that can be delivered on paper but care must be taken to provide adequate dimensioning and construction details.

A decision branch occurs after completing the building model. If both parties approve the design, the project proceeds to Step 5: Assign Construction Resources. If

either party does not approve of the building design model, then the process goes back to Step 2 where either the project goals and objectives are modified or the optimal, technological and social building components are adjusted until consensus can be reached on the building model by both parties. The project is ready for the implementation documents once both parties approve the building model’s design.

Phase 4: Implementation Documents

Implementation Documents is the fourth phase in the IPD approach and considers procurement of the resources needed to complete the project (AIA 2007). Figure 5 shows the fifth step of the theoretical framework, Assign Construction Resources, followed by two sequential decision points within the Implementation Documents phase of the IPD.

Construction resources are defined as the materials, equipment and labor necessary to build in accordance with the design created in Steps 1-3. A key focus in Wekesa’s model in Step 3: Selecting and Evaluating the Optimal Building Systems is to use locally available resources whenever possible so this stage is concerned with the procurement of those resources and filling in the gaps in delivery, equipment and human resources including management through other arrangements. Using locally available resources can be challenging because of any number of constraints like lack of infrastructure, unskilled labor, remote project location and limited access to equipment.


Figure 4: Design Building Model and Approvals

Figure 5: Resource Assignments


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An orphanage located in Hinche, the third largest city in Haiti with a population of 50,000, needed a solar pump/filter installed to provide clean drinking water. The LOP indicated that they had the system and the tools and they just needed someone to install it. The NGO got the system working on the first day and the only thing remaining was to attach the solar panel to the concrete roof. The brackets, screws and cement anchors were there but there was no drill. There was not a store in the entire area that had a drill for sale. A local blacksmith was apparently the only one around with a drill so he was hired to drill the holes. He arrived with his drill but only had wood bits. This simple situation exemplifies the complexities that one faces and the need to plan the smallest details. The NGO left after a week without attaching the solar panel to the roof.

The decision point following this step is whether the resources are locally procurable. If yes, then proceeds to the next step, complies with code. If no, then proceeds to a second decision point to determine if the NGO plans to provide the necessary resource. If the NGO provides the resource, then proceed to the next step, complies with code. If not, return to Step 3, Select Optimal Technological, Environmental and Social Building. In cases where resources are unavailable, the NGO may elect to provide those resources or the design may need to be sent back to the criteria design phase and altered.

Phase 5: Agency Coordination and Buyout

Agency Coordination and Buyout is the fifth phase in the IPD approach and considers codes and regulations and finalized pricing (AIA 2007). Figure 6 shows there are no steps, but rather a decision point for codes and regulations in the theoretical framework corresponding to the Agency Coordination and Buyout phase of the IPD

Most project designs will already exceed local codes or performance standards because of low or non-existent standards. Projects built in developing countries with established codes can refer to a methodology developed by Becker (2002) to create performance standards for improved design.

This stage is critical and acts as the gate between design and construction. It may be the last opportunity for an NGO to influence construction methods and techniques. Therefore, code compliance at this point in the project is defined in terms of both safe construction methods and safe designs. This may be where the greatest separation exists between the law and the moral and ethical standards of the NGO and the LOP. If the design is not considered up to code or acceptable safety standards then the project must return to the detailed design phase as the arrow illustrates in the framework and the building design altered. If the design is considered up to code then construction can proceed.

Phase 6: Construction Monitoring ProjectConstruction Monitoring Project is the sixth phase of the IPD and deals with the onsite construction management of the project. Figure 7 shows the sixth step of the theoretical framework that corresponds to the Construction Monitoring Project phase of the IPD. The primary function of the NGO and LOP during the building phase is construction management including cost, schedule and quality. The advantages of putting more effort into Steps 1-5 become most apparent in this step. Changes at this stage are the most costly and intrusive and should be minimized if enough effort was expended in the previous steps, and in particularly Step 4, Design Building Model.


Figure 6: Code Compliance

Figure 7: Construction


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When possible, local representation should oversee the project instead of the NGO taking the lead. This can be best illustrated with two simple composting latrine projects with extremely different results. The first latrine was in Togo, West Africa where the NGO was asked to provide a design and some construction oversight through a Peace Corps volunteer on behalf of the village. The market area had no facilities and thousands of people who visited on market day used the grass or whatever was available to relieve themselves. The LOP arranged a very reasonable budget because this was a village project, with locals purchasing the materials. The NGO arrived on the first day with the design and there were 20 workers ready to get going. Within two days the chamber was complete and they finished the walls and roof within the week.

This is contrasted with a similar request in a remote village in Haiti near the Dominican Republic. An NGO working in that village had a similar request for a composting latrine design and requested an estimate from the NGO that did the latrine in Togo. They agreed to spend two days showing the LOP how to build the composting latrine. When the NGO arrived two local masons were available and supplies were being purchased by the onsite NGO. The chamber was built in two days leaving only the walls and the roof for the locals. The onsite NGO complained that the project went well over budget and the two masons wanted to be paid to finish the walls and roof. This is an expected outcome of a project driven and funded by an NGO.

Step 5 works in conjunction with Step 6, by directing the NGO and LOP to heavily rely on local labor, expertise, experience and utilize local materials and resources whenever possible. It is more likely that the skilled labor, equipment and materials will be available when building practices common to the area are selected in the previous steps. According to research conducted by Corbett (2009), NGOs that take the lead can create a paternalistic effect that causes the locals to rely too heavily on the NGO to complete the project for them, when in many cases the local people could do the work themselves. Western-based building practices can also create a reliance on the NGO for materials, instruction, and management.

A fine line is walked when introducing technologies and/or materials that are unfamiliar to the LOP workforce. The school in Haiti mentioned in Phase

2: Criteria Design section of this document included an innovative roof design using wood joists fabricated onsite and installed by the LOP under the direction of an NGO representative. The joist were installed perpendicular to the metal decking and the intermediate walls and attached to the bond beam on the top of the wall. The method of construction is simple and easily replicable. Raw materials and labor were locally available and the design provided an alternative to the more difficult and more expensive steel structure without the concerns associated with a concrete roof.

Phase 7: Closeout, Training, Maintenance and Project SuccessCloseout, Training, Maintenance and Project Success is the seventh phase in the IPD approach and addresses the operations and training needed to run the completed building (AIA 2007). Figure 8 shows Step 7: Operations, followed by Step 8: Rate Project Success of the theoretical framework, within the Closeout, Training, Maintenance and Project Success phase of the IPD.

CONCLUSION AND RECOMMENDATIONS

The authors found small scale construction project failures abound in developing countries not due to negligent or nefarious intent but rather due to systemic inefficiencies by well-intentioned organization. In addition, they found that managing the life-cycle of a construction project from concept through operational sustainability in a developing country can be an incredibly difficult task. Further, NGOs struggle with optimizing their investments of time, money, and other resources. It is important to the overall success of the project that the stakeholders overcome the systemic barriers to deliver a sustainable project optimized through careful consideration at each step. This paper proposes an effective model that integrates the efforts of the NGO and the LOP in a step-by-step framework

Figure 8: Operations and Evaluation



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based on past research and experience. The authors utilized the guiding principles of the IPD to develop a sustainable framework with eight action steps and four decision points using input from both the NGO and LOP. These create a systematic process that NGOs can practically follow when building small-scale construction projects in developing countries. This framework helps develop a vested interest in the final outcome of the project for the LOPs and guides the NGO toward sustainable solutions that will continue far beyond their involvement. It involves both the local owner/partner (LOP) and NGO early on in the design decisions and requires them to develop a clear set of project performance indicators at the beginning stages of the project. It is recommended that organizations pursuing the activities described in this document focus on these two most important steps. They are key in determining the success of the project including operational sustainability managed by the LOP long after NGOs withdraw their support. More research is needed in developing countries to aid NGOs in their endeavors, and care must be taken to ensure that their efforts do not end up hurting the people they intend to help but rather result in an operationally sustainable outcome.

REFERENCES

Ahadzie, K., Proverbs, G., & Olomolaiye, O. (2008). Critical success criteria for mass house building projects in developing countries. International Journal of Project Management, 26(6): 675-87.

AIA, (2007). Integrated project delivery: A Guide. AIA California Council.

American Institute of Architects. Integrated Practice/Integrated Project Delivery. Retrieved April 28, 2013 from http://www.aia.org/about/initiatives/AIAS076981?dvid=&recspec=AIAS076981.

Ali, H., & Al Nsairat, S. F. (2009). Developing a green building assessment tool for developing countries – case of Jordan. Journal of Building and Environment, 44(5): 1053-1064.

Aspire Sustainability Tool. ASPIRE Research and Development. Retrieved April 4, 2013 from http://www.engineersagainstpoverty.org/publications.cfm.

Becker, R. (2002). Implementation of the performance approach in the investigation of innovative building systems. Journal of Building and Environment, 37(10): 923-931.

Chan, A.P. (2004). Key performance indicators for measuring construction success, Benchmarking: An International Journal, 11(2): 203-21.

Chileshe, N., & Yirenkyi-Fianko, A. B. (2011). Perceptions of threat risk frequency and impact on construction projects in Ghana: Opinion survey findings. Journal of Construction in Developing Countries, 16(2):115-149.

Corbett, S., & Fikkert, B. (2009). When Helping Hurts. Chicago 101: Moody Publishers.

Du Plessis, C. (2002). Agenda 21 for sustainable construction in developing countries. CSIR Building and Construction Technology. Boutek Report No Bou/E0204.

Easterly, W. (2006). The White Man’s Burden: Why the West’s Efforts to Aid the Rest Have Done So Much Ill and So Little Good. Penguin Publishers.

Engineers Without Borders. (2012). Technical Resources. Retrieved September 10, 2013 from http://www.ewb-usa.org/what-we-do/resources.

Hastak, M. & Shaked, A. (2000) ICRAM-1: model for international construction risk assessment. Journal of Management in Engineering, 16(1): 59-67.

Hill, R. C. and Bowen, P. A. (1997). Sustainable construction: Principles and a framework for attainment. Construction Management & Economics, 15(3): 223-239.

Lim, C.S. & Mohammed, Z. (1999). Criteria of project success: An exploratory re-examination. International Journal Project Management, 17:243-8.

Long, N.D., Ogunlana, S., Quang, T. & Lam, K.C. (2004). Large construction projects in developing countries: a case study Vietnam. International Journal of Project Management, Vol. 22: 553-561.

Mbachu, J. & Nkado, R. (2007). Factors constraining successful building project implementation in South Africa. Journal of Construction Management Economics, 25:39-54.



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Odusami, KT. (2003). Criteria for measuring project performance by construction professionals in the Nigeria construction industry. Journal of Financial Management Property Construct, 8(1):10.

Ofori, G. (2006). Revaluing construction in developing countries: A research agenda. Journal of Construction in Developing Countries, 11(1).

Ofori, G. (2007). Construction in developing countries. Construction Management and Economics, 25(1).

Ozolins, P.C. (2010). Assessing Sustainability in Developing Country Contexts: The Applicability of Green Building Rating Systems to Building Design and Construction in Madagascar and Tanzania. Blacksburg, Virginia: Virginia Polytechnic Institue and State University.

Shen, Q. & Liu, G. (2003). Critical success factors for value management studies in construction. Journal of Construction Engineering Management, 129(5):485-91.

Striebig, B., Ogundipe, A., Amini, A., Anderson, D., Haling, L., Morrison, B. & Wolfe, D. (2012). An interactive sustainable infrastructure design model for health clinics in sub-saharan Africa. In Global humanitarian technology conference (GHTC), 247-252.

United Nations (2002). Report of the World Summit on Sustainable Development, Johannesburg, South Africa, 26 August- 4 September 2002. United Nations, New York, 2002.

U.S. Green Building Council (2013). Leadership in Energy and Environmental Design. Retrieved August 5, 2013 from http://www.usgbc.org/leed.

Wang, S.Q., Dulaimi, M.F., & Aguria, M.Y. (2004). Risk Management Framework for Construction Projects in Developing Countries. Journal of Construction Management and Economics, 22 N3: 237-25.

Wekesa, W., Steyn, S. and Otieno, A.O. (2010). The response of common building construction technologies to the urban poor and their environment, Journal of Building and Environment, 4510: 2327-35.

World Bank (1985). The Construction industry: issues and strategies in developing countries. Report No. 13531. Washington, DC: World Bank.

Yaziji, M. (2004). Turning gadflies into allies. Harvard Business Review, 82(2): 110e115.



