Evaluating the system intelligence of the intelligent building systems: Part 1: Development of key...

17 (2008) 284–302www.elsevier.com/locate/autcon

Automation in Construction

Evaluating the system intelligence of the intelligent building systemsPart 1: Development of key intelligent indicators

and conceptual analytical framework

Johnny Wong a,⁎, Heng Li a, Jenkin Lai b

a Department of Building and Real Estate, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kongb Energy Management Manager, Facilities Management, Pacific Century Premium Developments Ltd., Hong Kong

Accepted 13 June 2007

Abstract

The rapid development of microprocessor-based technologies and the increasingly sophisticated demands for high performance workingenvironments have prompted an increasing number of developers to consider adding ‘intelligence’ to their new buildings in order to improve thebuildings' operational effectiveness and efficiency to enhance marketability. However, the lack of satisfactory consensus for characterizing thesystem intelligence and structured analytical decision models, inhibit the developers and practitioners to understand and configure optimumintelligent building systems in a fully informed manner. Little research has been conducted towards aiding in decisions and appraisal of thebuilding systems and components in the intelligent building. This paper (Part 1 of a two-part research project) aims to identify the key intelligentindicators, and map the analytical decision models for the system intelligence appraisal of the intelligent building systems. A total of 69 keyintelligent indicators were identified for eight major intelligent building systems. The development of system intelligence analytical models will bedescribed in Part 2 of the research. The analytic network process (ANP), a systemic analytical approach, is proposed to prioritize the intelligentindicators and develop the model for computing the system intelligent score (SIS) — a measurement of the system intelligence of the intelligentbuilding systems. ANP further enables the decision-makers to take the interdependent relationships between the intelligent attributes and thebuilding's operational goals/benefits into consideration. Their applicability will be also validated and demonstrated using a real intelligent buildingproject as a case study. The main contribution of this research is to promote and enhance understanding of the key intelligent indicators, and to setthe foundation for a systemic framework that can be used for appraising system intelligence of various intelligent building systems. It aims toprovide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed componentsdesign configurations.© 2007 Elsevier B.V. All rights reserved.

Keywords: Intelligent building systems (IBSs); Multi-criteria decision-making (MCDM); Analytic hierarchy process (AHP); Analytic network process (ANP);Intelligent indicators (IIs)

1. Introduction

The significant advances in microprocessor-based technol-ogies, and the growing awareness of the connection betweenhuman productivity and living/working environment havedriven many clients to implement intelligent systems into newbuildings in order to achieve an energy-efficient environmentthat can maximize the efficiency of the occupants; and topromote maximum profitability for their own business

⁎ Corresponding author. Tel.: +852 2766 5882; fax: +852 2764 5131.E-mail address: [email protected] (J. Wong).

0926-5805/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.autcon.2007.06.002

[94,139,109,60,114]. The implementation of intelligent build-ing technologies is particularly favourable in Asian cities asdevelopers look for product differentiation, and enhance their‘signature’ building image by forming highly integrated andintelligent building [133]. Although many new buildings areclaimed to be ‘intelligent’, their level of building intelligencenoticeably varies corresponding to the functionality andoperational efficiency of the installed intelligent components[109,133]. Some buildings may incorporate full configurationof intelligent systems and components, while others may onlyconsist of simple building automation systems. Occasionally,intelligent buildings are criticised for not flexibly responding to

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http://dx.doi.org/10.1016/j.autcon.2007.06.002

285J. Wong et al. / Automation in Construction 17 (2008) 284–302

the needs of the end-users. Failure to match the expectations ofclients or end-users would possibly intensify the disconnectionbetween the expectation and fulfilment of the intelligentbuilding. This may result in disenchantment, and a seriousdecline in interest and confidence in intelligent technologies[92,45]. Thus, although there is increasing popularity ofimplementing intelligent technologies in new buildings, thereal challenge is on designing and configuring optimumintelligent building systems (IBSs) which are able to respondintelligently to the changing needs of the end-users, and to fulfilthe goals of the developers.

