Expert Systems

EXPERT SYSTEMS AND PAVEMENT MANAGEMENT

HENDRICKSON, Chris T.

Associate Professor Department of Civil Engineering

Carnegie Mellon University Pittsburgh, Pennsylvania

r JANSON, Bruce N.

Assistant Professor Department of Civil Engineering

Carnegie Mellon University Pittsburgh, Pennsylvania

2nd North American Pavement Management Conference (1987)

TRB Committee AFD10 on Pavement Management Systems is providing the information contained herein for use by individual practitioners in state and local transportation agencies, researchers in academic institutions, and other members of the transportation research community. The information in this paper was taken directly from the submission of the author(s).

Expert Sydems a d Pavement Management

Chris T. endr ricks on^ and BNCO N. ans son*

Abstract

This paper is an broad assessment of potential expert system applications to the various needs and requirements of the pavement management process. First, we provide a Oeneral description of expert systems. Then, we focus on certain parts of the pavement management process for which expert system applications are being investigated andlor appear to be well suited. In addition, we discuss several potential applications of other related technologies, such as robotics and image processing, to pavement management tasks. One of our primary conclusions is that certain components of pavement management systems, such as diagnosing pavement deficiencies and selecting rehabilitation strategies, can be addressed quite successfully with expert system applications. On the other hand, computationally intensive procedures, such as input data processing and the execution of various pavement condition prediction models, are better suited to more conventional programming and database management approaches. However, a tremendous opportunity exists to integrate these data acquisition and manipulation routines with diagnostic and advisory expert systems.

---------- 1. Associate Professor, Dept. of Civil Engineering, Camegie Mellon University, Pittsburgh, PA. 15213

2. M i a n t Professor, Dept. of Civil Engineering, Camegie Melbn University, Pittsburgh, PA. 15213



i lntroductlon

This paper is an broad assessment and overview of potential expert system applications to various aspects of the pavement management process. It also considers the potential of related developments in artificial intelligence and robotics. Among the many ways in which computers are being integrated into civil engineering practice, expert systems and related technologies constitute some of the most promising areas of research and development effort today. However, some areas of civil engineering are more appropriate than others for the application of these tools.

Transportation engineering encompasses a wide spectrum of tasks required in the planning, design, construction and management of facilities and services, including roadways, bridges, ports, rail lines, watetways and transit services. Computers are an essential tool for the profession, but they have only been applied to limited aspects of the total process. In practice, tasks such as costs estimation, accounting, survey computations, operations management and project scheduling are all performed using computer based tools on a regular basis. in fact, some of these tasks are computationally intractable without the aid of computer based tools. However, there exist numerous interesting, abstract and "difficult" problem solving tasks that have not been computerized or where the available computer tools do little to address the intelligent aspects of the problem solving process. Knowledge-based expert systems provide a means of extending the use of computers to a broader range of transportation engineering applications.

Pavement management systems have undergone extensive research, development, implementation and testing over the past two decades, as indicated by the number of papers, reports and books published during that time. A recent bibliography of over 200 references published since 1967 [31] includes amng others [2, 7, 8, 14, 16, 30, 34, 35, 38, 39, 40, 47, 54, 551. Pavement management systems developed by these efforts have provided state and bcal highway agencies with information management and decision support tools at both the project and network levels. Moreover, these systems have become more sophisticated, comprehensive, and responsive to users' needs as computer systems and related technology have evolved. In particular, the recent growth in use of relational databases, cost estimating systems, project planning and management software, and local area networks has greatly expanded the degree to which pavement management systems can be interfaced and coordinated with other agency functions.

The primary uses of pavement management systems are to (1) store and maintain records of pavement condition data acquired by various manual and automated data collection techniques, (2) evaluate pavement conditions and compute serviceability measures using condition rating and projection models, (3) determine alternative maintenance or rehabilitation strategies for given roadway sections based on the structural and material deficiencies found, (4) recommend "best" or "most cost effective" strategies after evaluating the costs and benefits of each, and (5) program action strategies for roadway sections or classes of roads considering system performance goals and budget allocations over a given planning horizon.

The result of implementing and maintaining a complete pavement management system can be the automated generation of year-to-year maintenance management plans, plus a set of systemwide performance projections and budget requirements needed to carry out those plans. In addiiion, simulations of altemative management scenarios may predii the impacts of diierent funding restrictions or increases. The ultimate objective is a more cost effective mdnagement of resources needed to sustain and improve the overall condition and safety of the roadway system. However, pavement management systems have numerous components, and certain recent enhancements, such as automated data acquisition and image recognition, simply could not be achieved without today's advanced sensory technology.