17

Influence of Climatic Parameters on the Performance of Solar Photovoltaic Modules in a Subtropical Area

Aiyin JiangUniversity of North Florida | [email protected]

Elena BuzaianuUniversity of North Florida | [email protected]

Mag MalekUniversity of North Florida | [email protected]

Keywords: Performance of Solar Photovoltaic System, Climatic Parameters, Multiple Linear Regression

INTRODUCTION

Solar photovoltaic (PV) power generation is becoming widespread as a clean energy source. Solar power systems that are connected to the electricity grid, known as grid connected solar PV systems, generate electricity for users and feed excess energy back into the electric grid. The installation of residential and commercial grid connected solar PV systems has been increasing for decades as users seek to lower their utility bills,

reduce their dependence on the retail utility grid, and demonstrate a commitment to the environment. The average size of a grid-connected solar PV residential installation has grown steadily but comparatively slowly in the US. One of the short-term barriers to deployment of residential and commercial PV systems includes the high upfront cost of installation. Another concern for potential PV users is the performance or power generation efficiency of solar PV. Though the performance of solar cells is usually evaluated under standard test conditions (1000W/m2 irradiation, 25°C module temperature, and AM1.5 global spectrum), the actual output of a system cannot be estimated

ABSTRACT: The average size of a grid-connected solar photovoltaic (PV) residential installation has grown steadily but slowly in the US as compared to Europe. One concern of potential PV users is the power generation efficiency of solar PV. Though the performance of solar cells is usually evaluated under standard test conditions, operation in various environments is required for PV systems, and various climatic factors such as solar irradiation, temperature, and humidity affect the generating performance of the systems. Although many studies have been conducted regarding the impact of climatic factors on the efficiency of solar PV output, most of them focus only on one or two climatic parameters. This study analyzes the climatic impacts on energy performance of a solar PV system in a more comprehensive way by applying multiple linear regression analysis. The climatic parameters studied include ambient temperature, relative humidity, heat index, dew point, wind chill, wind velocity, solar radiation, THW (temperature/humidity/wind) index and THSW (temperature/humidity/solar radiation/wind) index. The study was done in a subtropical climate area, northeast Florida. The resulting energy output models will provide a good reference for local utilities and potential solar PV users who are considering the economic feasibility and performance of solar PV.

Aiyin Jiang is an Associate Professor in Department of Construction Management at University of North Florida. Her research interests include renewable energy application in infrastructure, sustainable construction materials, building energy simulation, and other project planning and management areas

Elena Buzaianu is an Associate Professor in Statistics in the Department of Mathematics and Statistics at the University of North Florida. Her research interests are in the areas of ranking and selection, multiple comparisons and sequential analysis.

Mag Malek, Professor and Chair of the Construction Management Department at the University of North Florida. He has been teaching in academia for 16 years at UCF, Mercer University (College of Engineering) and at UNF). His education is supplemented with a rich 15 years of working experience in the industry. His past experience exposed him to all facets of planning and administration of construction projects.



18 Influence of Climatic Parameters on the Performance of Solar Photovoltaic Modules in a Subtropical Area

solely from its performance under standard test conditions (STC). Information about operation under various environmental conditions is required for more widespread installation of PV systems. Field environments differ with respect to climatic factors, such as solar irradiation, temperature, and humidity, which significantly affect the generating performance of the systems.

LITERATURE REVIEW

Climatic factors vary in outdoor operating conditions and in combination they are expected to affect every module’s behavior. Nishioka et al. (2003) analyzed field-test data from a 50 kW solar PV system and found that a PV system operating in a wide temperature range was strongly affected by the temperature coefficient of conversion efficiency when the module temperature was high. Topic et al. (2007) concluded that the effective efficiency of solar PV depends on average annual daylight temperature, but only very weakly on daylight ambient temperature distribution. Omubo-Pepple et al. (2009) confirmed that the ambient temperature has no direct effect on the solar panel working temperature but solar flux has a substantial effect. Similar studies of the effect of temperature on efficiency of solar PV can be found in other studies (Zdanowicz et al., 2003; Katkar et al., 2011).

Other research focuses on the effect of solar radiation on the performance of solar PV system. Choosakul et al. (2011) studied the effect of intensity of the incident solar radiation on the amount of generated electric current from solar PV. Their results showed that the generated current was higher with less fluctuation on a sunny day compared to the current generated on cloudy and rainy days. Guechi et al. (2012) simulated global, direct and diffuse solar radiation incident on solar nanocrystalline silicon cells using the spectral model SMARTS2 for varying environmental conditions in Setif, Algeria. The results show that the short circuit current and the efficiency increased with increasing turbidity for diffuse solar irradiance and decreased for global and direct irradiance. Chegaar and Mialhe (2008) conducted similar research in Algiers, Algeria. Topic (2007) further concluded that distribution of annual solar energy is a function of irradiance level

and that the conversion efficiency of photovoltaic modules varies with irradiance and temperature in a predictable fashion.

Ettah et al. (2012) investigated the effect of relative humidity on performance of solar panels in Calabar, Nigeria. Results showed that low relative humidity between 69% and 75% resulted in an increase in output current from solar panels. Voltage output also increased with decreasing relative humidity but stabilized between relative humidity values of 70% and 75%. These results imply that low relative humidity has a positive effect on the efficiency of solar panels. Katkar et al. (2011) also pointed out that the efficiency of silicon solar cells varies with different temperature and humidity levels. Efficiency of solar cells increases from 9.702% at 31°C (87.8°F) with humidity at 60% to 12.04% at 36°C (96.8°F) with humidity at 48%, then it decreases to 2.37% at 58°C (136.4°F) with humidity at 29%. Omubo-Pepple et al. (2013) investigated the impact of relative humidity and solar flux on the efficiency of photovoltaic modules in the Niger Delta region of Nigeria. Results showed that solar panel efficiency is directly proportional to solar flux. Low relative humidity led to increased solar flux and resulted in increased output current, thus enhancing the efficiency of the solar panel.

Omubo-Pepple et al. (2009) studied the effect of three meteorological factors—temperature, solar flux, and relative humidity—on efficient conversion of solar energy to electricity using a solar PV module in a tropical climate region. They confirmed that with a working temperature of 43°C (109.4°F), low relative humidity of 70% to 76%, and solar flux of about 78.85 kiloflux, it is possible to obtain efficiency of up to 82.28% from the solar panel.

Research on effects of other climatic parameters, such as wind velocity, air mass, and water vapor on the efficiency of solar PV has also been conducted. For example, Siddiqui and Bajpai (2012) developed a formula regarding the impact of climatic variables—wind velocity and temperature—on the performance of solar PV modules installed in Lucknow, India. Chegaar (2008) concluded that increasing water vapor in the atmosphere leads to a reduction in the short circuit current of 4.57%, 4.4%, and 0.2% respectively for


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mono-crystalline, multi-crystalline, and amorphous silicon under global radiation. The short circuit current decreased with increasing air mass for the different types of silicon solar cells. Efficiency increased with increasing air mass for mono-crystalline and multi-crystalline solar cells but decreased for amorphous silicon solar cells. Guechi et al. (2012) confirmed in their simulation that the short circuit current decreases with increasing air mass under global, direct and diffuse solar irradiance. Touati et al. (2013) showed that dust accumulation decreases efficiency of amorphous and mono-crystalline PVs to a greater extent than do increased temperature and relative humidity. Table 1 lists the research conducted by the above mentioned authors.

Although research on the impact of climatic parameters on solar PV output efficiency has been conducted for years, it is important to investigate a PV system operating in an actual environment and to explore more comprehensively the dependence of solar module output on various climatic parameters in combination. This realistic approach considers the effect of all climatic factors simultaneously on PV output. The goal of the study is to predict PV output performance in a realistic and natural situation. The objective of this study is to determine how output of solar photovoltaic modules varies with ambient

temperature, relative humidity, dew point, wind chill, wind velocity, solar radiation and other factors in a subtropical climate area, northeast Florida.

THE SOLAR PHOTOVOLTAIC DEMONSTRATION SYSTEM AND WEATHER STATION

Analysis of the effects of climatic factors is complex. Its realization requires long term measurements with use of outdoor-installed data acquisition systems, producing large amounts of data which must then be carefully analyzed. Figure 1 shows the installed solar photovoltaic (PV) system and weather station. The weather station is a Vantage Pro which collects and downloads weather data to a host computer through Davis Instruments’ Weather Envoy and WeatherLink software. The solar PV demonstration system is installed in Jacksonville, Florida, USA (latitude 30.50° N,longitude 81.70° W). The system is south-facing (azimuth angle 180°) and the tilt is 30°. Two solar PV panels are installed. The system has the following components:

• PV arrays, which convert light energy to direct current (DC) electricity;

• Inverters, which convert DC to alternating current (AC) and provide monitoring and control functions;


Table 1: List of Research on Effects of Climatic Parameters on the Efficiency of Solar Photovoltaic Systems (in Chronological Order)


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• Balance of the system, such as wiring and mounting hardware;

• Monitoring equipment.

The solar PV system is integrated into an existing building’s electrical system. The system includes two Webel® photovoltaic modules --W1750. Each of them is rated at 180 peak DC watts. Peak watts are the rated output of the PV modules at standard operating conditions of 25°C (77°F) and insolation of 1,000 Watts/m2. Table 2 shows the electrical specifications of the W1750 module under standard test conditions in terms of current, voltage, and power. The solar PV system is integrated into an existing building’s electrical system. The system includes two Webel® photovoltaic modules --W1750. Each of them is rated at 180 peak DC watts. Peak watts are the rated output of the PV modules at standard operating conditions of 25°C (77°F) and insolation of 1,000 Watts/m2. Table 2 shows the electrical specifications of the W1750 module under standard test conditions in terms of current, voltage, and power.

Inverters convert generated power from PV modules to alternating current at the required voltage and number of phases. They enable the operation of commonly used equipment such as appliances, computers, and office equipment, and provide operational and safety functions for interconnection with the utility system. Inverters also include the control systems required for operation, including some metering and data-logging capability. The solar PV demonstration system includes two Enphase® M190 micro-inverters (208 volt, 3 phase). The three key elements of an Enphase Microinverter System are:

• the Enphase Microinverter;• the Enphase Envoy™ Communications Gateway;• the Enphase Enlighten™ web-based monitoring

and analysis system.

The Enphase Microinverter maximizes energy production from PV array. Each Enphase Microinverter is individually connected to one PV module in the PV array. This insures that the maximum power available from each PV module is exported to the utility grid regardless of the performance of the other PV modules in the array. The result is maximum energy production from the PV system.

The Envoy Communications Gateway provides an Ethernet connection to a broadband router or modem by plugging into any120Vac wall socket. After installation of the Envoy, the full network of Enphase Microinverters automatically begins reporting to the Enphase Enlighten web server. The Envoy is an integral component of the Enphase Energy Microinverter system. It operates between the Enphase Microinverters and the Enphase Enlighten web-based monitoring system. The Envoy functions as a gateway and collects energy data from the microinverters


Figure 1: Solar Photovoltaic Demonstration System

Table 2: Electrical Specification of Solar Photovoltaic Module W1750


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over AC power lines. It then forwards that data to the Enphase Enlighten web-based monitoring system via the Internet. The Enlighten software presents current and historical system performance trends, and it informs system users when the PV system is not performing as expected. Figure 2 shows snapshots of the system power output monitoring.

METHODOLOGYAlthough we have recorded solar PV output and climatic parameters such as temperature, relative humidity, and irradiance since 2011, unpredictable events have occurred because the system is out in the field. Some energy output data in 2011 and 2012 are missing for technical reasons. For example, one of the micro-invertors did not record data in July 2011. We also lost weather data for a short period of time in November 2012 because of miscommunication

between the weather station and the host computer. The 2013 data are the most comprehensive and highest quality since the solar PV system was installed. Table 3 shows the energy data set from August 2013.

Estimating Equipment Productivity and Costs

The climatic parameters in this study include temperature (°F), relative humidity (%), dew point (°F), wind speed (miles/hour), wind chill (°F), heat index (°F), THW (temperature/humidity/wind) index (°F), THSW (temperature/humidity/solar radiation/wind index) index (°F), barometric pressure (inches of mercury), rain (inches), and solar radiation (W/m2 = 0.3171Btu/h.ft2). Relative humidity indicates the likelihood of precipitation, dew, or fog. The effect of wind chill is to increase the rate of heat loss and reduce any warmer objects to the ambient temperature more quickly. The heat index combines air temperature and relative humidity in an attempt to determine the human-perceived equivalent temperature. The THW takes into account the cooling and heating effects of wind and the effect of humidity on our perception of temperature. The THSW index uses temperature, humidity, the heating effects of sunshine, and the cooling effects of wind to calculate an apparent temperature based on our perception of temperature. The dew point is a water-to-air saturation temperature. A high dew point indicates a higher chance of rain, severe thunderstorms, and tornados. Barometric pressure is also known as atmospheric pressure. A rise in pressure usually means improving weather while falling pressure may reflect impending inclement weather. Solar radiation is technically known as global solar radiation, a measure of the intensity of the sun’s radiation reaching a horizontal surface. This irradiance includes both the direct component from the sun and the reflected component from the rest of the sky. The solar radiation reading gives a measure of the amount of solar radiation hitting the solar PV panel sensor at any given time, expressed in W/m2 (0.3171 Btu/h.ft2).

Statistical analyses were performed using a statistical software package (SAS version 9.2; SAS Institute, Cary, NC). Correlation and regression analyses were conducted to determine the relationship between the solar PV energy output and the various climatic parameters. We performed multiple regression of


Figure 2: Snapshots of Solar Power Generation Monitoring System


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significant variables identified by univariate analyses to determine those that retained their significance in the simultaneous context of other variables. We used stepwise elimination with a significance level of 0.10 for entry into the model and a significance level of 0.15 for elimination from the model.

Table 3: Energy Output of Solar PV and Weather Data from August 2013



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RESULTS AND DISCUSSION

Models for monthly data

First, regression analysis was conducted by month. In the univariate analysis for each of the twelve months, solar energy was a significant predictor of solar PV

energy output. When multiple regression analyses were considered, for all months except August, solar radiation remained a significant predictor, even in the presence of other predictors. In fact, for all of the twelve months except March, August, November and December, solar radiation remained the only significant predictor in the multiple regression.