In practice, the components design configuration poses adilemma in that system designers need to amalgamate the bestavailable intelligent system packages or technologies among avast number of alternatives that are available on the market, andincorporate them to design a building with maximum intelli-gence that meets or exceeds the performance expectationsdesired by developers and end-users [49]. Such design decisionsare further exacerbated by the high aggregation of the multi-criteria and multi-dimensional perspectives of building intelli-gence. The criteria include user friendliness, satisfying businessand commercial needs, meeting international standard protocols,integrating to multiple systems, energy saving features,information technology, scalability, future proofing, and flexi-bility [133]. System designers need to strike a balance betweenthese perspectives, and to cater for the goals and expectations ofthe people paying for and/or intending to occupy the building[9,92]. In dealing with such complexities, developed analyticalmethods and techniques over the past few years of researchefforts have facilitated and enabled the decision-making andevaluation for intelligent building designs. However, review ofavailable literature reveals significant deficiencies specificallyon intelligent building research. The current focus of appraisalstudies is largely on categorical modelling of intelligentbuildings in which the research concentrated on classifying theintelligent building to a definite category according to theiroverall performance [17,109,112,144]. The major gap is todevelop an integrated systematic methodology and techniques inaddressing the system intelligence of the intelligent buildingsystems and components. This problem is further exacerbated bya lack of detailed investigations of system intelligence of theintelligent building systems in prior research [15]. Theseknowledge gaps and practical deficiencies have forced practi-tioners to continuously rely on their past experience, ‘gut-feeling’, rudimentary judgments, or a combination of them, injustifying the intelligence performance of the building systemsduring the design and configuration stages. The tried intelligentbuilding system configuration in one project does not necessar-ily mean the most suitable option in another project as the projectnature and building design varied from one to another. As aresult, inadequate understanding of system intelligence mightlead to incorrect selection of building systems or components.

This study is envisaged to make a significant contribution byaddressing the identified research problems. The main objectiveof this study is to identify the key intelligent indicators, and topresent a systemic analytical approach for appraising the systemintelligence of the key building systems in typical intelligent

buildings. The multi-criteria decision-making (MCDM) tech-nique is proposed as a solution to solve the complexitiesinvolved in the building system intelligence justification. Themultiple dimensions of system intelligence (i.e. the intelligentattributes) of the key intelligent building systems are to beevaluated through an analytic hierarchy-network process (i.e., acombination of AHP and ANP approaches). The analyticnetwork process (ANP) approach is utilized in the currentresearch for two reasons. First, ANP allows a more compre-hensive analytic framework which is not restrictive with ahierarchically structural model alone (i.e. analytic hierarchyprocess, AHP). This method can be used to integrate qualitativeinformation and quantitative analysis, and to capture inter-dependencies among the decision attributes [86,32,69]. Second,so far the application of ANP in solving decision-makingproblems with illustrative examples has been very limited inconstruction and intelligent building research (for example:[32,31,29]). The key processes of the current researchframework are organized as follows:

• review of current intelligent building appraisal research, andidentification of existing research gaps;

• development and validation of intelligent indicators for themajor building systems in the intelligent building (IBSs);

• developing the ANP-based analytical models for computingthe ‘system intelligent score’ (SIS) (discussed in Part 2); and,

• validation of the proposed analytical models using a real-lifepractical intelligent building project as a case study(discussed in Part 2).

Due to the research organization, this study is presented in atwo-part series (Part 1 and 2). This paper (Part 1) reviews theintelligent building literature to accentuate current researchdeficiencies, identifies the suitable intelligent indicators, andproposes the analytical approach for appraising the systemintelligence of the intelligent building systems. In Part 2, themethodology and analytical models are discussed. A real-lifeintelligent building project is used as a case study to validate theproposed system intelligence models, and to test their applicationpracticality. This study contributes tomaking better understandingof the intelligent characteristics and attributes of the key intelligentbuilding systems. It aims to consolidate information from theindustry in order to improve the understandings of the operationalefficiency as well as functionality of the intelligent buildingsystems. This study further formulates the ‘system intelligencescore (SIS)’which may provide a reference for existing buildingsas well as future developments to systematically analyze thespecific building systems performance value to the intelligentbuilding. With this reference tool, it will assist developers andbuilding-stakeholders in justifying the level of system intelligenceof the building systems with the perspective to meet theiroperation objectives.

2. Literature review

The concept of intelligent building has evolved since theearly 1980s. Early perceptions of intelligent building highlighted

286 J. Wong et al. / Automation in Construction 17 (2008) 284–302

on technology related to the building automation (i.e. thedefinition by Cardin (1983: cited in [139]); and that of theIntelligent Building Institution in Washington (1988: cited in[72,37])). Since the mid-1980s, it has been recognized thatbuildings with ‘intelligence’ need to respond to the change andto meet the requirement of the users [141]. An intelligentbuilding should be a dynamic and responsive building thatprovides a productive and cost effective operation environmentthrough optimization among its four basic elements: places(fabrics, structure, facilities, materials), process (automation,control, systems); people (services providers, occupants); andmanagement (maintenance, performance); and the interrelationbetween them (CIB Working Group W098 (1995: cited in [39]).The integrated and intelligent systems play an important role inthat they act as a balance between building contents, theorganization and services that determine if the value objectivesof clients, facility managers, and users are achieved [39]. Theseobjectives include creating a highly energy efficient andenvironmental-friendly built environment with substantialsafety, security, well-being and convenience, a lower life-cyclecost, and long term flexibility and marketability, which lead toachieve a high level of buildings that have the highest social,environmental and economic values [29].