Expert systems are now recognized as a significant breakthrough in software technology. Expert systems involve information processing and problem solving strategies that are quite different from those of conventional computer programs. These differences will dramatically change the devebpment and use of many new decision support systems - from how and what types of data are collected, to what types of information are expected from these systems and the user skills required to effectively elicit this information. Since expert systems are relatively new and unfamiliar to many persons involved in the development of decision support systems for engineering, planning and management problems,



the appropriate applications of expert system to pavement management have yet to be bt ahhaugh a few initial assessments have been made [19.52,61]. e-

EvoIutionS in computer hardware and software will continue to spur new developmeas ,,, n, design of pavement management system, and new technobgies such as expea system sunikant hopes for improving both the use and effectiveness of pavement management smsm ~xpert systems can bring expertise to bear on problems h ways that are diff i ih or lmposrb I& conventional programs to achieve. The explanation faciliiies in expert systems can also be ured to make the operation of pavement maMpern0ll system more i n s t ~ d i ~ e . Automated data ~cq,,g&,, a d robotics linked to pavement management systems might also bring about signifiinl redmiom pavement costs over the long run. As new technologies evolve, It Is important to insure that pavement management systems are poised to take advantage of their potential integration. Flexibility in the design of pavement management systems is essential in this regard.

2 A Brief Ovewlew of Expert Systems

Knowledge-based expert systems are an alternative to more traditional types of computer program development and application in engineering. The development of a knowledge-based expert system (KBES) is actually the application of artificial intelligence (Al) techniques designed to reach the performance level of a human expert in a particular field. Generally, the scope of an expert system application must be very well defined so that domain specific knowledge on which rules within the system are based is applicable to the particular types of problems for which the system is intended. However, within a domain specific application area, a major advantage of an expert system in comparison to other more algorithmic programming approaches is that problems given to an expert system can be less well SffUctVfed. The exact description of each problem can vary, both in the amount and reliability of data, since an expert system can be designed to fire rules, make deductions, and reach conclusions based on the available data and the confidence that a user places in that intornation.

Expert systems a n applicable to a wide range of problem solving tasks. For examgle, problems in construction engineering, planning and management for which expert systems (of varying degrees of sophistication) have already been developed include project feasibility evaluation and site investigation [6, 36, 3Tj, construction project planning and scheduling [23, 24, 451, and claims analysis and risk evaluation [5, 9, 331. The current development of automated vehicles and construction robots will lead to additional problem areas for which expert system approaches will be appropriate, such as sensory interpretation, equipment diagnoses, and operation monitoring and control (1 1, 13, 28, 48, SO]. The remainder of Section 2 is based largely on an earlier report [22], and can be skipped by readers familiar with the methods and terminology of expert system.

2.1 Expert Systems versus Algorithmic Programming

The principal distinction between expert systems and algorithmic programs lies in the use of knowledge. A tradiiional algorithmic application is organized into data and program. An expert system Separates the program into an explicit howiedge base describing the problem solving strategy and a control program or inference engine that manipulates the knowledge base. The data portion or antext describes the problem being solved and the current state of the solution process. Such an approach is referred to as knowledge-based.

In order to place expert systems in perspedive, the nature and limitations of conventional computer Programs must first be examined. Any computer program can be viewed as consisting of a recipe of mies that completely specifies the problem solving sequence as illustrated below.

IF condi t ionl THENactionl IF condi t ionZ THEN act ion2

IF cond i t ioni THEN a c t ioni

IF condi t ion , THEN action,

Each rule contains a premise or condition to be evaluated, and the action invoked by one rule becomes the premise of another. During execution, if the oondiion of a rule is found to be true, then the



corresponding adion is performed, and execution continues with the next rule. ll a condition k found to be false, then the action is skipped. Conventional computer programs are developed by explicitly stating all applicable rules and their precise sequence of executbn.

Knowledge-based expert systems are designed to overcome the shortcomings of algorithmic computer applications. They may be based on the same types of premise-action rules as algorithmic programs, but the exact sequence of selecting and applying those rules is not specified prior to solving the problem. The rules are represented symbolically and used by the knowledge processing component of the expert system to invoke actions based on the rules that apply to a particular situation. Expert systems have additional capabilities for selecting rules based on incomplete or uncertain data, and for explaining why rules are selected and how they are used.