Table 4: Summary of the Multiple Regression Analyses Performed by Month



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Table 4 includes a summary of the multiple regression analyses performed by month. Significant predictors identified are those that remain after having performed stepwise multiple regression. The estimated regression coefficients of these predictors along with their standard errors and p-values are also included. For each final multiple regression model, we included the corresponding coefficient of variation (R-square), the value of the F test and the corresponding p-value.

For the months of March, November, and December, multivariate analyses suggested that other predictors of solar PV energy output were significant in addition to solar radiation . For example, for the month of March, the estimated regression line is:E = 154.415 + 3.877 r – 3.541 h (1)where

E=solar PV energy output in March;r=solar radiation;h= humidity;

The two-predictor model was able to account for 96.2% of the variability in solar PV energy (F (2, 30) = 6.06, p = .02, R2 = .962).

For the month of November, the model includes temperature as a predictor along with solar radiation, while for the month of December, the model includes humidity and barometric pressure in addition to solar radiation.

Interestingly, while solar radiation was a significant predictor of solar PV energy output during the month of August (that is, significant if it were the only predictor in the model), it was not significant in the multiple regression analysis when the other predictors, such as humidity and barometric pressure, were present. However, solar PVs definitely rely on solar radiation to generate electric power. So, for the month of August, we have decided to consider a model that includes solar radiation, humidity and barometric pressure as predictors.

Overall, Table 4 suggests that solar radiation is positively correlated to the response variable (solar PV energy output). However the strength of the linear relationship seems to vary from season to season, weaker linear relationships being evident for the summer months.

Model for year-round data

We also analyzed the year round data to find out the relationship between energy output and climatic parameters (see Table 5). By analyzing 2013 year-round data, we obtained a more complex model that includes more climatic parameters, such as temperature, THWIndex, and THSWIndex. The squared multiple correlation R² of the model is 0.8369, and the adjusted R² is 0.8333. The energy output has the following linear relationship with the climatic parameters:Ey = 3810.4129 + 0.70684 r – 1.98595 h – 104.55343 b + 0.818 w + 17.06449 t – 139.93047 i1 + 117.62583 i2 (2)Where

Ey: daily solar PV energy output using the yearly data ;r: solar radiation;h: humidity;b: barometric pressure;w: wind speed;t: temperature;i1: THW index;i2: THSW index;

The year-round model indicates that solar radiation, wind speed, temperature, and THSW have positive impact on energy output while humidity, barometric pressure, and THW have negative impact on energy output.

Table 5: Regression Analysis for Year-round Data



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CONCLUSION

It is not accurate to estimate the performance of a solar PV system in standard test conditions (STC). It is seriously influenced by the operating situation, especially climatic parameters. Although many studies have been conducted regarding the impact of climatic and meteorological factors on the efficiency of solar PV output, most focused only on one or two climatic parameters. This study analyzes the climatic impacts on energy performance of a solar PV system in a subtropical area—northeast Florida—in a more comprehensive way. The climatic parameters that were considered in this study include ambient temperature, relative humidity, heat index, dew point, wind chill, wind velocity, solar radiation, THW index and THSW index. After several years of data collection, the data from 2013 were selected for completeness and quality. Multiple linear regression analyses were used to analyze the data. The results show that solar energy output is a function of solar radiation, humidity, barometric pressure, and wind velocity. Monthly solar energy output varies with these climatic parameters in a predictable fashion. Apparently, solar radiation has the largest impact on the solar energy output. Relative humidity plays a secondary role in affecting energy output in subtropical northeast Florida, in which high humidity is common. Barometric pressure and wind velocity also have significant effects on energy output. Although wind does not usually directly affect the solar PV module, it is relevant to other climatic parameters such as humidity and temperature. With respect to wind velocity, it is important to realize that hurricanes are a serious concern in a subtropical area. Recently the high cost of energy, dropping cost of PV, and excellent solar resource in Florida have prompted utility companies to consider meeting energy demands in part by utilizing renewable energy. Energy output models are good references for local utilities considering economic feasibility and performance of solar photovoltaics. In further such studies, the effects of air mass, dust, cloud cover, and degradation of solar PV systems should also be taken into account in developing the predictive models.

REFERENCES

Chegaar, M. & Mialhe, P. (2008). Effect of atmospheric parameters on the silicon solar cells performance, Journal of Electron Devices, 6, 173-176.

Choosakul, N., Buddhakala, M., Barnthip, N., Muakngam, A., and Banglieng, C. (2011). Application of solar cells for daytime weather study, 9th Eco-Energy and Materials Science and Engineering Symposium, 9, 171-177.

Ettah, E. B., Udoimuk, A. B., Obiefuna, J. N., and Opara, F. E. (2012). The effect of relative humidity on the efficiency of solar panels in Calabar, Nigeria, Universal Journal of Management and Social Science, 2 (3), 8-11.

Guechi, A., Chegaar, M., and Aillerie, M. (2012). Environmental effects on the performance of nanocrystalline silicon solar cells, Energy Procedia, 18, 1611-1623.

Katkar, A. A., Shinde, N.N., and Patil, P.S. (2011). Performance and evaluation of industrial solar cell w.r.t. temperature and humidity, International Journal of Research in Mechanical Engineering and Technology, 1 (1), 2249-5762.

Nishioka, K., Hatayama, Uraoka, T., Fuyuki, Y. T., Hagihara, R., and Watanabe, M. (2003). Field-test analysis of PV system output characteristics focusing on module temperature, Journal of Solar Energy Materials & Solar Cells, 75, 665-671.

Omubo-Pepple, V. B. & Israel-Cookery, C. (2009). Effects of temperature, solar flux and relative humidity on the efficient conversion of solar energy to electricity, European Journal of Scientific Research, 35 (2), 173-180.

Omubo-Pepple, Tamunobereton-Ari, V. B. I., and Briggs-Kamara, M. A. (2013). Influence of meteorological parameters on the efficiency of photovoltaic module in some cities in the Niger Delta of Nigeria, Journal of Asian Scientific Research, 3 (1), 107-113.

Siddiqui, R. & Bajpai, U. (2012). Deviation in the performance of solar module under climatic parameter as ambient temperature and wind velocity in composite climate, International Journal of Renewable Energy Research, 2 (3), 486-490.



26 Influence of Climatic Parameters on the Performance of Solar Photovoltaic Modules in a Subtropical Area

Topic, M., Brecl, K., and Sites, J. (2007). Effective efficiency of PV modules under field conditions, Progress in Photovoltaics: Research and Applications, 15, 19-26.

Touati, F., Massoud, A., Hamad, J. A., and Saeed, S. A. (2013). Effects of environmental and climatic conditions on PV efficiency in Qatar, International Conference on Renewable Energies and Power Quality, Bilbao, Spain, March 20 to 22, 11

Zdanowicz, T., Rodziewicz, T., and Waclawek, M. Z. (2003). Effect of air mass factor on the performance of different types of PV modules, 3rd World Conference Photovoltaic Energy Conversion, May 11-18, Osaka, Japan


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Keywords: Project Delivery Method, Design-Bid-Build, CM at-Risk, Procurement

INTRODUCTION

Project delivery methods are comprehensive management systems utilized to assign contractual responsibilities to those involved with a project’s design and construction (Kenig 2011). Those in decision-making capacities are charged with selecting the project delivery method that is most appropriate for dealing with the issues related to their specific construction project scenarios.

Since 1995, more than $310 billion has been spent on capital projects for education with more than half ($174 billion) being spent on new school facilities (Abramson 2013). The Abramson report also notes that the median cost per square foot to construct new schools has doubled during this same time period. Meanwhile, federal, state, and municipal budget shortfalls, slow projected economic growth, and reduced tax revenues experienced during the recent economic downturn continue to place pressure on capital expenditures for public schools, while growing populations and changing demographics increase demands on aging and outdated facilities (Abramson 2012; Oliff, Mai, & Palacios 2012; McNichol, Oliff, & Johnson 2011; US

Project Delivery Methods for the Construction of Public Schools in the Southeastern United States

Noel Carpenter, PhDClemson University | [email protected]

Dennis C. Bausman, PhD, FAIC, CPC Clemson University | [email protected]

ABSTRACT: Construction of new facilities is a risky enterprise due to the situational aspects of the project, multiple parties involved in the process, timing and budget constraints, and the design features and quality levels required by the owner. Decision makers are charged with selecting a project delivery system to successfully manage the design and construction process in order to meet the owner’s needs. This paper documents the delivery methods utilized for the construction of public schools in Florida, Georgia, North Carolina, and South Carolina from 2006 to 2012 and provides insight into the reasons for the delivery method selections made by those in decision-making capacities. The study revealed utilization differences between states, with the predominant methods being Design-Bid-Build and Construction Manager at-Risk. The data collection was completed in conjunction with a two-year study of the performance of project delivery methods for public schools completed in 2014.

Noel Carpenter, PhD, taught undergraduate courses at Clemson University and currently works in the wireless telecommunications industry. With more than 20 years of experience in commercial construction management, he has served in various positions within the construction industry including Operations, Quality Management, and Business Development..

Dennis C. Bausman, PhD, FAIC, CPC is a Professor and Endowed Faculty Chair in the Construction Science and Management Department at Clemson University. He has 22 years of industry experience in project management and senior leadership positions in the commercial construction industry.



28 Project Delivery Methods for the Construction of Public Schools in the Southeastern United States

Census Bureau 2011). The construction industry as a whole is fraught with risk and complexity (Saporita 2006); and school construction projects are particularly complex due in part to multi-party involvement, schedule and funding intricacies, and federal, state, and local district statutory requirements (Vincent & McKoy 2008).

Multiple project delivery methods have been developed to reduce the impact of the previously described issues. Public school decision makers considering new construction projects must select the delivery method they feel is best suited to ameliorate the issues related to their particular circumstances. Between 2012 and 2014 a study was conducted to investigate the construction delivery methods being utilized for the construction of public schools in the Southeastern US. This paper documents the delivery methods in use during the 7-year period from 2006 to 2012 and provides insight into the reasons for the delivery method selections made by those in decision-making capacities.

DEFINITIONS

Although, varying definitions exist within the construction industry, the following descriptions obtained from the Associated General Contractors, 2011 publication, Project Delivery Systems for Construction by Michael E. Kenig have been utilized to define the project delivery methods discussed within this research.

Project Delivery Method – The comprehensive process of assigning contractual responsibilities for designing and constructing a project to include definition of project scope, contractual responsibilities, interrelationships of the parties, and the processes for managing time, cost, safety, and quality.

Design-Bid-Build (DBB) – The defining characteristics of this project delivery method are that a) the design and construction are separate contracts -- owner-designer, owner-contractor, and b) total construction cost is a factor in the final selection of the constructor.

Construction Manager at-Risk (CM at-Risk) – The defining characteristics of this project delivery method are that a) design and construction are separate contracts -- owner to architect, owner to CM at-Risk, and b) total construction cost is not a factor in final selection of the constructor.

Design-Build (DB) – The defining characteristics of this project delivery method are that the design and construction responsibilities are contractually combined into a single contract with the owner.

Public Schools – refers only to publicly funded school(s), grades kindergarten through twelfth grade (K-12).

LITERATURE REVIEW

Design-Bid-Build

The Design-Bid-Build (DBB) method was developed in the late 19th century following a number of fraud and abuse charges on large US infrastructure projects (US DOT 2006; Heady 2013). Known as the traditional method, DBB is the most widely accepted project delivery method utilized throughout the United States for both private and public construction (FMI/CMAA 2010). DBB is suitable for projects where scope can be clearly defined and unlikely to change, with complete design documents, and projects that do not have greater than average schedule challenges (Gordon 1994).

The structure of the DBB method is shown in Figure 1. The method follows a mostly linear process in which the architect is hired by the owner to help program and design the required facility prior to releasing the construction documents for competitive bidding (Civitello 2000). The owner then awards a construction contract to the general contractor submitting the lowest qualified bid. The general contractor typically enters into subcontracts with specialty firms (subcontractors), who perform the majority of the work (Demkin & AIA 2009).


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Figure 1: Design-Bid-Build Contract Structure (Civitello 2000)

The DBB project delivery method reportedly provides for an easily understood and well-documented process, the perception of fairness, owner control of the process, and reduced issues of corruption as well as sound schedule predictability and initial cost certainty (Rojas & Kell 2008; Kenig 2011; US DOT 2006). Disadvantages of this approach are reported to include: adversarial relationships, the competitive nature of the selection process driving prices to levels at or below the actual cost, construction documents and budgets prepared without input from those that will ultimately construct the project, and the lack of flexibility to incorporate changes due to the linear process of design followed by construction (AIA-AGC 2011).

Construction Manager at-Risk

Proponents of alternatives methods to DBB, such as Construction Manager at-Risk (CM at-Risk) and Design-Build (DB), believe that these methods offer the promise of improved cost, time, and quality performance when utilized on certain types of projects (Konchar & Sanvido 1998; US DOT 2006; AIA-AGC 2011). The key differences the CM at-Risk method offers center around collaborative approaches that are expected to improve performance throughout the project life cycle. Early selection of the CM at-Risk allows for constructability reviews and cost analyses during the development of the construction documents that assist the owner and architect in keeping the project within budget and on schedule. It also allows for fast-tracking, starting the construction phase prior

to the completion of the design phase and preparation of final construction documents.