2.1. Current intelligent building appraisal methodologies

Evaluating the building performance is a key determinant in anyacceptance of the intelligent building as a feasible building option[109]. ‘Building intelligence’ has been employed as a unique termof measure to reflect the specific performance and properties of theintelligent building. Over the last twenty years, a large amount ofappraisal methods and techniques have been developed tobenchmark the intelligent performance of the intelligent building(for example: [17,6,7,108,109,114,67,103,121,122,123,146]; andthe work of Building Research Establishment (cited in [16])). Forexample, Arkin and Paciuk [6] devised the ‘intelligent buildingscore’ (IBS) which enables the IB performance to be quantified interms of the building systems installed and the level of integrationthat exists between them. Smith [108] developed the ‘reframing’ tomeasure the enabling ability of intelligent building to meetorganizational objectives through the examination of the organi-zational structure, politics, human resources and culture. Smith[109] also proposed a ‘building intelligent assessment index(BIAI)’ to assess level of building intelligence through sevenbuilding characteristics: site specification, operational cost,intelligent architecture, identity, intelligent technology, systemresponsiveness, and access and security. Concurrently, someinternational intelligent building institutes developed a series ofrating methods to grade the intelligent building according to theirsystem design and performance [29]. For example, the AsianInstitute of Intelligent Buildings (AIIB) [121,122,123] developedan ‘intelligent building index (IBI)’ to assess the performance andcategorize the IB. The UK-based BRE [13] devised a matrix toolfor IB performance assessment. The Continental AutomatedBuilding Association [40] in Canada is at present establishing anew assessment tool (i.e. Intelligent BuildingRanking Tool, IBRT)to assess the level of integrated systems within an IB. Despite such

achievements, most of the measures were developed in forms ofsimplified and generic indexes. Many existing approaches wererestricted in their scope on either tangible (i.e. IBS and IBIA) orintangible (i.e. reframing and QFSD) aspects of IB [109]. Someindexes (i.e. IBI) were criticised for ‘non-determinism of criteria;non-sequitur calculation method; non-uniqueness of calculationresults; and non-organizational judgment of assessment procedure’[29]. Apart from these drawbacks, existing studies lack anexhaustive investigation of the system intelligence of the intelligentsystems and components in the intelligent building. There is also adearth of integrated systematicmethodology and techniqueswhichfacilitates the appraisal of the system intelligence of specificintelligent building systems and components.

2.2. Concepts of system intelligence

Over the last two decades, a plethora of intelligent componentsand products have been introduced. The term “intelligent” hasbeen extensively applied to portray the ‘smart’ properties of thebuilding system products. Manufacturers of the intelligenttechnologies often claimed their system are more intelligent thanthe others of its kind, but these assertions tend to be vague andunjustified [15]. Few universally accepted properties of systemintelligence have been established for characterizing the ‘intelli-gence’ of the intelligent systems in a quantitative approach [15]. Areview of literature suggested that diverse concepts of systemintelligence have been developed. For example, Armstrong et al.[4] suggested that an ‘intelligent’ system should be one able todemonstrate its intelligence to respond effectively to changingneeds of potential occupiers. Wigginton and Harris [139] pointedout that a systemwith real intelligence should behavemore closelyrelated to the realms of both artificial and natural intelligence withthe ability to respond and react to external stimuli in a predictablemanner. That is, a system with artificial intelligence is able toprovide the capacity to perform similar functions to those thatcharacterize human behaviour by emulating the thought process ofliving beings, while a system with natural intelligence relates tothe aspirations of appropriating or devising faculties found inliving beings and the biological capacity [139]. Smith [109] statedthat there are two perspectives of ‘intelligence’ of modernbuilding. One view is related to how the building responds tochange, while another view is closely related to adaptability. Asystem is said to be ‘intelligent’ if it is ‘able to respond and adapt inall these ways’ [109, p.36]. Himanen [59, p.42] suggested that abuilding is ‘intelligent’ if it is implemented with ‘environmentalfriendliness, flexibility and utilization of space, movable spaceelements and equipment, life cycle costing, comfort, convenience,safety and security, working efficiency, an image of hightechnology, culture, construction process and structure, longterm flexibility and marketability, information intensity, interac-tion, service orientation, ability of promoting health, adaptability,reliability, and productivity.’ Despite such efforts, many conceptsof system intelligence tended to be simplistic. There is lack ofcomprehensive and extensive investigations on the intelligentattributes of the intelligent building systems.