Only a person that is knowledgeable in the domain of a given program, i.e. a domain expert, can define the applicable conditions and corresponding actions. This is particularly true in any practical engineering program, where a very large proportion of the rules are not necessarily based on the causality of physical laws, but represent heuristics -- assumptions, limitations, rules of thumb or style -- of the expert(~). Developing a complete set of rules for any engineering application is a major undertaking. The ~ l e s must satisfy the following criteria:

- Completeness. The rules must provide an action for eve possible combination of conditions. X Even though some conditions may be "second nature" to t e programmer or user, failure of the system to consider all possible conditions will result in a program that will not perform in an acceptable manner over a complete range of potential cases.

- Uni ueness. The rules must provide one and only one unique outcome for each possible com ‘I, ination of conditions. While this requirement may be difficult to achieve, failure to achieve it results in a flawed program.

- Correctness. The rules must provide a correct outcome for all possible conditions. The rules and actions must be properly specified, sequenced and associated with each other. Otherwise, misinterpretation problems can occur.

Applications based on mathematical models and those requiring intense numerical computations may be conveniently and effectively built as algorithmic programs. However many problem solving situations, such as interpretation or design, are too ill-structured andlor illdefined, and are less well suited to the rigid algorithmic format [56]. Numerous engineering tasks fall into this category, and the potential applications of expert systems to such tasks are discussed in the following sections.

2.2 Expert System Architectures

A variety of expert system architectures exist. Various domain independent systems have different inference procedures and knowledge representation schemes, including (1) production systems with IF- THEN rules as illustrated above [3], (2) semantic inference networks (491, and (3) frame representations 1601. More complex blackboard systems, which are based on multiple experts operating at different levels of abstraction, have also been buift [lo].

The component parts of any expert system application are defined below. One point to emphasize is that only the knowledge base is domain specific. All the other components are parts of a general purpose expert system building framework applicable to other application domains.

- Knowledge Base. The knowledge base is the repository for all knowledge that is used by the system in solving problems. This information is essentially of two basrc types and representations: 1) causal or factual knowledge of the application domain, a$ (2) empirical associations or rules. \ his information is organized within the knowledge base m a manner that can be effectively

utilized by other components of the system.

- Context. The context contains the information that describes the problem wrrentl being solved,

g r including both roblem data and solution status. The problem data can also be o two types: (1) facts provided y the user, and (2) facts derive or inferred by the program. The use of an expert system program begins with the user entenng some known facts about the problem into the context.



- inference E ine. The Inference e ine Is the knowledge processor. It Y 7' on cony utllizjnp the nowledge base to de uce qew facts about the problem, w c h are then m~ wkequea inferences. The basic operaton of a fonvanl chsnfq ~nference engine is &inla kop that performs three steps:

1. earnine the premises of all rules in the know e base and determine which of these are "% ~ ~ e r r t l y hve based on the current values of pro lem data contained in the antext. step, performed by the change monitor or panem matcher, yields a set of candidate mles.

2. Select one of the candidate rules. This rule is chosen by the scheduler or m a r .

3. Invoke or fire the corresponding action of this rule, which will change some data vahes within the context. The context is updated by the knowledge modifier.

The objective of the inference engine is to arrive at a global conckrsion (i.e. the pal). Thii mle firin process continues until the problem is solved and the context is transformed lnto the desired goa ? state, or until there are no more rules to be fired.

- Explanation Module. The explanation module provides the expert system with-the capability of explaining its reasoning and problem solving strate y to the user. At any point, the user can

n8 interrupt the system to inquire what it is doing a see why it is pursuing its current line of reasoning. In addition, the program can explarn how any particular fact was deduced an how knowledge was applied.

- Knowledge Acquisition Module. Information in the knowledge base is in a wid format, and the translation of knowledge obtained from experts into the required internal format may be tedious. The knowledge acquisition module aids in this task. Although it is desired that human experts eventually be able to enter knowledge directly into the system, this goal is currently not achieved.

- User Interface. The user accesses the system through a friendly interface, often using a problem oriented subset of natural language queries, menudriven selections or pictoral icons. The interface enables the user to monitor the erformance of the system, volunteer information,

expert system. P request explanations, or to redirect the prob em solving line of reasoning being pursued by the

2.3 Scope of Expert System Applications

system applications are appearing in many disciplines. However, not all tasks are amenable to expert system formulation. The following is a partial list of criteria for the evaluation of promising potential applications [21]:

- There are recognized experts in the field whose performance is better than that of novices.

- The factual component of domain knowl e is routinely taught to neophytes who became experts "P by developing their own rules and ernpirica associations.