Figure 2 presents the structure of the CM at-Risk delivery method. As is shown, CM at-Risk maintains the two separate contracts approach utilized with DBB. But what is not shown is the timing or method of selection (procurement). The procurement of construction services utilizing the CM at-Risk method is typically accomplished utilizing a Qualifications-Based-Selection (QBS) or a Best Value Selection, which focuses primarily on contractor qualifications and price, early on in the process in lieu of competitively bidding the project once the design and project documents have been completed (Kenig 2011).

Figure 2: Construction Manager at-Risk (CM at-Risk) Contract Structure (Civitello 2000)

Contractor selection based on qualifications is expected to improve project team relations and contractor performance, which can reduce risk and help to control cost and schedule overruns (AGC-NASFA 2006). Additionally, collaborative establishment of project costs utilizing an open-book Guaranteed Maximum Price (GMP) in lieu of competitive bidding may foster trust, leading to better project quality and reduced costs (Kenig 2011).

Design-Build

Proponents of Design-Build (DB) believe that this method provides many of the same benefits as the CM at-Risk method, even though it employs a different contractual relationship. Utilizing DB, the contractor and architect operate as a combined, single



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contractual entity with the owner as shown in Figure 3. And, although selection can be made based solely on competitive bidding, DB work is typically procured utilizing a QBS or Best Value approach (Kenig 2011).

Figure 3: Design-Build Contract Structure (Civitello 2000)

Procurement Laws and Regulations

Federal agencies including the Department of Defense, Veterans Administration, Department of Transportation, and many others regularly utilize alternative methods including CM at-Risk and DB. CM at-Risk is legally authorized for public building construction at the state level in 46 states. However, utilization is precluded in the states of Wisconsin, New York, and Hawaii, and its use is not clearly defined in the state of Iowa (AGC 2013).

DB is legally authorized at the state level for public building construction in 45 states and is precluded from use in the states of Wyoming, Wisconsin, and Delaware. State law is not clearly defined for DB building construction in North Dakota, Iowa, Arkansas, and Alabama (AGC 2013).

Close examination of this study’s states of interest, SC, NC, GA, and FL, reveals the following: GA and FL were two of the first states to allow utilization of CM at-Risk and DB for the construction of public schools after Congress enacted the Clinger-Cohen Act in 1996 (Smith 2001; Leavitt & McIlwee 2011). North Carolina approved utilization of CM at-Risk in 2001 with the passage of Senate Bill 914, and the 2008 Procurement Code Revisions – S. 282 fully authorized utilization of alternative methods in South Carolina in that same

year (McCook 2008). Furthermore, Article 9 Section 3005 of the South Carolina Model School District Code, effective August 15, 2011 authorizes the procurement of CM at-Risk, DB, and other alternative delivery methods for construction of infrastructure facilities in South Carolina.

Despite gaining legal authorization, Ghavamifar and Touran (2008) suggest that fear of favoritism, unnecessary added costs, lack of experience with the processes, and loss of owner control may be influencing public decisions to adopt or utilize alternative methods of project delivery. Additionally, anecdotal evidence suggests that public employees, acting in the positions of facility owners and district construction managers, may continue to avoid alternative delivery methods due to their lack of knowledge and experience or due to traditional operating procedures currently in effect (Carolinas AGC 2009).

RESEARCH METHODS

In order to determine the project delivery methods commonly being utilized to construct public schools within the study area, a review and compilation of public school construction records at the state and district levels was conducted. As a matter of convenience, the projects selected for this research were located in the states of Florida, Georgia, North Carolina, and South Carolina. Although states often differ in the manner in which funds for capital projects are raised and administered, the states selected were similar in that the majority of funding for their public school projects is provided from county or district levels (Filardo, Cheng, & Allen 2010). In order to reduce project variability related to construction materials, methods, and designs, the study population was limited to those projects that were completed (opened) during the 7-year period from 2006 to 2012. A survey instrument was developed and administered to district construction managers in order to obtain indications for the reasoning behind their project delivery method selections. A complete copy of the survey is provided as Appendix A.



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Data Collection and Distributions

A limited number of states collect and report detailed construction data from their school projects (Vincent & McKoy 2008). Although all of the states within the study area required their districts to submit various forms of new school construction data at different stages throughout the construction life cycle, the study revealed that a wide disparity of data and project records were collected and maintained. For example, Districts in Florida that construct new schools are required to submit a Report of Cost of Construction form to the Florida Department of Education (FL DOE) once the project has been completed. The FL DOE utilizes this information to calculate construction and plant facility costs per square foot and costs per student ratios and to track budgets resources utilized for school construction funding and other issues. The information is shared among developing districts through the use of the FL DOE online database. North Carolina and Georgia maintain similar databases containing a wide variety of project information. In contrast, the investigation revealed that the South Carolina database included only the date original construction documents were submitted for review, the name of the county or school district submitting those documents, and the name of the school to be constructed. Hard copies of actual project documents were only available from the state of Georgia. The primary reason behind the noted disparities in data collection and database maintenance may be due in large measure to the fact that the majority of funding and development responsibility for public school educational capital projects resides at the county or district levels.

The initial historical data collection effort began with a request for information from the Departments of Education within each state. Based on the data obtained, a list of all new school projects completed from 2006 to 2012 was compiled (FLDOE 2012; GADOE 2012; NCDOE 2012; SCDOE 2012). This information was tabulated in order to develop the sampling frame identifying a population (N) of 829 new public school projects constructed across 247 of the more than 460 school districts in the 4 state study area as depicted in Figure 4a. However, as is shown in Figure 4b, the majority of information required to determine

the project delivery methods being utilized was not available from the state level.

Figures 4a and 4b: State Provided Population by State and by Type

District Data Collection

The focus then turned to historical project data collection directly from the individual school districts in order to obtain the missing project delivery method data and the supplementary data required to complete the project delivery method performance research. Based on the population (N), estimations of the sample (n) and state samples (nST) required to maintain properties similar to the population were calculated. The calculations revealed that data from a minimum number of 90 projects would be required. School districts throughout the study area were then contacted by phone, email, and onsite visits were scheduled in order to collect the remaining data. Data sets containing the required project delivery



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information were obtained on 165 projects exceeding the calculated sample minimum requirement and producing the results presented in Figures 5a and 5b. The data shows that public school projects are completed with project delivery method ratios similar to that of the US commercial construction industry in Figure 5c having 55% DBB, 24% CM at-Risk (Blended), 16% DB, and 5% other (FMI/CMAA 2010).

Figure 5a and 5b: District Provided Sample by State and by TypeFigure 5c: Distribution of US Commercial Market

When distributed by both State and Type as shown in Figures 6a – 6d, the data are clearly differentiated.

Districts in Georgia predominantly utilize DBB, while those in Florida overwhelmingly utilize the CM at-Risk method. North and South Carolina district utilization resembles that of the US market. DB projects were found only in Florida and Georgia districts.

Figures 6a and 6b: District Provided Samples by Type, Florida and Georgia



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Figures 6c and 6d: District Provided Sample by Type, North and South Carolina

SURVEY FINDINGS

Following completion of the historical project data collection process, an internet based survey questionnaire was distributed to the district construction managers in order to discover the rationale behind the owner’s delivery method selections. Survey data collection was not targeted toward, nor was data obtained from, schools constructed utilizing DB due to the limited number of projects found to have been completed with that method in the population.

District Policies

Survey responses to the question, “Do the policies of your district require the utilization of a particular Project Delivery Method?” indicate that a large number of projects (20% of the total) are being completed in districts where policies require utilization of a particular method. As shown in Figure 7, this confirms that district policies

are in effect that serve to limit project delivery method selections, in spite of the federal and state statutory authority that provides for choice. Furthermore, statistically significant differences are shown to exist between the utilization policies found within those districts. The data shows that district policies requiring utilization of a particular method impact 29% of those utilizing DBB, which is nearly five times more than the 6% reported by those utilizing CM at-Risk. Therefore, district policies support and serve to increase the utilization of the DBB project delivery method.

Figure 7: District Requirements for Project Delivery Method

Additionally, as shown in the lower portion of Figure 7, the negative responses regarding policy requirements for utilization of a particular method indicate that 80% of all districts do not have such policies. This signifies that a larger percentage of those having the flexibility to choose are selecting the CM at-Risk method (94%) in lieu of DBB (71%).

In a related question, district managers were asked, “Does your district regularly utilize more than one type of Project Delivery Method for the construction of public schools?” As shown in Figure 8, 67% of CM at-Risk respondents and 72% of those utilizing DBB (70% of all respondents) indicated that their districts did not regularly utilize more than one delivery method. Perhaps the most important element surrounding this issue is the fact that, even though federal and state statutes allow for utilization of multiple methods in all districts within the study area, the vast majority of districts still utilize only one method of project delivery for all of their public school construction projects.



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Figure 8: Utilization of Multiple Project Delivery Methods

Selection Criteria for Project Delivery Methods

This study provides conclusive evidence that public owners utilizing both DBB and CM at-Risk delivery methods within the study area focus on relatively the same criteria when making project delivery method selections. The analysis of survey data indicates there are no statistically significant differences between the factors that district managers consider important when selecting project delivery methods. As shown in Figures 9, other than district policy requirements as discussed in the preceding section, the 5 factors receiving the largest percentage of Very Important responses by both CM at-Risk and DBB managers were: Improving Building Quality, Controlling Schedule Overruns, Reducing Disputes and Claims, Controlling Change Orders, and Improving Process Quality. This is surprising based on the expectation that managers utilizing CM at-Risk would have placed a much higher emphasis in areas such as Reducing Disputes and Claims, Reducing Change Orders, and Improving Process Quality than their DBB counterparts. And, although it is not unexpected to learn that this particular list of factors is considered with such a high level of importance by district managers (which ultimately expresses their desire to control quality, schedule, and cost variability), it is a noteworthy discovery to find that Improving Building Quality was selected most often by managers utilizing both methods.

While managers utilizing neither method focused highly on Reducing Overall Project Cost as being a Very Important factor for consideration, it should be noted that managers utilizing DBB selected it slightly more often than did managers utilizing the CM at-Risk method. Improving Project Team Relations was selected by a relatively low percentage of managers utilizing both methods, as was the Experience Level of the Owner (themselves), selected as being Very

Important by a mere 50% of managers utilizing CM at-Risk. This is striking due to the expectation that owner experience would have been viewed at a higher level of importance by those utilizing CM at-Risk since it is viewed as a more sophisticated process than DBB.

CONCLUSIONS AND RECOMMENDATIONS

The purpose of this study was to determine the project delivery methods being utilized for public school construction in the southeastern US and to provide insight into why those methods were being selected. This information is necessary in order assist decision makers at state and municipal levels in making informed choices when selecting the most appropriate project delivery methods for the construction of their school projects. The distribution of the sample indicates that project delivery method utilization for the construction of public schools is proportionally similar to that of the US commercial construction industry, with the majority of schools (55%) constructed utilizing the DBB method. However, stratified by state, the data indicates that the methods of project delivery being utilized and the manner in which those methods are being selected vary at the state and local level. Districts in Florida focus chiefly on CM at-Risk (65%) and Georgia

Figure 9: Criteria Considered “Very Important” for Project Delivery Method Selection



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predominantly utilizes DBB (91%). Delivery method utilization in North and South Carolina generally follows that utilized nationally.

Project delivery method selections are based on the personal experience and purchasing philosophy of the decision maker (Sanvido & Konchar 1999) and research has shown that the experience and perception of the owner influences the actual definition of project success which, in turn, influences the project delivery method selection (Chan & Chan 2004). The most important finding of this research is that district managers utilizing both the CM at-Risk and DBB methods make their project delivery method selections based on concerns for the same foundational issues. Furthermore, their selections are based on virtually the same level of concern for each of these factors. The five factors of importance selected most often, in order, are Improving Building Quality, Controlling Schedule Overruns, Reducing Disputes and Claims, Controlling Change Orders, and Improving Process Quality. Therefore, the research confirms that project delivery method selections are being made based on decision maker beliefs that their selected methods are superior in performance to opposing methods when utilized for the construction of public schools.

Researchers have suggested that issues such as fear of favoritism, unnecessary added costs, lack of knowledge and experience, loss of owner control, or commitment to traditional operating procedures may be influencing district policies on project delivery method utilization (Ghavamifar & Touran 2008; Carolinas AGC 2009). Although utilization of multiple project delivery methods for public school construction has been authorized in every state within the study area, this research has shown that multiple methods are still not authorized for utilization in 20% of the local districts. Furthermore, restrictive policies within 80% of these districts mandate utilization of DBB, increasing overall utilization of that method. Additionally, the evidence provided by this research indicates that only one type of project delivery method is being utilized within 70% of the districts. Public school construction projects are inherently risky due to specific project conditions, multiple party involvement, design complications, financial responsibilities, and many other issues. Beneficial properties exclusive to a particular project

delivery method may be useful or necessary in order to successfully manage the situational aspects experienced on certain projects. Therefore, restrictive policies imposed at the district level may be impacting the ability of managers to efficiently and effectively construct public schools.

FUTURE RESEARCH

In order for the public to benefit most, delivery method selections must be based on performance superiority and policies must be aligned so that the most appropriate delivery methods may be utilized when those situations requiring them arise. In order to determine project delivery method performance superiority, future research will be conducted to compare the performance levels of opposing project delivery methods utilizing metrics obtained from established public school project success factors.