Although there are limitations in existing research, somestudies have been tried to tackle these problems. Bien et al. [15]


developed a concept of ‘system intelligence’ by summarizing avast literature regarding system and machine intelligence ofadvanced technologies. Four notions of system intelligence(i.e., autonomy; controllability of complicated dynamics; man-machine interaction; and, bio-inspired behaviour) were pro-posed [15]. The first conception of the intelligent system relates tothe abilities on performing self-operative functions (‘autonomy’).An intelligent system should be designed in a manner that allowsminimum human intervention as much as possible duringexecution of task [15]. Self-calibration, self-diagnostics, fault-tolerance and self-tuning are considered to be the key autonomousfeatures of intelligent systems [15,78,119,70,55,30,93,71,83,128].Second, a system is considered to be ‘intelligent’when it possessesthe ability to perform interactive operative functions, and is able tomake a very complicated dynamic system well-controlled(‘controllability for complicated dynamic systems’). Examplesof the features of controllability for complicated dynamicsystems include non-conventional model-based, adaptation,non-linearity, and motion planning under uncertainty[15,63,64,2,148,115,68,73,126,79,120,44]. In addition, Bienet al. [15] suggested that an intelligent system should possessthe abilities to interface with operator and working staff, whichmake the human users feel more comfortable and use-friendly.The capability of human-friendly interaction between human andmachine (‘man-machine interaction’), is therefore considered asanother important property of the intelligent systems. Examples ofman-machine interaction include human-like understanding orcommunication, emergence of artificial emotion, and ergonomic

Fig. 1. Taxonomy of key intelligent attributes in an

design [15,99,43,117,5,62,56,104,105,53,16,11,77,21]. The lastnotion of system intelligence was related to the capability ofperforming ‘bio-inspired behavioural based technology’ [15]. Thisis the ability to interact with the built environment and the servicesprovided. Previous work suggested that biologically motivatedbehaviour, cognitive-based, and neuro-science are considered asthe typical bio-inspired behaviour of the intelligent systems[41,65,102,124,129,88,57]. Bien et al. [15] further suggested thatboth autonomy and human-machine interaction are two commoncomponents of intelligent systems or machines, while the othertwo constructs, bio-inspired behaviour and controllability forcomplicated dynamics are considered as a specific components ofintelligent systems based on the operational characteristics of thegroups. Furthermore, the model of system intelligence [15]suggested that any intelligent systemwith the above four identifiedintelligent attributes can lead to improved safety, enhancedreliability, high efficiency, and economical maintenance (Fig. 1).

In this study, the system intelligence model of Bien et al. [15]is adopted for the development of the intelligent indicators.With the above model in mind, it is expected that theimplementation of intelligent systems in the building wouldfacilitate the accomplishment of a number of operational goalsand benefits. A review of literature suggested that the mainoperational benefits of installing intelligent building compo-nents include: (1) improved operational effectiveness andenergy efficiency, (2) enhanced cost effectiveness, (3) increaseduser comfort and productivity, and (4) improved safety andreliability [29,109,4,38,33,91].

intelligent system (reference: Bien et al. [15]).


3. Research methodology

This study aims to develop key intelligent indicators, andconstruct the analytical models for appraising the systemintelligence of the main intelligent building systems. The currentresearch will confine the investigation to the following eightmain building control systems in a typical intelligent building.

• integrated building management system (IBMS) for overallmonitoring and building management function;

• heating, ventilation and air-conditioning (HVAC) controlsystem for indoor air quality (IAQ) and comfort control;

• addressable fire detection and alarm (AFA) system for fireprevention and annunciation;

• telecom and data system (ITS) for communication networkbackbone;

• security monitoring and access (SEC) system for surveil-lance and access control;

• smart/energy efficient lift system (LS) for multi-floorstransportation service;

• digital addressable lighting control (DALI) system for lightdesign and control; and,

• computerized maintenance management system (CMMS)for inventory control and service works.

For the development of intelligent indicators, the basicrequirements for a proposed factor or criterion are that it shouldbe quantifiable, effective, relevant, understandable, and usable bythe practitioners and stakeholders [130]. Our list of proposedintelligent indicatorswas first derived from a comprehensive reviewof intelligent building literature (for example: [3,4,6,8,12,10,14,18–20,22,23,28,33–36,42,47,48,51,52,54,58,59,65,74–76,80–82,84,85,89,90,95,98,100,101,106,107,110,111,113,116,118,125,127,132,134,136,137,138,147], and followed by inter-views with local experts and practitioners. Apart from re-viewing these studies, a number of available building servicesguides [for example, guideline series of Chartered Institution ofBuilding Service Engineers [24–27] and intelligent buildingindices [for examples, ‘intelligent building score’ (IBS),

Fig. 2. Research framewo

‘building intelligent assessment index (BIAI)’, and ‘intelligentbuilding index (IBI)] also provide valuable information anduseful insight into the generic intelligent measures of thebuilding services and components. In order to review, justify,and further expand our list of proposed intelligent indicators,three experts (i.e. two M&E engineering consultants and oneproperty developer) in intelligent building field were consulted.

In this research, two consecutive questionnaire-based surveys:(1) a general survey; and (2) anANP-based survey,were conductedin order to develop and validate key intelligent indicators and toconstruct the analytical decision models. The first survey wasdesigned to collect general views from industry practitioners todetermine the relevance and suitability of the indicators to measurethe system intelligence of various IBSs. This survey also aimed todevelop a teamof expertswho have rich knowledge and experiencein intelligent building design and development. They were invitedto participate and complete the ANP-based survey.