- Typical tasks are performed by an expert in a few minutes to several hours.

- Tasks are primarily cognitive, requiring reasoning at multiple levels of abstraction.

- Algorithmic solutions are either impractical or result in overly constrained or specialized programs.

- There are substantial benefits in apptying the expert knowledge to each occurrence of the task.

The range of potential expert system applications spans a wide spectrum, from detivation or interpretative problems to fomtion or generative problems [I]. In derivation problems, the problem conditions and description are given as part of a solution description or goal. The expert system completes the solution description by applying the available knowledge and rules such that the initial data and conditions are well integrated in the solution. As an example, in a derivation problem such as theorem Proving, a solution hypothesis is formulated which the expert system attempts to ptave .by applying rules to the known data. Repeated application of rules transforms the problem statement (1.0- the initial state) into the solution state.



In formation problems, conditions or wnstraints are given in the form of properties that the solution must satisfy. Candidate solutions are generated and tested against the specified constraints. Two subclasses exist: (1) constraint satisfaction in which the solution need only satisfy the governing constraints, and (2) optimization where an attempt is made to find an optimal solution. The design of a plan, object, or system fits this paradigm. Most problems are not pure formation or derivation problems, but lie somewhere in between and require that techniques from both categories be used in solving them.

The following list of problem types illustrates the spectrum of expert system applications.

- Interpretation. An interpretation system infers a problem's state on the basis of observed data. One example is Dipmeter Advisor, a system for interpreting geophysical oil well k g data 1571.

- Prediction. A prediiion system infers likely consequences on the basis of initial conditions. An exam le is MASON, a system for estimating mansonry construction activiiy durations and identi t' ying potential problems [23].

- Diagnosis and Debugging. A diagnostic systems infers the type or cause of a matfunction or system failure from observed irregularities and other known data about the system's state. An expert system for diagnosing retaining wall failures is one example (41.

- Monitoring. A monitor observes system behavior and compares the observations to the planned behavior to determine flaws in the Ian or potential malfunctions of the system. An example is MOVER, an expert system design id' to mon~tor the operation of a people-mover [12).

- Design. Design is the process of developing a confi uration for an object which satisfies all applicable constraints. HI-RISE, a design system for tall !u ildings, is one example [44j.

- Planning. ~ l a n n i d is a design rocess that yields a set a actions intended to produce a desired EP outcome. One example is PLA EX, an expert system for planning construction projects 124).

- Repair. Repair systems plan remedies for system malfunctions, failures or deficiencies found through diagnoses (for example, see 151,531).

- Control. A control system also possesses man of the same characteristics held by the other applications described above. It must interpret d ata, predii outcomes, fomulate plans, execute the plans, and monitor their execution (for example, see [13)).

Interpretation, prediiion, monitoring and diagnostic applications lie at the derivation end of the spectrum while design, planning, and control lie at the formation end.

3 Some Potential Applications to Pavement Management

Descriptions of expert system applications to civil engineering problems in general appear in (36, 37, 43, 50, 581. References that assess or describe applications to problems of transportation engineering, planning and management include (15, 20, 25, 61, 62). Publications that more specifically focus on expert systems for pavement management include [19,51,52,53]. Bebw we provide a partial view of where expert systems and related technologies fit into the pavement management process. Many other potential applications exist.

3.1 Automated Image Processing and Pavement Deficiency Recognition

One basic task of a pavement management system is to assess a pavement's condition based on visual and test data. Automation of data input procedures would be exceptionally helpful in providing current, consistent and comprehensive information. Machine vision and video image processing have bng been active areas of research [42] and are likely to become widely applied took for road surface condition assessment. Vision is an information processing task in which arrays of brightness values received by a camera or other type of sensor are manipulated to form a two or three dimensional model of a scene. This process may involve inferring the types of objects or material characteristics present and their other attributes.

For reasons of efficiency, image processing technology relies mainly on algorithmic techniques, while expert systems can be used to interpret the completed scene or image. Several protoype systems



have already appeared [In. Other sensing requirements Can also be automated such as locating a* placing reinforcement bars in concrete pavement during construction (41, 46). In the future autonomous or setfgulded vehicles might be developed to perform condiion assessment tasks. such ;

could be designed to travel at a slow uniform speed along the shoulder of a highway for a lonq continuous period of time, thus eliminating the need for drivers to perform such tasks.