The lack of a cohesive public school construction dataset is a barrier to the pursuit of knowledge and solutions to problem issues. Currently, data are not uniformly collected or reported in districts across the four states included within this study and thus, development of a shared database is not possible. Research directed at the development of a common data collection method and database would be useful for all future studies employed to assist the education system with the construction of public schools.

REFERENCES

Abramson, P. (2012). The 2012 school construction report. School Planning & Management, CR1-16. Retrieved 9/24/2012, 2012, from file:///C:/Users/Student/Downloads/SPMConstruction2012.pdf

Abramson, P. (2013). The 2013 school construction report. School Planning & Management, CR1-16. Retrieved 11/19/2013, 2013, from file:///C:/Users/Student/Downloads/SPMConstruction2013.pdf

AGC (2013). The Associated General Contractors of America website. Project Delivery Resources, State by State Maps. Retrieved 1/26/2015, from http://www.agc.org/cs/industry_topics/project_delivery/cmatrisk

AIA-AGC, (2011). Primer on project delivery, second edition.



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AGC-NASFA (2006). Best Practices for use of Best Value Selections.

Carolinas AGC (2009). Conference on Alternative Project Delivery Methods.

Chan, A.P.C., Chan, A.P.L. (2004). Key performance indicators for measuring construction success. Benchmarking: An International Journal, 11 (2), 203 – 221.

Civitello, A.M. (2000). Construction Operations Manual of Policies and Procedures. USA: McGraw-Hill.

Demkin J.A., AIA (2008) The Architect’s Handbook of Professional Practice. Hoboken, NJ: John Wiley & Sons, Inc.

Filardo, M., Cheng, S., & Allen, M. (2012). 21st Century School Fund website. Retrieved 2/16/2012, from http://www.21csf.org

FLDOE (2010). Public Schools: Cost of Construction. Retrieved 05/30/2012, from http://www.fldoe.org/edfacil/oef/cocps.asp

FMI/CMAA, (2010). Eleventh annual survey of owners: rising from the ashes of recent economic woes.pdf (application/pdf object).

Ghavamifar, K., & Touran, A.T. (2008). Alternative project delivery systems: Applications and legal limits in transportation projects. Journal of Professional Issues in Engineering Education and Practice, 134, 106-12.

GADOE (2012). Georgia Facilities [Data files]. Received 02/07/2012, from [email protected]

Gordon, C. (1994). Choosing appropriate construction contracting method. Journal of Construction Engineering, 120(1), 196–210.

Heady, E.J. (2013). Construction law; the history is ancient! Smith, Currie, and Hancock, LLP, Retrieved 4/2/2013, 2013, from http://www.constructionconnection.com/blog/smith-currie-hancock-llp/

Kenig, M.E. (2011). Project delivery systems for construction. Arlington, VA: The Associated General Contractors of Americas.

Konchar, M., & Sanvido, V. (1998). Comparison of u.s. project delivery systems. Journal of Construction Engineering and Management, 124(6), 435-445.

Leavitt, J.M., & McIlwee, J.C. (2011). Navigating state design build statutes in the wake of a “turned federal battleship.” Practicing Law Institute Symposium. Building Better Construction Contracts: Tailoring Incentives, Creating Collaboration and Developing Effective Risk Allocation Panel Discussion: Creating a Better Design/Build Agreement. New York City.

McNichol, E., Oliff, P., & Johnson, N. (2011). States continue to feel recession’s impact - center on budget and policy priorities.pdf (application/pdf object).

McCook, K. (2008). AGC Project Delivery Method Conference. Highlights of recent changes to state procurement statutes, regulations, and policy. Conference handout.

NCDOE (2012). Project Delivery Query [Data file]. Received 4/15/2012, from [email protected]

Oliff, P., Mai, C. & Palacios, V. (2012). States continue to feel Recession’s impact. Center on Budget and Policy Priorities.

Rojas, E.M., & Kell, I., (2008). Comparative analysis of project delivery systems cost performance in pacific northwest public schools. Journal of Construction Engineering and Management, 134(6), 387-98.

Sanvido, V., & Konchar, M. (1999). Selecting project delivery systems: Comparing design-bid-build, design-build, and construction management at risk. State College, PA: Project Delivery Institute.

Saporita, R. (2006). Managing Risk in Design and Construction Projects. NYNY: ASME Press.

SCDOE (2012). CD Final Query and Greenville Daily Log [Data files]. Received 03/27/2012, from [email protected]

Smith, G. (2001). Impact of project delivery method on construction costs for school construction projects in atlanta, ga, from 1993 to 2001. Dissertation. Clemson University.

United States Census Bureau. (2011). Capital spending report 2011.pdf (application/pdf object)

United States Department of Transportation. (2006). Design-build effectiveness study.

Vincent, J. M. & McKoy, D. L. (2008). The complex and multi-faceted nature of school construction costs: factors affecting California. American Institute of Architects, California Council.



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Construction Degree Graduates: An Evaluation of Depth of Skill Understanding and Skill Priority by Construction Industry

Professionals

Ben F. Bigelow PhDTexas A&M University | [email protected]

Edelmiro Escamilla PhDTexas A&M University | [email protected]

Lauren Kuecker MSCM

Texas A&M University | [email protected]

ABSTRACT: Previous studies have been conducted to determine the skills the construction industry expects graduates and entry level professionals to possess. The majority of those studies report soft skills and technical expertise as the main qualities graduates should have upon entering the industry. Many differing skills have been reported, but neither the expected depth of understanding in those skills nor the industry’s priority of those skills have been explored. To do so, a survey was administered to members of the Construction Industry Advisory Council at Texas A&M University asking these questions. Depth of understanding was evaluated on a four level basis (Awareness, Comprehension, Application, and Analysis) and priority was based on participant ranking. The results show that soft and technology skills are expected to be understood at a higher level, while other construction skills are expected at a lower level. Further, soft skills comprised 8 of the top 10 skills in priority.

Dr. Ben F. Bigelow is an Assistant Professor in the Department of Construction Science at Texas A&M University, his research focuses on construction education, workforce issues, housing, and Underrepresented groups in construction. .

Dr. Edelmiro Escamilla is an Instructional Assistant Professor in the Department of Construction Science at Texas A&M University. His research focuses on Hispanic issues in construction, workforce shortages, and construction education.

Lauren Kuecker is a graduate of the Masters of Science Construction Management program at Texas A&M University, and earned her bachelor degree in Environmental Design also at Texas A&M University. She is an assistant project manager at Kieschnick General Contractors.

Keywords: Construction Education, Skills, Competencies

INTRODUCTION

The skills needed to be successful in the construction industry have been the subject of many studies (Ahn, Kwon, Pearce, & Shin 2010; Souder & Gier 2006; Badger, Wiezel, & Bopp 2007; Perreault 1993; Banik 2008), and as a result many broad skill sets and even more specific skills have been identified as necessary in the construction industry. These studies however typically have been performed individually, and have

focused on specific skill set areas, so as each study has been performed the list skills needed to work in the construction industry has continued to grow.

Previous studies report that there is a need for technical, soft, and other skill sets among graduates, however there is no prioritization of the skills. Further the depth of understanding of skills, that graduates should poses, has not been explored. Love et al. (2001) pointed out that managers should be tolerant and considerate of the inexperience of recent graduates, but by preparing students not only with

Construction Degree Graduates: An Evaluation of Depth of Skill Understanding and Skill Priority by Construction Industry Professionals


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the appropriate skills, but also the appropriate depth of understanding, they will be better prepared to contribute upon entering the industry.

Departments of construction higher education frequently utilize advisory councils, made up of industry professionals, for direction on what skills the construction industry expects of graduates. Feedback may occur in an annual meeting (Hynds & Smith 2001), or through informal communication. This information collected from the industry is valuable and helps attenuate any disconnect between academia and industry. However this feedback is unlikely to be formally disseminated, and as a result does not reach many faculty and administrators. The purpose of this study is to provide empirical data supporting a prioritized list of the skills needed by construction graduates and to provide a minimum depth of understanding of these skills for educators to use in guiding their curriculum. This information will allow universities to better prepare students to succeed in the construction industry.

This study asked; what is the skill depth of understanding that the construction industry expects of graduates entering the construction industry? Further it asked, among the many skills identified as necessary by previous study, which skills does the construction industry place the highest priority on? These questions are significant and add to the body of knowledge on this subject for two reasons. First, while many studies have presented skills or skill sets needed by graduates, they have not compared the different skills, and in the cases where skills have been compared and prioritized they have compared a limited listing of skills, not an exhaustive listing of the skills reported as necessary in the literature. Second, previous studies have focused on skill needed, but have not evaluated the depth of understanding expected in those skill areas, which was a primary focus of this study. LITERATURE REVIEW

Twenty years ago Perreault (1993) determined computer skills and field and technical skills as necessary in construction. Ten years later computer literacy was again reported as a necessary skill (Love,

Haynes & Irani 2001). A foundation of technical and construction skills and other competencies such as computer skills are expectations required of graduates to succeed in the construction industry (Ahn et al. 2010). While researchers generally concluded that recent graduates, from programs of construction higher education, have satisfactory technical skills and meet industry expectations, they report that graduates lack training in soft skills, and concluded that universities do not place enough emphasis on them, specifically on communication (Banik 2008). Souder and Gier (2006) also report a need for emphasis on other skill areas. This need for soft skill training likely contributes to why construction student competition are so valued by the construction industry, as participants gain soft skills through participation (Bigelow, Glick, Arragon, 2012). Pant and Baroudi (2008) concluded that a balance needs to be struck in education between technical and soft skills.

Of the primary skill requirements of construction graduates determined by Perreault (1993), three of them were in soft skills: team work, effective oral and written communication, and analytical and management skills. In terms of fundamental soft skills, Souder and Gier (2006), and subsequently Ahn et al. (2010), found that ethical issues, leadership, communication and business writing, and management organization to be the primary expectations of professionals. Love et al. (2001) found that students failed to meet expectations with certain soft skills such as intrapersonal skills, time management, and being able to exercise personal judgment. Badger, Wiesel, and Bopp (2007) also found leadership to be an expected skill required by graduates. They classified soft skills such as communication and team work as leadership qualities. Over the years, soft skills such as ethical issues, oral and written communication, leadership, team work, and management skills appear consistently as necessary (Perreault 1993; Love et al. 2001; Souder & Gier 2006; Badger et al. 2007; Ahn et al. 2010), unfortunately the depth of understanding of those skills has not been addressed.

While soft and technical skill sets were the most common throughout the research, a number of other skill sets were identified that are more directly related to the construction industry. Perreault (1993) found contract and legal documentation knowledge to be



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primary requirements of graduates. Souder and Gier (2006) reported negotiation skills, along with skills such as estimating, plan and specification reading, safety, and scheduling. Ahn et al. (2010) determined that problem solving skills were needed.

METHODOLOGY

In order to address the questions posed by this study: What is the skill depth of understanding that the construction industry expects of graduates entering the construction industry? Among the many skills identified as necessary by previous study, which skills does the construction industry place the highest priority on? All of the different skills needed by graduates identified in the literature review, were used to inform this study. From those skills the survey was produced that was used for data collection, specifically allowing for prioritization and evaluation of expected depth of understanding.

Population

This study used the Construction Industry Advisory Council (CIAC) at Texas A&M University as the selected sample. The Texas A&M CIAC was selected for convenience to the researchers, however because of its size (at the time, over 100 members) it could still provide a robust sample (n). A total of 61 responses were received, and after filtering 56 usable responses emerged. Responses were delimited to those with at least 4 years of experience, and holding a management position. Participants did not provide feedback for every single skill, so pairwise deletion was used and the n for each skill varies from 46 and 49. The majority of respondents (32) were Senior Executives, with other management categories such as Project or Program Management, Superintendents, Construction Managers, and Resource Managers included. The majority (44) had more than 15 years of experience. 43 participants classified themselves as being from the general building sector of the construction industry. The remaining 12 came from the heavy industrial and infrastructure sectors of the construction industry.

The sample in this study represents a good cross-section of types of construction companies and sizes,

however the small sample (n = 56) and the regional nature of the participants (state of Texas) present threats to the external validity of this study. The authors assert that the sample is representative of the population, which is a vital consideration in external validity (Gliner, Morgan, & Leech, 2009), but results should only be generalized outside the state of Texas with great caution. Data Collection & Analysis

Institutional Review Board (IRB) approval was received before beginning the data collection process. The survey was organized based on the skill sets reported in the literature and a total of four skill set areas were identified and they include: soft, technology, project development, and project execution skills. Within each skill set area, individual skills were presented and participants were asked to report at what depth of understanding they expected students to possess each skill. Four different options for depth of understanding were provided:

• Awareness- The simplest level, the graduate is aware of the skill and its meaning.

• Comprehension- The graduate has an understanding of the skill, but is not yet able to apply it.

• Application- The graduate can apply the skill.• Analysis- The highest level, the graduate is able to

not only apply a skill, but also able to analyze and understand it completely.

After providing an expected depth of understanding for each skill, participants were asked to rank the skills they believe are most important for a recent graduate to have when entering the construction industry. Rankings from 1 to 10 were requested, with 1 being the most important skill. Although the list included more than ten skills, participants were only asked to rank their top ten. The remaining unranked skills were assigned an 11. ANALYSIS & DISCUSSION

A quick overview of the responses was not surprising, they indicated that most skills (77%) should be understood at a comprehension or application level, indicating graduates should have more than just knowledge of the skills in question, but are not expected to be experts. 723 (39%) were on an Application level,



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Table 1: Soft Skills Response Breakdown

699 (38%) were on a Comprehension level. Only 5% of responses indicated an analysis level, the highest level, as the expected depth of understanding. 18% of responses indicated the need of only an awareness level, the lowest level, depth of understanding. To better understand these aggregate numbers each skill set area was considered.