The ANP-based survey was adopted to include all relevant(suitable) intelligent indicators, and to compute their meanweights in order to prioritize and distinguish the more importantindicators from the less important ones in general. ANP allowsthe users to consider interdependencies between and amongfactors, and enables the problems to be solved in a complex andnon-hierarchical structure [97]. The overall objective of themodels developed in this research is to determine optimumbuilding systems which help promote the maximum benefits ofintelligent building. The end result is a weighting of the systemalternatives being considered, and the optimal building systemwill be the one which ranks the highest. The findings of theANP survey and the model development will be discussed inPart 2 [143] of this two-part series. The research framework andmethodology of this study is summarized in Fig. 2.

4. Development of suitable intelligent indicators

4.1. Survey instruments and analytical tools

The general questionnaire was purposely designed to testthe suitability of identified intelligent indicators for use in

rk and methodology.


measuring the system intelligence of the intelligent buildingsystems. A pilot study was first undertaken to test the potentialresponse, suitability and comprehensibility of the questionnaire.Five intelligent building experts including three mechanical andelectrical (M&E) engineers, one architect and one propertydeveloper, were selected. These experts were asked to assesswhether the proposed indicators sufficiently represented theintelligent characteristics or attributes of any intelligent buildingsystems if being examined; whether the wording was acceptableor whether they should be changed to make the indicatorsunderstandable to the respondents; and whether additionalindicators should be added that are not included. Commentswere received and minor amendments were made to the originalinstrument. At the end of consultations, a total of 120 intelligentindicators were generated and organized into four intelligentattributes [15]. Table 1 presents the proposed intelligentattributes and their corresponding indicators for the eight keyintelligent building systems. This is not an exhaustive list ofrelevant measures but they are expected to be appropriategeneric intelligent indicators based on the literature and experts'opinions. Additional intelligent indicators can be integratedwhen they are deemed to be essential by the individualstakeholder/respondent.

In this study, two approaches were used to acquire anappropriate size of survey sample. First, an invitation letter ande-mail were sent to main design consultancies (i.e. architectureand engineering firms) and property developers in Hong Kong inearly September 2005. Until late November 2005, a fewcompanies that experienced in intelligent building design anddevelopment accepted our invitation and participated in thissurvey. Questionnaire surveyswere distributed to the staff in thesecompanies who have experience in design and development ofintelligent buildings, via the post and e-mails. Furthermore, the‘snowball’ sampling method was adopted in order to boost thesurvey sample size. The respondents were invited to distribute thequestionnaires to those colleagues or professionals they know thathave rich experiences in intelligent building design anddevelopment. A total of 157 questionnaires were sent out anddistributed, and 48 questionnaire surveys were returned by theend of February 2006. Four completed questionnaires wereremoved due to erroneous use of the rating scale or inappropriaterespondents, and only 44 replies were usable for the analysisgiving a net usable response rate of 28%. This response rateappears both representative and reasonable for two reasons. First,the limited number of experienced professionals in intelligentbuilding field, and the extent of knowledge required forcompleting this lengthy and comprehensive survey restrictedthe size of available sample. Second, empirical study with smallsurvey sample often appears in construction research. Forexample, Ekanayake and Ofori [50] invited a sample of 43building contractors in Singapore in developing the determinantsfor the construction wastes. Ugwu et al. [130] included a sampleof 33 construction practitioners in developing key performanceindicators for sustainability appraisal in infrastructure project inHongKong. Dulaimi andHong [46] identified factors influencingbid-mark-up decisions of building contractors in Singapore by asample of 23 contractors.

This first questionnaire consists of two sections. The firstsection serves to introduce the objectives and scope of thesurvey. The terminology of each IBS and intelligent attributewas defined in order to clarify their meanings. The first sectionis also used to collect demographic data regarding therespondent's previous experience and general knowledge inintelligent building field in order to select those experts who aresuitable for the subsequent ANP-based survey. Participantswere invited to elicit their opinions on the suitability of each ofthe proposed intelligent indicators on a five-point Likert scaleformat (1=Not suitable; 2=Less suitable; 3=Suitable; 4=Moresuitable; and, 5=Most suitable). The critical rating was fixed atscale ‘3’ since ratings above ‘3’ represent ‘more suitable’ and‘most suitable’ according to the scale. Likert scales facilitate thequantification of responses so that statistical analysis could betaken and differences between participants could be observedand generalized [1]. The descriptive statistics were employed toanalyze the survey results on the critical intelligent indicators.The mean scores ratings of all proposed indicators werecalculated using the formula [50,61]:

Mean ¼ 1 rv1ð Þ þ 2 rv2ð Þ þ 3 rv3ð Þ þ 4 rv4ð Þ þ 5 rv5ð ÞN rv1 þ rv2 þ rv3 þ rv4 þ rv5ð Þ ð1Þ

where rv1, rv2, rv3, rv4, rv5 represent the total number ofresponses for scale ‘1’ to ‘5’ respectively.