,

3.2 Pavement Condition Evaluation and Failure Diagnoses

Evaluating the condiion of a roadway surface section and diagnosing the causes of Ws deficiencies is based very much on site specific characteristics and cannot necessarily be achieved by a categorical approach. Inferences based on site specific condition attributes are needed to interpmt the pavement's condition, diagnose Its problems, consider appropriate maintenance or rehabilitation strategies, and achieve an "intelligent" design that combines both the knowledge of standard material and cost relationships with the Mica1 perception of special cirwmtances. Several systems are currently being developed to fill this need (19, 18, 51, 531. Since knowledgeable and skilled engineers in the field of pavement management and design come only at a premium at the local level, an expert system such as this can partially provide the "expertise" of larger design offices to smaller city or county agencies.

Beyond the direct issue of pavement management per se, equipment is becoming more complex and harder to maintain. Robotic construction, maintenance and inspection equipment as well as automated guideways and vehicles will present even more complex diagnosis and repair problems. Expert system diagnosticians can keep track of the equipment's internal conditions by diagnosing its operating state, make "connections' between spatially and temporarily dispersed signals and events, e.g. distinguish between transient and hard failures, and revise their operational expectations based on previous history. Such systems can act as expert mechanics or mechanic's assistants in suggesting possible fault causes or repair and preventive maintenance procedures based on operational behavior and failure symptom. One prototype diagnostic system for an airport people-mover is MOVER [12], and the Mitre Corporation [59] has proposed a diagnostic assistant for bus maintenance.

Finally, expert systems to assess the safety and serviceability of various classes of structures (bridges, pavements, dams, etc.) could be designed to evaluate their integrity (bad carrying capacity, potential Sie, functional suitability, etc.), identify likely causes of deficiencies, and provide recommendations for remedial actions. Such systems would be particularly valuable in repair, reconstruction and restoration (3R) work, where detailed plans are often not available and where many possible modes of deterioration must be considered. An example of such an application is SPERIL, an expert system for the assessment of seismic damage to buildings [29]. Linking a roadway pavement diagnostic system to automated sensors might be particularly rewarding.

3.3 Selection of Maintenance and Rehabilitation Strategies

The evaluation of a pavement's surface can provide needed information with which to judge the current serviceability of a pavement section, deduce probable causes of distress, determine the need for further evaluation, and to make preliminary recommendations of rehabilitation strategies. The selection and scheduling of routine pavement maintenance activities to the diversity of roadway section types, conditions, traffic characteristics and geographical locations are repeated tasks in any highway agency that can benefit from the rule-based logic of an expert system, since these assignments are not made on the basis exact engineering criteria. One expert system called ROSE 1191 has been developed to facilitate the selection of routing and sealing activities for asphalt concrete pavements in cold areas. The system recommendations include whether muting and sealing are appropriate treatments for a given roadway section, and to what degree such treatments shouid be administered. The prototype system was developed with a commercial expert system development package, and then converted into a more conventional production level system in Fortran.

Pavement management systems are not usually intended to serve as project specific design tools WI. Pavement management systems are typically used to develop approximate estimates of budget and work force needs as input to the fiscal year planning process. The resource estimates provided by a .Pavement management system may be based on the assumption that certain actions will be taken with respect to certain types of roads in various andtion categories, but the design of a work order or contract for a part i~lar road section is not a part of the overall pavement management process. A basic reason for this lack of specific strategy design is that the design process has eluded the application of



traditional programming approaches because of Its site specific nature. Computationally intensive algorithmic and statistical models used to estimate overall resource requirements based on system-wide conditions are well suited to traditional programming environments. Hence, there is a role for both expert systems and mnventional programs in this area.

3.4 Construction Management: Planning, Monitoring and Control

Construction planning, scheduling and estimating are examples of tasks that are computerized at the micro level, but which require expertise at a macro or strategic level. Once a quantity take-off has been prepared, the estimation of total cost is an algorithmic task. However, the mmplete process of construction planning also involves task scheduling and resource allocation, which must be completed before full projects costs are known. The process of allocating equipment and crews, sequencing construction tasks and determining quantity take-offs for the cost estimate are planning tasks that require expertise. Expert systems such as PLANEX 1241 can be developed for these applications.

3.5 Robotic Equipment Control and Automated Maintenance

The development of robotics for pavement management has been relatively slow, despite the fact that highly mechanized types of paving equipment are widely used. Increased mechanization and automation of tasks, such as pothole patching, surface preparation and inspection using smart autonomous vehicles can be expected. For this use, an autonomous vehicle must be capable of sensing both the external environment and its internal state, plan an action based on that state, and act according to the plan. To the extent that such environments are not controlled or malfunctions may occur, expert systems can be developed to both plan and invoke appropriate vehicle actions [27].