Table 1 reveals consistency among soft skills. According to the participants most skills falling in the soft skill set area are expected to be understood at an application level. So while graduates are not expected to be experts they are expected to have more than just a basic understanding of these skills. Graduates are expected to be able to apply these soft skills in their positions.

Despite the consistency between most of the soft skills, there are some exceptions. Non-native language skills and cultural/gender/generational awareness were two exceptions to the application depth of understanding expected with other soft skills. Cultural/gender/generational awareness was closely split between awareness and comprehension indicating expectations are lower regarding this skill. However non-native language skills have the lowest expectation of any skill set, with nearly 80% of participants indicating that they expected only an awareness depth of understanding (the lowest level). This result is particularly interesting given the high participation rate of non-English speakers in the construction workforce. The researchers point out that this study does not question the value of the skills presented, it only evaluated the expected depth of understanding for graduates so it should not be construed that non-native language skills are not desirable or beneficial,

only that graduates are not currently expected to have more than an awareness of them.

Like with soft skills, the construction industry has great expectations of students in regard to their depth of understanding of technology skills. As shown in table 2, with 9 responses for an analysis level depth of understanding, computer skills have the highest expectations of any skill in this study. In fact 96% of respondents indicated that they expected graduates to have computer skills at an application or analysis level, which is also higher than any other skill in this study. Proficiency with specific software did not have quite the same expectation as computers in general. However it was still clear that the industry has a high expectation of a graduate’s depth of understanding regarding software. Graduates are expected not only to possess, but to be able to apply skills with computers and different software packages. Skill expectations with technology are the highest of all skill areas surveyed.

While soft skills and technology skills could apply to almost any industry, project development skills represent a skill set area focused on construction specific applications. While a few respondents indicated that these skills should be understood at higher levels, about half of all responses indicated these skills should be understood at a comprehension level, where graduates would be expected to have only a basic knowledge of the skill. Table 3 displays all the results for project development skills.

While most responses regarding project development skills indicated an expectation of a comprehension level depth of understanding, with only one exception,



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between 20% and 30% of responses indicated that these skills were only expected at the lowest level, awareness. The results are intriguing as these skills are certainly important skills for a construction professional. Despite their importance, they are not expected to be developed in new graduates. The researchers conclude that these skills are expected to be acquired in a work environment, not in college. The researchers conclude this to be the case because project development skills

are connected to positions that are unlikely to be filled by a recent graduate, these positions are typically filled by more experienced individuals.

Legal issues represent a variation within the project development skill set. Overall the expectations for graduates regarding legal issues is low, with 47% of responses indicating only an awareness depth of understanding, while other skills were expected at a comprehension level. These results do not devalue skill with legal issues, however it is clear that expectations

of these skills is low and second only to non-native language skills as having the lowest expectation regarding a graduate’s depth of understanding.

Project development skills are more likely to be used in higher ranking positions, but project execution and control skills fall to positions that a recent graduate is likely to occupy. As can been seen in Table 4, the expectations for graduates regarding project execution

and control skills are higher than project development skills. Graduates should be able to understand these skills and apply them at a low level.

Comparing tables 3 and 4 we see a shift in expectations from the project development skill set to project execution and controls skill set. While the depth of understanding expected for most of these skills in both areas are at the comprehension level, there is a shift from an awareness-comprehension for project development to comprehension-application for project

Table 2: Technology Skills Response Breakdown

Table 3: Project Development Skills Response Breakdown



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execution and controls. Based on these results it is clear that the construction skills that should be focused on in construction education are those found in the project execution and controls skill set. The more profound depth of understanding expected for most soft skills is particularly interesting when soft skills are compared to the responses for other skill set areas. As is shown in tables 3 and 4, skills that are more technical in nature dealing with construction specific skills do not have the same expectations regarding depth of understanding. These results indicate that the industry has greater expectations in soft skill areas than they do regarding most technical construction skills. This finding is of particular importance given the subject matter of most construction education courses, where a construction skill is the focus and soft skills are accessories to the subject. These results indicate that perhaps the opposite approach would better prepare students to work in the construction industry, making soft skills the focus of courses and construction skills as accessories.

Like soft skills, Technology skills were shown to have high expectations regarding a graduate’s depth of understanding. Although technology skills only included two specific skills, expectations were notably higher for them than for any of the more construction specific skills and were comparable to many soft skills. The industry clearly has high expectations of graduate’s abilities to use technology. This depth of understanding expected regarding both soft skills and technology skills seems to indicate that the construction industry places greater importance on these areas.

Depth of understanding is an important measuring stick of student preparation for careers in the construction industry. However, high industry expectations are not necessarily the same as importance when comparing one skill to another. This study supports previous research regarding skill needs of construction graduates and concludes that all of the skills identified in the literature are valued by the construction industry and are expected of graduates from programs of construction higher education. Because the depth of understanding expected in each of these skills varies, it is important to recognize skills with a higher level of understanding may merit additional time or emphasis in a curriculum, but are not necessarily more important than other skills with lower expectations regarding depth of understanding.

Skills that have a higher depth of understanding expectation could easily be construed as more important than another skill, as a result this study also explored the priority placed on the different skills identified by the construction industry. To evaluate priority among the skills identified, the study asked participants to prioritize the skills they feel are most important for graduates to possess.

Participants ranked their top ten most important skills in order with 1 as the most important skill a graduate should possess. Because there were more than 10 skills to choose from, an 11 was assigned to any skill which participants did not include in their top ten. Using the 11 for the non-responses prevented the data from becoming skewed. Table 5 lists the rankings of the skills based on the average score, with lower scores indicating greater importance. The skill set category

Table 4: Project Execution & Controls Skills Response Breakdown



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for each skill is also listed to illustrate which categories received the most attention concerning priority.

The high expectations of most skills in the soft skill area coincide with the high priority identified for construction professionals and at number 12, problem solving just missed the top ten. It should be noted however that the remaining soft skills constituted three of the four lowest priority skills. This somewhat bipolar result among the soft skills set is important as it indicates that a broad focus on soft skills (or any one

particular skill area) may not serve a graduate well, rather there are certain soft skills which are considered significantly more important, and for which a graduate’s depth of understanding is expected to be much higher. Even given the low priority of three of the soft skills, the value of soft skills in general is punctuated by the fact that eight of the top ten most important skills are soft skills.

Technology skills remained high on the priority list however their rankings were lower than might

Table 5: Skill Priority by Average Score



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have been expected based on the reported depth of understanding expectations. An evaluation based solely on the expected depth of understanding would have made a solid argument for computer skills as the single most important skill, and software proficiency at least in the top ten. In the priority results however, computer skills and software proficiency ranked 10th and 14th respectively, indicating that while these skills have high importance, the construction industry does not value them as highly as most soft skills and even a few project execution skills. While the depth of understanding results and priority results do not exactly coincide, the data presented indicates that technology skills are among the most important skills a student should possess upon graduation.

The skill of planning and scheduling represented the one construction specific skill that made the top ten. It is worth noting however that estimating came in at 11, only a 16th of a point behind the number ten skill. Scheduling and estimating skills were split between a comprehension and application expectation for depth of understanding, so they are skills whose value could have easily been overlooked. Based on the priority rankings, these two skill areas are considered the most important construction specific skills for graduates to possess, making them arguably the construction skills deserving of the greatest focus in construction education. But despite that argument for their importance, the construction industry has a very reasonable, comprehension-application, expectation of graduates regarding these skills.

Project execution skills were scattered from number 9 to number 33 on the priority list, however most of these skills fell within the top twenty skills. As might be expected from the depth of understanding expectations, project executions skills generally fell in behind soft skills, and were comparable with technology skills based on priority. Project execution skills generally include those construction skills that graduates will be required to perform in their positions upon entering the construction industry. While there is an expectation that graduates will have a comprehension-application depth of understanding of most of these skills, they are considered less important than soft skills for graduates. This study confirms the need and expectation of these skills for graduates, however based on the results,

the researchers conclude that even more important than these construction specific skills is the ability to communicate in their regard on a job site. The fourth skill area considered was project development skills. These skills had an awareness-comprehension depth of understanding expectation, and their rankings in regard to importance ranged from 16 to 36 reinforcing the conclusion that these are skills that the construction industry expects graduates to be aware of, but that they will acquire in depth with time and experience working. These findings do not mean project development skills are not important for graduates, rather they indicate that these skills and topics should be accessories to construction education not its focus. The researchers conclude this is primarily because these skills are not typically utilized in positions filled by new graduates, so they are a lower priority.

The results regarding depth of understanding and skill priority reaffirm the importance of a diverse skill set in the construction industry. Soft skills, technology skills, and construction specific skills are all valued and expected, but there is a distinct hierarchy among them and the level of understanding of each skill varies. Soft skills are seen as the highest priority skills to possess, whereas technology skills have the highest expectation for depth of understanding. As a result construction programs should tailor to meet those priorities and expectations. The researchers assert that knowing the depth of understanding expected and the priority of these skills are both vital pieces of information for shaping construction education as some skills may be important but have low depth of understanding expectations which should impact instruction and training in those skills.

Despite the findings of this study, indicating the importance of soft and technology skills, it is project execution and development skills that are the focus of most classes in programs of construction higher education. While the skills taught in these classes are important, these findings clearly indicate that construction education is likely focusing on areas that are less important for a graduate upon entering the construction industry. The results do have exceptions, as skills such as scheduling and estimating have a high priority, but in general these findings indicate that the focus of most construction education



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programs should be soft skills with construction specific skills as an accessory. Based on both sets of findings, the researchers conclude that a graduate who is able to apply soft skills such as ethics, team work, communications, and professionalism will acquire technology and project specific skills, however the opposite may not be true. The scope of this study did not consider current teaching pedagogy in construction education, and how these findings could be applied, beyond the anecdotal experience of the researchers. As a result the researchers recommend for future study the following topics:

• How do programs of construction higher education currently align with these conclusions regarding depth of understanding and priority?

• Do recent graduates’ skills align with these expectations?

• How is and individual’s depth of understanding in soft skills determined?

• Are these results from the state of Texas consistent with other parts of the country?

REFERENCES

Ahn, Y.H., Kwon, H., Pearce, A.R., & Shin, H. (2010). Key Competencies for U.S. Construction Graduates: An Exploratory Factor Analysis. ASC Proceedings of the 46th Annual Conference. Wentworth Institute of Technology, Boston, Massachusetts.

Badger, W.W., Wiezel, A., & Boop, P.H. (2007). Leadership Education and Training: “Leadership Skills Truly Make a Difference.” ASC Proceedings of the 43rd Annual Conference. Northern Arizona University, Flagstaff, Arizona.

Banik, G. (2008). Industry expectations from new construction engineers and managers: Curriculum improvement. Paper presented at the American Society for Engineering Education (ASEE) conference.

Bigelow, B. F., Glick, S., Aragon, A. (2012). Participation in Construction Management Student Competitions: Percieved Positive and Negative Effects. International Journal of Construction Education and Research. 9(4), 272-287.

Gliner, J. A., Morgan, G. A., & Leech, N. L. (2009). Research methods in applied settings: An integrated approach to design and analysis. Psychology Press.

Hynds, T., & Smith, J. (2001) Industry Advisory Councils of Undergraduate Construction Programs: A Comparative Study of Common Practices. ASC Proceedings of the 37th Annual Conference. University of Denver, Denver, Colorado. pp.239-246.

Love, P. E. D., Haynes, N.S. & Irani, Z. (2001). Construction managers’ expectations and observations of graduates. Journal of Managerial Psychology, 16(8), 579-593.

Pant, I. & Baroudi, B. (2008). Project Management Education: The human skills imperative. International Journal of Project Management, 26(2), 124-128.

Perreault, R. (1993, April 15-17). Identification of the occupation requirements of the construction manager. Paper presented at the ASC 29th Annual Conference, Fort Collins, Colorado.

Souder, C. & Gier, D. (2006, April 20-22). What does the Construction Industry expect from recent Construction Management Graduates? Paper presented at the ASC 42nd Annual Conference Fort Collins, Colorado.

Soft Skills. (n.d.). In Oxford Dictionaires online. Retrieved from http://www.oxforddictionaries.com/us/definition/american_english/soft-skills?q=soft+skills



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Accident Patterns in Road Construction Work Zones

Tariq Shehab, Ph.DCalifornia State University, Long Beach | [email protected]

Leah Phu, M.Sc.California State University, Long Beach | [email protected]

ABSTRACT: Traffic safety in road construction zones has always been a major concern for engineering practitioners, departments of transportation, law enforcement professionals, researchers and many others. Due to the deteriorated condition of road and underground infrastructure facilities in the United States, the number of rehabilitation projects and their associated traffic accidents have increased over the past few years. To perform the needed road and underground utility rehabilitation activities in safer environments, researchers need to understand the causes behind these accidents. Although many researchers have been exploring the safety of many construction activities, research pertaining to road construction projects is lacking and insufficient. Despite the fact that few research efforts have been made to fill in this reported gap, many important collision factors have not yet been explored. This paper augments earlier safety research work by analyzing 11 years’ worth of data and presenting a wide range of primary factors that contribute to traffic accidents in road construction zones. The paper also presents the impact of many factors, such as environmental and road conditions on the type and severity of collisions. The findings presented in this paper will not only contribute to the reduction of construction zone related traffic accidents by avoiding their causes, but it will also assist contractors and transportation authorities in better planning and scheduling their projects in a manner that positively contributes to the safety of served communities. This will be achieved by presenting factors that highly influence the decision on the start dates and duration of projects such as, day(s) of the week and month(s) of the year that are mostly associated with higher rates of traffic collisions.