The t-test analysis was employed to identify ‘suitable’intelligent indicators among them [50,61,142]. The rule of t-testof this survey sets out that the indicators which value larger than3.00 were considered to be critical. The null hypothesis (H0:μ1bμ0) against the alternative hypothesis (H1: μ1Nμ0) weretested, where μ1 represents the mean of the survey samplepopulation, and μ0 represents the critical rating above which theindicators considered as ‘suitable’. The value of μ0 was fixed at‘3’ because it represents ‘suitable’, ‘more suitable’ and ‘mostsuitable’ indicators. The decision rule was to reject H0 when theresult of the observed t-values (tO) (Eq. (2)) was larger than thecritical t-value (tC) (Eq. (3)) as shown in Eq. (4).

tO ¼Pv � l0wsD=

ffiffiffi

np ð2Þ

tC ¼ t n�1;að Þ ð3Þ

tONtC ð4Þwhere Pv is the sample mean, wsD=

ffiffiffi

np

is the estimated standarderror of the mean of different score (i.e. wsD is the sampledstandard deviation of difference score in the population, n is thesample size which was 44 in this study), n-1 represents degreeof freedom, and α represents the significant level which was setat 5% (0.05).

The suitability of intelligent indicators in this study wasexamined using Eqs. (3) and (4)). If the observed t-value is largerthan the critical t-value (tON tC), t (43, 0.05)=1.6820 at 95%confidence interval, thenH0 that the indicator was ‘less suitable’and ‘not suitable’ rejected, and only the H1 was accepted. If theobserved t-value of the mean ratings weighted by the

Table 1Proposed intelligent indicators of the key intelligent building systems (IBSs)

IBS Proposed intelligent attributes and their associated indicators

Autonomy Controllability for complicateddynamics

Man-machine interaction Bio-inspired behaviour

Integrated buildingmanagement system(IBMS)

• Adaptive limitingcontrol algorithm(e.g. max/min thresholdlimiter, fault-toleranceadaptation)

• Ability to link multiplestand alone building controlsystems from a variety ofmanufacturers (interoperability)

• Web base interface to any locationand wireless terminal for functionalaccess (i.e. PALM, pocket PC,mobile phone)

• Analyze operation functionparameters to select the best andeffective operation logic to run thebuilding services systems over time

• Self-diagnostic ofoperation deviations

• Remote control via Internet • Reports generation and output ofstatistical and trend profiling ofcontrols and operations

• Automatically adapt to dailyoccupied space changes to controlbuilding services systems

• Year-round timeschedule operation

• Ability to connect multiplelocations

• Ability to provide operational andanalytical functions for totalizedbuilding performance review

• Provide adaptive controlalgorithms based on seasonalchanges to control building servicessystems

• Alarms and events statistics • Single operation system/ platformfor multiple location supervision

• Control and monitor HVACequipments on sequence control,time scheduling, thermalcomfort, ventilation, faultrecovery operations

• Graphical representation and real-time interactive operation actionicons

• Control and monitor securitysystem interlock operation with“other services”

• Run continually with minimalhuman supervision

• Control and monitor lighting timeschedule/zoning operation• Control and monitor firedetection interlock operation with“other services”• Control and monitor verticaltransportation operation.

Telecom and datasystem (ITS)


• Integrate multiple network orservice providers

• Fixed hub/terminal port installedfor flexibility connections andexpansions

• Interactive voice system

• Self-diagnosis todetect the timewornparts

• Transmission capacity control anddiversion

• System life and turn-roundcomplexity

• Transmission/processing analysis

• All digital system • End-user terminal provisionsHeating ventilation air-conditioning controlsystem (HVAC)


• Operation control mechanismto achieve efficient powerconsumption

• Provide management staff withdatabase and analytical tools foroperation and service evaluation

• Adaptive to occupancy workpattern

• Sensing the internaltemperature andhumidity, and auto-adjustment of systems

• Interface with EnergyManagement System, BuildingAutomation System, orIntegrated Building ManagementSystem

• Pre-programmed responses andzoning control

• Utilize natural ventilation controlto reduce air-conditioning powerconsumption

• Sensing of externaltemperature andhumidity, and auto-adjustment of systems

• Interact with lighting and sun-blinds systems

• Graphical representation and real-time interactive operation actionicons

• Automated faultdetection• Self-diagnosis todetect the timewornparts

Addressable firedetection and alarmsystem (AFA)

• Alarm deploymentalgorithm within thebuilding and notificationto Fire Department

• Integration and control ofsensors, detectors, fire-fightingequipment


• Analysis of alarm and false alarmevents patterns


IBS Proposed intelligent attributes and their associated indicators



Addressable firedetection and alarmsystem (AFA)




• Self-diagnosticanalysis for false alarmreduction

• Interact with security systems • Pre-scheduled of special events andincidents

• Self test of sensors,detectors and controlpoints

• Interact with HVAC systems • Provide access for tenants andoccupants concurrent informationof the services provision

• Self-diagnosis todetect the timeworn parts

• Interact with lift systems

• Interact with emergencygenerator systems

Security monitoringand access controlsystem (SEC)

• Adaptive limitingcontrol algorithme.g. max/min thresholdlimiter, fault-toleranceadaptation)

• Dynamic programming(routing, time schedule,monitoring sequence, controlreaction, etc.)