4 Concluding Remarks

In this paper, we provided a brief description and outline of where we think expert systems can be applied effectively to the pavement management process. 01 course, with several large pavement management systems having already been implemented, many of the existing programming environments m y be inappropriate for the immediate implementation of expert system technology. However, certain characteristics of prototype expert system applications in pavement 'management might be transferable to these programming environments even at the present time.

A basic premise that we emphasized throughout this paper is that certain aspects of the pvement management process, such as diagnosing pavement deficiencies and selecting rehabilitation strategies, can be addressed quite successfully with expert systems. Other more cornputationally intensive procedures, such as input data processing and the execution of various pavement condition prediction models, are better suited to more conventional approaches. However, a tremendous opportunity exists to improve the interfacing of data acquisition and manipulation routines with diagnostic and advisory expert systems [4,26,32]. The management of PMS databases and the development of user interfaces using natural language communication will require considerable use of AllKBES techniques.

References

[ I ] Amarel S. (1 978). Basic Themes and Problems in Current Al Research. Proceedings, Fourth Annual AIM Workshop, pp. 28-46. Ceilsielske V.B., Ed. Rutgers University. New Bwnsw'd, NJ.

[Z ] Anderson D.R. (1984). Maintenance Management Systems. NCHRP Synthesis of Highway Practice No. 11 0. Transportation Research Board. Washington, D.C.

[3] Brownston L., Farrell R., Kant E. and Martin N. (1985). Programming Expert Systems in OPS5: An Introduction to Rule-Based Programming. Addison-Wesley Publishing Co. Reading, MA.

[4] Chahine J.R. and Janson B.N. (1987). Interfacing Databases with Expert Systems: A Retaining Wall Management Application. Microcomputers in Civil Engineering 2(1):19-38.

(51 Cobb J.E. and Diekmann J.E. (1986). A Claims Analysis Expert System. Project Management Journal 18(3) :39-48.



161 Corn\& T.C., Bull S.P., Nowell M.C.C. and Coles E.J., Editors (1986). Expert Systems &the Constmtion Industry. Proceedings of the ESCAD '86 Workshop. Berkshire, England.

[q Dahir S.H. and Gramling W.L. (1986). Impact of a Comprehensive Pavement Management smem Developed in Pennsylvania. Transportation Research Record 1060:45-52.

181 Darter M.I., Smith R.E. and Uzarski D.R. (1984). Pavement Mana~ement Systems for U M n ire= In Innovative Strategies to lnlprove Urban Transportation Performance, pp. 179-204. American

'

Society of Civil Engineers. New Yotic, NY.

[g] Diekmann, J.E. and Kruppenbacher T.A. (1984). Claims Analysis and Computer Reasoning. of Construction Engineering and ManagemenVASCE I 1 O(4) :39 1 -406.

[ lo] Erman L.D., Hayes-Roth F., Lesser V.R. and Reddy D.R. (1980). The Hearsay-I1 Speech Understanding System: Integrating Knowledge to Resolve Uncertainty. Computing Surveys 12(2):213:253.

[ t l ] Fenves S.J. and Rehak D.R. (1984). Role of Expert Systems in Construction Robotics. Proc8edings, First Conference on Robotics in Constnrdion. Camegie Mellon University. Pittsburgh, PA.

[ I 21 Fenves S.J., Maher M.L., Rehak D.R., Rychener M.D., Sriram D. and (1984). PDS'MOVER: Development of a PDS Expert System Prototype for Peoplemover Operation and Maintenance. Technical Report. Department of Civil Engineering and the Robotics Institute, Camegie Mellon University. Pittsburgh, PA.

113) Fenves S.J., Baker N. and Balash J. (1985). Expert Systems for Planning Robotic Excavation. Proceedings, Second Conference on Robotics in Conshrdion. Carnegie Mellon University. Pittsburgh, PA.

(141 Golabi K., Kulkarni R.B. and Way (2.6. (1982). A Statewide Pavement Management System. Intedaces 12(6) 5-21 .

[ I 51 Gosling G.D. (1 987). Application of Expert Systems in Air Traffic Control. Joumal of Transportation Engineering/ASCE 11 3(2) :139-154.

1161 Haas R.C.G. and Hudson W.R. (1982). Pavement Management Systems. Krieger Publishing Co. Melbourne, FL.

[ I 7] Haas C., Shen H., Phang W.A. and Haas R.C.G. (1 984). Application of Image Analysis Technology to Automation of Pavement Condition Sunreys. Presented to Roads and Transportation Association of Canada, Montreal.