Tariq Shehab is an associate professor of construction engineering and management. His area of expertise includes heavy civil construction.

Leah Phu is a research assistant in the area of construction engineering and management. Her area of expertise includes traffic management and accidents in the construction industry.


Keywords: Safety, Roads, Construction, Accidents, Heavy Civil.

INTRODUCTION

Construction projects are listed among the most risky work environments in many countries. In the United States, the Business Insider ranked the construction industry among the 15 most dangerous US jobs (Lubin and Lincoln 2011). In 2012, 3.7% of full time construction workers had non-fatal occupational injuries, and at least 3 American workers may fail to return home by the end of every working day due to fatalities on construction sites, which is about 17% of fatal accidents across all industries (Bureau of Labor

Statistics 2013, Chi and Han 2013, Solis-Carlos and Arcudia-Abad 2013).

On the international level, it was documented that about four annual fatal construction accidents per 100,000 workers may take place in the United Kingdom. In Singapore, Chile and Mexico, the construction accidents per 100,000 workers are nine, 18 and 21, respectively (Solis-Carlos and Arcudia-Abad, 2013 and Haslam et al. 2005). Falkner et al. (2012) documented that while the number of fatal accidents in Finland, Netherlands, Switzerland, Ireland, Sweden, Norway, Japan, France and Belgium is less than ten workers per 100,000 workers, the number of fatal accidents in


47Accident Patterns in Road Construction Work Zones

Thailand, Indonesia, South Africa and Russia is between 10 and 40 workers per 100,000 workers.

While so many researchers have been exploring the safety environments in many types of construction activities, limited research has addressed the highway construction sector (Kim et al. 2013 and Arditi et al. 2007). The following information presents the research that addresses road construction projects.

LITERATURE REVIEW

Kemper et al. (1984), focused on the impact of narrow lanes for traffic control on construction sites. The researchers concluded that narrow lanes contributed significantly to rates of construction zone related traffic accidents. They reported that when the nine foot-wide lane was introduced to the public, the traffic accident rate increased from 1.68 accidents per million vehicle-miles to 2.63 accidents per million vehicle-miles. They also reported that when an 11 foot-wide lane was used, the traffic accident rates were less than the rate associated with a nine-foot lane.

Bryden et al. (1998) analyzed three years’ worth of data on New York Department of Transportation highway construction projects, and reported that 33% of all work zone traffic accidents were related to pavement bumps and joints, drainage features, excavation materials and construction vehicles. Their analysis revealed that 20% of all construction zone traffic accidents involved construction vehicles, equipment and/or pedestrian workers. Bryden and Andrew (1999) covered a five-year study period and reported that traffic accidents accounted for 20% and 40% of serious injuries and fatal accidents on construction sites, respectively. Although the study identified three types of traffic accidents (i.e. vehicles that collide with other vehicles, fixed objects and pedestrians), it did not address the collisions’ contributing factors.

Mohan and Gautam (2002) addressed the cost of highway work zone injuries over three years. They reported that while 30% of the reported accidents

involved construction workers, 70% involved motorists. The types of motorist accidents reported in this study were overturning, rear-end collision, small-hit-object, large-hit-object and side impact. The direct overall cost associated with these types of accidents ranged from $5.74 to $6.75 billion.

Qi et al. (2005) reported, based on a seven year study period, that construction work zones had more rear-end accidents compared to non-construction work zones. They also reported that rear-end accidents in which alcohol, night conditions, and pedestrians were involved were more severe. Furthermore, rear-end accidents that took place before the work area approach taper were much more severe compared to other work zone locations (e.g. after and within work area zones.

Bai and Li (2006) focused on fatal accidents only and reported that male drivers caused about 75% of fatal work zone accidents in Kansas. These accidents were mainly attributed to inattentive driving and misjudgment. Drivers aged of 35 to 44 were associated with the highest percentage of fatal accidents. While drivers younger than 55 caused most nighttime accidents, drivers between 35 and 44, and older than 65, caused the most daytime accidents.

Arditi et al. (2007) studied the impact of daytime and nighttime on accidents in highway construction zones. The study focused on fatal accidents only, and was based on a five-year study period. The researchers found that nighttime highway construction projects were five times more hazardous than daytime projects. They found that weather parameters had very limited effect on this conclusion.

Li and Bai (2008) compared characteristics between fatal and injurious accidents in highway construction zones during a period of five years. The researchers focused on head-on and rear-end collisions. They reported that head-on and rear-end were the most common types for fatal accidents and injuries, respectively. The researchers also reported that while ignored traffic control, alcohol,


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and speeding caused a large proportion of fatal accidents (about 60%), tailgating and inattention had a very high contribution to injuries (about 70%). Unfavorable light conditions caused more fatal accidents than injuries.

Kim et al. (2013) studied the factors that contributed to non-traffic accidents on highway construction projects. They classified highway construction work into six groups: drainage, tunnels, installations, earthmoving operations, paving activities and structures. The researchers reported that types of accidents vary between these six groups of projects. Although the researchers did a very good job in showing the significant differences between accidents that take places on six types of construction activities, they limited these differences to time of accidents and provided very few causes of accidents (e.g. fall and struck by).

Farmer et al. (2013) reported that 120 death cases, at least, were attributed to road construction activities in Texas, Georgia, California and Florida in 2010. Snyder and Associates (2013) reported that the US daily injury and death rates due to road construction activities were about two and 100 persons, respectively. Furthermore, 85% of killed people are motorists. The National Work Zone Safety Information (2013) reported about 600 national work zones traffic fatality cases in 2010, about 50% of which were in Texas, Georgia. Florida, California, Illinois, Pennsylvania and Oklahoma. It further reported that about 2% of traffic crash fatalities took place in construction work zones. The National Highway Traffic Safety Administration (2014) reported that total national yearly property damage was about $900 billion.

Although the aforementioned studies present excellent and valuable safety research regarding road construction projects, Kim et al. (2013) and Arditi et al. (2007) agreed on the need to expand and the research to explore additional safety issues related to this class of projects. Furthermore, Kim et al. (2013) reported that research pertaining

to road construction projects was lacking and insufficient.

This research is presented to fulfill the reported need to understand traffic collisions in road construction projects, which accounts for the majority of accidents in this category of the construction industry, and to identify their contributing causes. It fills in the gap in safety research work by focusing on traffic accidents in construction zones only. It augments earlier research presented in the literature and overcomes the existing limitations, such as not addressing a wide variety of primary collision contributing factors, focusing on limited types of collisions and addressing fatal accidents only. This study went beyond previous ones by investigating additional important and influential factors such as traffic volumes, roadway classifications, property damages and human errors during the daytimes and nighttime. It ameliorates the knowledge of traffic accidents in road construction work zones by highlighting a wide range of issues (e.g. contributing factors and roadway classifications) that should be addressed by the team of road project participants, including contractors, to minimize traffic accidents in this class of projects. It uses information collected from traffic accident reports that were prepared during a period of 11 years in Buena Park, California.

DATA COLLECTION

Data for construction zone related collisions that happened from January 1, 1999 to December 31, 2009 were downloaded from the City Traffic of Buena Park Collision Database. To perform this task, Crossroads software was used. Buena Park is home to about 80,000 people in the northwestern part of Orange County, CA, where two major freeways intersect: Interstates 5 (I-5) and State Route 91(SR-91). Buena Park is classified as an entertainment zone (E-Zone), where attractions such as Knott’s Berry Farm, Medieval Times Dinner and Tournament, Ripley’s “Believe It or Not!” Museum, Knott’s Soak City and the Pirates Dinner Adventure are located.



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To perform this study, the authors focused on accidents that took place within construction work zones, which are defined as sections of highway where construction, repair, or maintenance was being performed. A construction work zone could be divided into four sections: 1) advanced warning section; 2) transition section; 3) activity section and 4) termination section (U.S. Department of Transportation 2009).

In the advanced warning section, single or multiple signs are used and may be up to 0.5 miles long. The transitional section is where a reduction in road width is required. This reduction is achieved using tapers, which vary in length, depending on speed limits (U.S. Department of Transportation 2009). The activity section is where work takes place, and could be stationary or moving. The termination section consists of a downstream taper that may extend up to 100 feet beyond the activity section. A more detailed description regarding the characteristics of construction zone sections, such as speed limits and dimensions, were presented in the U.S. Department of Transportation (2009).

During the 11 year analysis period, 9,426 traffic accidents were reported. A closer look at the traffic collision reports reveals that the following ten pieces of information are documented in each report: 1) time of day; 2) day of the week; 3) month and year; 4) lighting condition (e.g. daylight or dark-no street lights); 5) type of

collision (e.g. head-on or rear end); 6) primary collision factor (e.g. unsafe speed or unsafe lane change); 7) objects involved with collision (e.g. pedestrians or other motor vehicles); severity (e.g. injury or fatality); 8) weather condition (e.g. raining or cloudy); 9) roadway surface condition (e.g. wet or dry) and 10) traffic control devices (e.g. not present or not functioning). A sample collision report can be found on the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) website (ACTAR 2013). The following information presents the number and percentages of traffic accidents associated with each of the aforementioned ten pieces of information.

RESULTS

The following information reflects the magnitude of traffic accidents related to construction work activities and provide more information about their contributing factors.

Construction Zone Related Collisions Versus Total Collisions

To compare the total number of traffic accidents to those related to construction activities, all accidents reported between 1999 and 2009 were analyzed. This analysis revealed a total of 9,426 accidents, out of which, 204 happened in construction zones. The distribution of these 204 accidents over the 11 year study period is shown in Figure 1. As shown

Figure 1: Number of Collisions Vs. year



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in Figure 1, the two leading years with the most work zone related accidents were 1999 and 2008. While the reduction in the number of accidents that took place from 2000 to 2007 could be attributed to improvements in safety procedures, the increase in the number of accidents that took place in 2008 was mainly attributed to about 25% increase in number of public work construction projects, including those related to I-5 improvement plans.

Table 1 shows that construction zone related traffic collisions account for about 2% of all reported traffic collisions that occurred in the City of Buena Park within the 11 year time period. In view of the overall national statistics reported by Farmer et al. (2013), Snyder & Associates (2013), and National Work Zone Safety Information, along with the insistent need to minimize death and injury cases in road construction work zones, 2% it is too high to be ignored.

Table 1: Construction Zone Related Collisions Versus Total Collisions

Type of Accident Number of Accidents Percentage (%)

Construction zone related

204 2.0

Non-Constuction Related

9222 98.0

Extent of Injuries

The extent of injuries in construction work zones included four options listed on accident reports: 1) injury; 2) fatal; 3) property damage only and 4) none. Table 2 presents that property damage and injuries were equally reported to be 49%, each. While one case was

reported as fatal (about 0.5%) only, three cases were classified as none within the 11 years of data. It should be noted that a damage description to private property is noted on traffic collision reports, and the investigating officer is required to notify the owner. However, private property damages are settled financially between the responsible party and the private owner, and thus the city would not have any record of private property damage expenses.

Table 2: Severity of Accident in Construction Work Zones

Severity Percentage (%)

Injury 49.0

Fatal 0.5

Property Damage Only 49.0

None 1.5

Time InformationFigure 2 shows that most of the work zone traffic collisions occurred during the daytime period of 7:00 AM to 5:00 PM As depicted in Figure 2, there are four major spikes at 2:00 AM, 7:00 AM, 1:00 PM and 5:00 PM, respectively. To understand the causes of these spikes, the researchers had a second look at the accident reports. Although this further investigation revealed that the obvious increase in the number of work zone accidents at 2:00 AM was mainly due to driving under the influence (DUI), no obvious reasons were found to explain the spikes at 7:00 AM, 1:00 PM and 5:00 PM, except that there was an increase in traffic volume during rush hours. It should be noted that positive DUI laboratory results take up to 30 days to be reported and documented in the collision reports.


Figure 2: Time of Day Vs. Number of Collisions


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To validate this theory, a study of traffic volume was needed throughout the city of Buena Park. Due to the huge volume of data, the researchers considered a random sample of one week of data associated with Valley View Street. This street is one of the major arterials in the city of Buena Park and is associated with the highest average daily traffic (ADT) counts (i.e. 45,000). (City of Buena Park 2013). It should be noted that no information related to the number of construction projects that took place on this street and/or their associated number of traffic collisions were available to the researchers. Analysis of the traffic volume on this street is depicted in Figures 3 and 4. While Figure 3 focuses on weekdays, Figure 4 focuses on weekends. As shown in Figure 3, traffic peaks during weekdays are around 8:00 AM and near 6:00 PM with the majority of traffic accumulating between 7:00 AM and 9:00 PM Figure 4 shows that the highest traffic volumes are between 11:00 AM and 5:00 PM, which partially explains the spike at 1:00 PM in Figure 2. It should be noted that Figures 3 and 4 indicate that traffic volumes during weekdays and weekends are about the same (i.e. 16,000 vehicles per day). This could be attributed to the entertainment nature of Buena Park. It should be also noted that although this validation approach provided reasonable justification, other validation approaches that use construction work zone data need to be attempted.