• Human behaviour analysis anddiagnostic

• Sabotage proof toresist physical damageand modification

• Configurable to accuratelyimplement the security policiesfor the premises


• Adaptive to demands in hightraffic or occupancy situations


• Interface with other system,e.g. communication network,phone system, etc

• Provide access for tenants andoccupants concurrent information ofthe services provision

• Interface with EnergyManagement System; BuildingAutomation System, orIntegrated Building ManagementSystem

• Pre-scheduled set up of specialevents and normal routines;

• Multiple detection orverification mechanism

Smart/energyefficient lift controlsystem (LS)


• Accommodate changes ofpassenger traffic pattern(up peak/down peak)

• Human engineering design tofacilitate convenience of passengers(i.e. voice announcement, fit fordisables, lighting, floor display up/down, etc)

• User designation, verification andspecific control (static sectoring ordynamic sectoring)

• Auto-controllednavigation at emergency(with remote override)

• Remote monitoring • Provide management staff withdatabase and analytical tools foroperation and service evaluation(i.e. levelling performance)

• Integration with building usageschedule for travel programming

• On-line data loggingfacilitating routinemaintenance

• On-line investigation andanalysis of lift activity




• Pre-scheduled of special events andnormal routines

Digital addressablelighting controlsystem (DALI)


• Adaptive to occupancy workschedule


• Provide multiple level and controlmode for occupants to programcustom-made settings

• Monitoringcapabilities that lampperformance and hoursrun can be logged

• Presence detection(i.e. dimmable occupancy sensor,access triggered control)


• Sensing the light intensity andangle of projection and solarradiation to maximize natural light/reduce lighting power (i.e.photoelectric switching anddimming controls)

(continued on next page)

Table 1 (continued )


Table 2Demographic details of the general survey respondents

Demographic information Number %

Nature of workDesign consultants (building services engineers) 27 61%Developers 9 21%Facility managers 8 18%Total 44 100%

Year of experience0–5 years 7 16%6–10 years 16 36%11–15 years 7 16%16–20 years 7 16%21–25 years 1 2%26–30 years 4 9%Over 30 years 2 5%Total 44 100%

Experience in intelligent building developmentCommercial/residential 25 30%Commercial/office 30 37%Commercial/hotel-resort 11 14%Commercial/recreational 5 6%Industrial/warehouse 0 0%Industrial/manufacturing 0 0%Residential/single block villa 3 4%Residential/complex 7 9%Total 81 100%

Table 1 (continued )IBS Proposed intelligent attributes and their associated indicators



Digital addressablelighting controlsystem (DALI)


• Control of individualluminaries, groups of luminariesor lighting zone

• Pre-programmed response andcontrol

• Automatic lighting orshading controls


• User interface via internet/intranetor remote control

Computerizedmaintenancemanagement system(CMMS)

• Automaticallygeneration of routinemaintenance workschedule with alert ofsystem contractexpiration

• Deployment mechanism • Input/output display custom design formanagement requirements

• Diversion of work processon busy schedule

• Statistical evaluationof building services(breakdown, recovery,parts replacement, etc)

• Interfacing with telephonesystem, mobile phone system,SMS system, fax system, e-mailsystem, etc

• Dispatch and works trackingon demand

• Interactive communicationthrough system with siteworkers and operator tomaintain up-to-the-minutesstatus


• System configuration allowsmultiple locations, multiple trade,multiple client database

• Management programming toupkeep changes of labour, work typeand material inventory

• Service quality and customerfeedback management

• Set up maintenance scheduling andspecial services



respondents was less than the critical t-values (tOb tC), the H0

that was ‘less suitable’ and ‘not suitable’ only was accepted.

4.2. Summary of the survey results

Table 2 summarises the sample characteristics of this survey.Forty-four industry practitioners including design consultants,property developers, and facility managers, etc who wereinvolved in intelligent building design and development,participated in this survey. About 61% of the respondentswere from a design background (i.e. M&E engineers, andarchitects), and the remainder were property developers (21%)and facility managers (18%). Most respondents (84%) hadabout 1 to 20 years of work experience in the constructionsector, and 16% of respondents had more than 21 years workexperience. Main types of intelligent building projects theyhave participated in were commercial/residential (30%), andcommercial/office (37%) development. Other developmentsincluded commercial/hotel-resort (14%), commercial/recrea-tional (6%), and residential (13%) projects.