[ I 81 Haas C. (1 986). PRESERVER: A Pavement Maintenance Consultant. Project Report. Department of Civil Engineering, Carnegie Mellon University. Pittsburgh, PA.

[ I 91 Hajek J.J., Chong G.J., Haas R.C.G. and Phang W.A. (1 987). Can Knowledge-Based Expert System Technology Benefit Pavement Maintenance? Report PAV-86-05. Research and Development Branch, Ontario Ministry of Transportation and Communications. Ontario, Canada.

1201 Harris R.A., Cohn L.F. and Bowlby W. (1987). Designing Noise Bamers Using Expert System CHINA. Journal of Transportation Engineerinp'ASCE 1 13(2) :127-138.

[21] Hayes-Roth F., Waterrnan D. and Lenat D. (1983). BuiMng Expert Systems. Addison-Wesley Publishing Co. Reading, MA.

[221 Hendrickson C.T.. Rehak D.R. and Fenves S.J. (1982). Expert Systems in Transportation System Engineering. Working Paper. Department of Civil Engineering, Camegie Melbn University. Pittsburgh, PA.

1231 Hendrkkson C.T.. Martinelli D. and Rehak D.R. (1987). Hierarchial Rule-Based Actih*y Duration Estimation. Journal of Construction Engineering and ManagemenVASCE 1 13(2) 288-301.



rer E .

1241 Hendridtson C.T., Zozaya-Gorostiza C., Rehak D.R., Baracco-Miller E. and Llm P. (1987). An ~xpert System for Construction Planning. Journal of Corrputino/ASCE, (in press).

[25] Hendrickson C.T., Zozaya-Gorostiza C. and McNeil S. (1987). A Knowledge Based Expert System Architecture for Computer Aided Analysis and Design of Intersections. Proceedings, Tenfh International Symposium on Transportation and Traffic Theory. Cambridge, MA.

[26] Howard H.C. (1986). Intedacing Databases and Knowledge Based System for Stnrdural Engineering Applications. Ph.D. Dissertation. Department of Civil Engineering, Camegie Melbn University. Piburgh, PA.

[27l Hunt V.D. (1985). Smart Robots - A Handbook of Intelligent Robotic Systems. Chapman and Hall. New York, NY.

(281 lbbs W. (1986). Future Directions for Computerized Construction Research. Journal of Construction Engineering and ManagemenVASCE 112(3):326-345.

[29] lshizuka M., Fu K.S. and Yao J.T.P. (1 981). SPERIL-I: Computer Based Stnrtural Damage Assessment System. Technical Report CE-STR-81-36. Department of Civil Engineering, Purdue University. Lafayette, IN.

1301 Jackson N.C. (1985). Operation of the Washington State Pavement Management System. Transportation Research Record 1048:23-28.

[3 11 Janson B .N. (1 987). Pavement Management, Priority Assessment and Project Programming: Bibliography of Publications Dated 1967- 1987. Department of Civil Engineering, Carnegie Mellon University. Pittsburgh, PA.

r (321 Jarke M. and Vassiliou Y. (1984). Databases and Expert Systems: Opportunities and Architectures

for Integration. In New Applications of Data Bases, pp. 185-201. Academic Press. London, England.

[33] Kangari R. (1 987). Expert Systems for Risk Analysis. Civil Engineering/ASCE n ( 6 ) :78-79.

[34] Karan M.A., Christison T.J., Cheetham A. and Berdahl G. (1983). Devebpment and Implementation of Alberta's Pavement Information and Needs System (PINS). Transportation Research Record 93811 1-20.

[35] Karan M.A., Longenecker K., Stanley A. and Haas R.C.G. (1983). lmplementation of Idaho's Pavement Management System. Transportation Research Record 938:43-53.

[36] Kim S.S.. ~ a h e r M.L.. Levitt R.E., Rmney M.F., Siller T.F. and Ritchie S.G. (1 986). Survey of the State-of-the-Art ExpertKnowledge Based Systems in Civil Engineering. USA-CERL Special Report P-87101. Construction Engineering Research Laboratory. Champaign, IL.

[37l Kostem C.N. and Maher M.L., Editors (1 986). Expert Systems in Civil Engineering. American Society of Civil Engineers. New York, NY.

[38] Kulkami R., Finn F.H., LeClerc R. and Sandahl H. (1976). Development of a Pavement Management System. Transportation Research Remrd 602:117-121.