Figure 3: Weekdays Example Traffic Data

Figure 4: Weekends Example Traffic Data

Lighting Information

Figure 5 reveals that the majority of work zone related traffic collisions (i.e. 75%) occurred in the daytime where daylight was the main source of lighting. The nighttime period had a relatively low crash rate (i.e. 22%). Because these statistics may indicate that motorists may have a tendency to drive more cautiously at night around work zone traffic control zones, further comparisons between traffic volumes during day and night times were performed using information depicted in Figures 3 and 4, respectively. This comparison revealed that nighttime traffic volume is about 7% of that associated with daytime (about 1,000 vph versus 15,000 vph). This comparison may suggest that accidents during nighttime should be about 7% of those occurring during daytime. Since information depicted in Figure 5 shows that collisions that occur during nighttime are about 30% of those occurring during daytime (22% versus 75%), this indicates that lighting conditions positively contribute to crash rates. It should be noted that more discussion about the impact of lighting condition on the number and type of collisions is presented in the following section titled Primary Collision Factors.

Figure 5: Lighting Condition vs. Number of Collisions



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Day of the Week Information

While Figures 3 and 4 indicate that traffic volumes during weekdays and weekends are about the same (16,000 vehicles per day), Figure 6 shows that the number of collisions during weekends is significantly lower than weekdays. This is partially attributed to the impact of work fatigue on drivers. Furthermore, Figure 6 shows that work zone traffic collisions increase gradually on Mondays, and then decline after Tuesday. By comparing Figures 3, 4 and 6, it is noticed that although Tuesday is associated with the highest number of accidents, it is not associated with the highest traffic volume.

Upon further efforts to explain the results shown in Figure 5, the city of Buena Park minimizes lane closures and prohibits construction work on weekends, thus explaining the low number of work zone traffic collisions. Furthermore, upon interviewing the Certified Public Infrastructure Inspector regarding possible explanations for the high occurrence on Tuesdays, public work inspectors tend to start construction projects on Mondays and do not begin lane closures until 9 am that day. Since initial lane closures do not begin until Mondays at 9 a.m., most people are at work, and accordingly, do not run into the traffic control for the first time until the next morning, this may lead to a combination of motorist confusion and frustration during their commutes on Tuesdays.


Figure 6: Days of the Week vs. Number of Collisions

Figure 7: Months of the Year vs. Number of Collisions


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Months of the Year Information

Figure 7 shows that in the busy Entertainment city of Buena Park, most of the work zone related traffic collisions tend to accrue during the summer months. About 34% of the crashes were reported in slow-construction seasons including November, December, January, February, and March. It can be speculated that summer months tend to attract drivers that are not familiar with the area, especially since the city of Buena Park attracts thousands of tourists every year to its Entertainment Zone. Tourists, who are usually new visitors to the area and are unfamiliar with the street network, may find themselves confused when traffic control and detours are present.

Collision Type

Figure 8 shows that the most frequently encountered types of collisions are rear-end collisions (34%), broadside/right-angle (27%), sideswipe collisions (20%) and hit objects (9%). The dominance of rear-end collisions indicates that relatively high speed and/or following too closely were contributing factors. The large number of right-angle collisions also suggests a high incidence of right-of-way violations or non-compliance for traffic signals and/or stop signs. Sideswipe collisions may be mostly associated with unsafe lane changes.

Primary Collision Factors

Primary collision factors could be generally attributed to drivers and non-drivers. Analysis of collected data revealed that collisions associated with these two groups are 98% and 2%, respectively (Figure 9). As shown in Figure 9, 98% of the work zone traffic collisions were associated with driver errors (i.e. unsafe starting/backing up, unsafe speed, following too closely, running red lights/stop signs, wrong side of the road, auto right-of-way, improper turning, unsafe lane changes, unsafe speed or DUIs). Among the most common human errors are unsafe speed for existing conditions (30%), automobile right-of-way (16%), improper turning (13%) and disregarded traffic signs/signals (11%).

In an effort to compare the primary collision factors during the daytime and nighttime periods, information depicted in Table 3 was prepared. As shown in this table, four major factors cause more nighttime collisions. These factors are DUIs, driving on the wrong side of the road, running red lights/stop signs and following too closely. Other factors, such as unsafe speed conditions, improper turning, auto right-of-way and unsafe lane changes, also cause more daytime collisions. According to the inspected reports, no nighttime accidents were attributed to unsafe starting/backing up and/or factors other than drivers. This indicates that 100% of nighttime accidents are attributed to human errors. While the explanation of the very high DUI nighttime incidents is obvious, more research is required to explain the


Figure 8: Collision Types

Figure 9: Primary Collision Factors


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difference between daytime and nighttime collision factors. It should be noted that although Figures 3 and 4 indicate that nighttime traffic volume tends to be much lower than that associated with daytime (about 1000 vph versus 16,000 vph), the results shown in Table 3 indicate that most identified collision factors cause more nighttime accidents. This observation is considered to be true when keeping in mind that nighttime traffic volume is about 7% only of daytime (Figures 3 and 4).

Involved with Other Objects

Figure 10 shows that the majority of collisions were involved with another motor vehicle (i.e. 85%). 11% of collisions involved fixed and other objects that may include animals, barriers, walls, and raised medians. While collisions with pedestrians and bicyclists contributed 3%, collisions with other vehicles contributed 4%.

Figure 10 depicts that 98% of work zone traffic collisions results in property damages and injuries. Furthermore, one fatality was reported between 1999 and 2009. It should be noted that property damage expenses were not reported,

Weather and Roadway Surface Conditions

Figures 11 and 12 show that most of work zone related traffic collisions (82%) occurred when the weather was

clear, and reveals no significant impact on work zone related traffic collisions. Furthermore, only 3% of the work zone traffic collisions occurred under inclement weather conditions, and less than 5% took place when the road surfaces were not dry. Therefore, it could be concluded that these weather and road condition related factors were not particularly significant in causing work zone traffic collisions.


Primary Collision FactorDaytime Nighttime

Contribution (%) Contribution (%)DUI 1 21Wrong Side of Road 3 4Ran Red Light/Stop Sign 8 20Following Too Close 1 4Unknown 3 6Unsafe Speed for Conditions 32 21Improper turning 16 4Auto Right-of-Way 17 12Unsafe Lane Change 8 4Other 5 4Unsafe Starting/Backing 3 0Other Than Driver 3 0

Table 3: Comparison of Collision Factors

Figure 10: Objects Involved in Collisions


55Accident Patterns in Road Construction Work Zones

Presence and Functionality of Traffic Control DevicesFigure 13 shows that 98% of traffic collision reports indicated that traffic control device was either functioning or not a factor in the collision. Only 2% of all work zone related traffic collisions experienced a non-presence or malfunction of traffic control devices. It should be noted that according to the Collision Investigation Manual, traffic control devices include traffic signals, regulatory, warning, and construction signs. Traffic control devices exclude striping and officers or other persons directing traffic.

Roadway Classification

Figure 14 shows that 98% of work zone traffic collisions occurred on principal (eight divided lanes), major (six divided lanes), primary (four divided lanes) and secondary (i.e. 4 undivided lanes) arterials, while 2% occurred on local residential streets (two undivided lanes). The maximum capacities of principal, major, primary, secondary arterials and local streets are 60,000, 45,000, 30,000, 20,000 and 4,000 vehicles per day, respectively.

Gender and Unlicensed Drivers

Figure 15 shows 427 drivers were involved in the 204 traffic accidents that took place in construction zones. The number of drivers involved in each accident ranged from one to five, who were classified as males, females and has not met the California State Age requirements yet. The percentages of these driver categories are 58%, 36% and 6%, respectively.

Figure 11: Effect of Weather Conditions Figure 13: Effect of Traffic Control Devices

Figure 14: Collisions vs. Roadway Classification

Figure 15: Collisions vs. Gender and Unlicensed Drivers

Figure 12: Effect of Road Conditions


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CONCLUSION

This paper focused on the analysis of accidents that occur in the road construction zones that did not have enough attention within the construction research community, and findings in this domain were described in recent publications as lacking and insufficient. It focused on collision factors and environments reported in California Highway Patrol (CHP) reports. The results of this study show that 98% of road work zone accidents result in injuries and property damages. About 0.5% of these types of accidents involved fatalities. Most of these accidents occur during daytime in summer months, and Tuesdays were associated with the highest number of accidents. Rear-end collisions were found to be the most common type of accidents (i.e. 34%), followed by right-angle (i.e. 27%) and sideswipes (i.e. 20%). 98% of accidents were attributed to human errors such as unsafe speed and improper turning. Comparisons between the primary collision factors between daytime and nighttime revealed that DUIs, diving on the wrong side of the road, running red lights/stop signs and following too closely cause more accidents during nighttime. About 6% of accidents involved unlicensed drivers, who have not met state age requirements yet. The study found that while 85% of the construction zone related traffic collisions involved vehicles only, 3% involved pedestrians and bicyclists. Unfavorable lighting/weather conditions and wet roads did not significantly influence work zone traffic collisions. 2% of the accidents were partially attributed to malfunction and/or the non-presence of traffic control devices. While most accidents (i.e. 98%) occurred on arterials within the city, very few (i.e. 2%) occurred on local streets. Although this paper highlighted many factors that contribute to and/or result from traffic collisions in road construction work zones, more research efforts are needed. Further studies could explore related information, from contractors’ perspectives, such as sections of work zones in which accidents occurred, compliance of contractors to the standard dimensions of work zone segments, type of work being done and

whether sudden changes in pavement elevations were existed before and/or after the accidents took place. It is also believed that minimizing traffic collisions in road construction zones is a shared responsibility between many involved parties, including contractors, owners and road users, and extensive research work is needed to ameliorate the knowledge in this field.

REFERENCES

Accreditation Commission for Traffic Accident Reconstruction (ACTAR), 2013. Traffic Collision Reports, Online Forms. (Accessed on November 2013, http://www.ACTAR.org)

Arditi, D., Lee, D. and Polat, G., 2007. Fatal Accidents in Nighttime vs. Daytime Highway Construction Zones, Journal of Safety Research 36, 399-405.

Bai, Y. and Li, Y., 2006., Final Report on Determining Major Causes of Highway Work Zone Accidents in Kansas, Kansas Department of Transportation, University of Kansas, K-TRAN:KU-05-1.

Bryden, J., Andrew, L. and Fortuniewicz, J., 1998. Work Zone Traffic Accidents Involving Traffic Control Devices, Safety Features, and Construction Operations, Transportation Research Records 1650, 71-81.

Bryden, J. and Andrew, L., 1999. Serious and Fatal Injuries to Workers on Highway Construction Projects, Transportation Research Records 1657, 42-47.

Bureau of Labor Statistics, 2013. Occupational Outlook Handbook. U.S. Bureau of Labor Statistics, Department of Labor, Washington, DC. (Accessed on November 2013, http://www.bls.gov).

City of Buena Park, 2013. 2008 Average Daily Traffic (ADT), ADT Volume Map. (Accessed on November 2013, http://www.buenapark.com)

Chi, S. and Han, S., 2013. Analyses of Systems Theory for Construction Accident Prevention with Specific Reference to OSHA Accident Reports. Journal of Project Management 31, 1027-1041.

Falkner, L., Schneider, J. and Arnold, J., 2012. Health and Safety, Prevention and Accident Costs in Construction Industry in International Comparison, Geomechanics and Tunnelling 5, 621-630.



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Haslam, R., Hide, S., Gibb, A., Gui, D., Pavitt, T., Atkinson, S. and Duff, A., 2005. Contributing Factors in Construction Accidents, Applied Economics 36, 401-415.

Kamper, W., Lum, H., Tgnor, S., 1984. Safety of Narrow Lanes for Traffic Control at a Construction Site, Public Roads 47(4), 119-124.

Kim, Y., Ryoo, B., Kim, Y. and Huh, C. 2013. Major Accident Factors for Effective Safety Management of Highway Construction, Journal of Construction Engineering and Management 129(6), 628-640.

Li, Y. and Bai, Y., 2008. Comparison of Characteristics Between Fatal and Injury Accidents in the Highway Construction Zones, Safety Science 46, 646-660.

Lubin, G. and Lincoln, K., 2011. The 15 Most Dangerous Jobs in America, Business Insider, New York. (Accessed on November 2013, http://www.businessinsider.com).

Mohan, S. and Gautam, P., 2002. Cost of Highway Work Zone Injuries, Practice Periodical on Structural Design and Construction 7(2), 68-73.

Qi, Y., Srinivasan, R., Teng, H. and Baker, R., 2005. Final Report on Frequency of Work Zone Accidents on Construction Projects, University Transportation Research Center, Region 2, City College of New York, 55657-03-15.

Solis-Carlos, R. and Arcudia-Abad, C., 2013. Construction-Related Accidents in Yucatan Peninsula, Mexico, Journal of Performance of Constructed Facilities 27(2), 156- 162.

U.S. Department of Transportation, 2009. Manual of Uniform Traffic Control Devices (MUCTD). Accessed on November 2013, http://www.mutcd.fhwa.dot.gov.


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Approximately 10-12 articles are published annually in The American Professional Constructor. To maintain our high standards of publication, AIC requires the support of competent and committed reviewers. We would like to express our deep gratitude to the following reviewers of the articles published in the Journal’s Spring and Fall 2015 Issues:

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