(391 Kulkami R.B. (1984). Dynamic Decision Model for a Pavement Management System. Transportation Research Record 997: 1 1 - 1 8.

[40] Lu D.Y. and Lytton R.L. (1976). Strategic Planning for Pavement Rehabilitation and Maintenance Management Systems. Transportation Research Record 59829-35.

[41] Maser K.R. (1986). Applications of Automated Interpretation to Sensor Data. In Expert System in Civil Engineering, pp. 224-238. American Society of Civil Engineers. New York, NY.

[42] Machine Vision AssociationlSME (1985). Wsion '85 Conference Proceedings. Society of Manufacturing Engineers. Dearborn, Michigan.



[43] Maher M.L., Sriram D. and Fenves S.J. (1984). Tools and Techniques for Knowledge Based Expen Systems for Engineering Design. Advances in Software Engineering 6(4): 1 78- 188.

[44] Maher M.L. (1 984). Hi-Rise: A Knowledge-Based Expert System for Preliminary Strvctural Design of High Rise Buildings. Ph.0. Dissertation. Department of Civil Engineering, Carnegie Mellon University. Pittsburgh, PA.

1451 McGartland M. and Hendrickson C.T. (1985). Expert Systems for Construction Project Monitoring. Journal of Construction Engineeting and ManagemenWASCE 1 1 1(3) :293-307.

1461 Motazed B. (1984). Expert Imaging of Magnetic Objects. Ph.D. Dissertation. Department of Civil Engineering, Camegie Mellon University. Pittsburgh, PA.

[47] Nelson T.L. and LeClerc R.V. (1 982). Development and Implementation of Washington State & Pavement Management System. Report WA-RD50.1. Washington State Department of Transportation. Olympia, WA.

(481 Paulson B.C.,Jr. (1985). Automation and Robotics for Construction. Journal of ~onstnrdion Engineering and ManagemenWASCE 11 1(3) :190-207.

1491 Reboh R. (1981). Knowledge Engineering Tools and Techniques in the Prospector Environment. Project Report 81 72. SRI International. Palo Alto, CA.

(501 Rehak D.R. and Fenves S.J. (1985). Expert Systems in Civil Engineering, Construction Management and Construction Robotics. In the 1984 Annual Research Reviewof the Robotics Institute, Carnegie Mellon University. Pittsburgh, PA.

[51] Ritchie S.G., Yeh C., Mahoney J.P. and Jackson N.C. (1986). Development of an Expert System for Pavement Rehabilitation Decision Making. Transportation Research Rewrd 107096-1 03.

[52) Ritchie S.G. (1987). Expert Systems in Pavement Management. Transportation Research 21A(2):145-152.

(531 Ritchie S.G., Yeh C., Mahoney J.P. and Jackson N.C. (1987). Surface Condition Expert System for Pavement Rehabilitation Planning. Joumal of Transportation EngineenngLASCE 113(2):155-167.

[54] Shahin M.Y. and Rozanski F.M. (1978). Development of a Computerized System for Pavement Maintenance Management. Transportation Research Record 674:3-11.

[55] Shahin M.Y. (1980). Components of a Pavement Maintenance Management System. Tr-rtation Research Record 781 :31-39.

[56] Simon H.A. (1981). The Sciences of the Artificial (Second Edition). MIT Press. Cambridge, MA.

157) Smith R.G. and Baker J.D. (1983). The Dipmeter Advisor: A Case Study in Commercial Expert System Development. Proceedings, Eighth international Joint Conference on Artificial Intelligence, pp. 122-1 29. Karlsruhe, West Germany.

1581 Sriram D. and Adey R., Editors (1 986). Applications of Artificial Intelligence in Engineering Problems. Springer-Verlag. New York, NY.

[59] Wood P. (1985). A Knowledge-Based System foi~ransil Bus Maintenance. Technical Report MP-84W15. Mitre Corporation.

[60] Wright, M. and Fox M. (1983). SRL: Schema Representation Language. Technical Report. Robotics Institute, Camegie Melbn University. Pittsburgh, PA.

[611 Yeh C., Ritchie S.G. and Schneider J.B. (1986) Potential Applications of Knowledge-Based Expelt Systems in Transportation Planning and Engineering. Transportation Research Record 1076:59-65-

I621 Zozaya-Gorostiza C. and Hendrickson C.T. (1987). Expert System for Traffic Signal Setting Assistance. Journal of Transportation EngineeringlASCE 1 13(2):108-126.



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