Table 3 presents the mean scores and t-test results. Based onthe findings, 69 key intelligent indicators were extracted from atotal of 120 proposed indicators under the eight IBSs. Pursuantto this table, some patterns were identified:

4.2.1. Integrated building management system (IBMS)The main function of IBMS is to integrate all essential

building services systems to provide an overall strategicmanagement in all aspects with the capacity to systematicallyanalyse and report the building performance and connect withmultiple site/location to give corporation a portfolio view of the

situation. It aims to provide automatic functional control andmaintain the building's normal daily operation. Current IBMSalso provides the function of power quality monitoring andanalysis as well as distribution analysis of electricity, gas, andwater consumption in the intelligent building. In this survey, 16indicators were determined as critical in determining the level of


intelligence of IBMS. The top three ranked indicators were the‘ability to link multiple standalone building control system froma variety of manufacturers’; the ‘graphical representation andreal-time interactive operation action icons’; and the ‘ability toconnect multiple locations’. The highest ranking of ‘ability tolink multiple standalone building control system from a varietyof manufacturers’ reflects an awareness among stakeholdersover the importance of total integration of the sub-systems bythe IBMS. This is probably caused by the frustrationsencountered by stakeholders regarding the incompatibilitiesand limited opportunities for the integration of buildingautomation and control systems among product of differentmanufacturers [135]. Respondents also recognized that theability of IBMS to accommodate all devices and to conformthem to the protocol standard being used is significant. Devicesfrom different manufacturers should employ the same commu-nications network, communicating with their peers and notinterfering with other equipment. In addition, the graphicalrepresentation and real-time interactive operation action iconwere considered as an indispensable intelligent attribute ofIBMS. Example includes the graphical displays of plantoperation which allow diagrams of plants with live point valuesdisplayed, giving on-screen displays of temperatures, flows etc.Operating states of items of plant should be displayed. Setpoints may be adjusted directly and plant items switched on andoff [27]. An optimum IBMS should be able to display real-timetrend graph of the present situation or a review of historical data.

Interestingly, among the three-categories of participants,developers also ranked the ‘ability of system self-diagnostic ofoperation deviations’ and ‘ability to provide operational andanalytical function’ as the top intelligent indicators of IBMS.This reflects that there is a high level of awareness that theimportance of IBMS to detect and diagnose faults in the controlsystems, devices and sensors with automated monitoring andcontrol instrumentations in which design consultants andfacility managers regarded relatively low in their ranking.

4.2.2. Telecom and data system (ITS)The telecom and data system is a communication network

backbone in intelligent building designed to provide effectiveand efficient information transmission or exchange inside andoutside building [109]. Four intelligent indicators including the‘ability of integrating multiple network or service providers’;‘transmission capacity control and diversion’; ‘the installationof fixed hub/terminal port’ and ‘system life and turn-roundcomplexity’, were identified as the most suitable indicator bythe stakeholders. This ranking implies that during datatransmission, communication network should have the abilityto deal with message prioritization/diversion and the avoidanceof message collision when several devices are attempting totransmit at the same time [27].

4.2.3. HVAC control systemThe primary objective of HVAC control system is to enhance

thermal comfort, humidity control, and adequate ventilationinside the buildings. To determine the intelligence performance ofa HVAC system, design consultants placed greater emphasis on

the ability to ‘sense the internal temperature and humidity, andauto-adjustment’, and the ‘interface with EMS, BAS, or IBMS’ ofHVAC control system. As commented by Bischof et al. [1993,citied in [127]], indoor air quality (IQA) is critical for the well-being of occupiers because inadequate ventilation in buildingscan lead to serious problems including sick building syndrome,building-related illnesses and mildew [34,121,3]. In addition, acomfortable and healthy visual environment is critical to supportthe activities of the occupants. In HVAC control system, the PID(Proportional-Integral-Derivative) controls are employed tocontrol the supply air temperature, supply static pressure, andreturn air flow rate. Optimum control strategies are used to resetthe set points of the local PID control loop of the supply staticpressure (for VAV/AHU system), Sensors concerned are thetemperature sensors of the fresh air, return air, and supply air,humidity sensors of the return air and fresh air, and the staticpressure sensor of the supply air. These sensors are essential inmonitoring and automatic control of the air handling process[145]. System instability would result in comfort complaints, IAQissues, control problems, and exorbitant utility cost [3].

Developers and facility managers ranked the ‘ability of pre-programmed responses and zoning control’ as the top intelligentindicator. This implies the need for the existence of pre-pro-grammed control modules in its software to facilitate their dailyHVACcontrol andmonitoring.Anumber of logic control functionswhich may be used to improve control operation [27]. The contro-ller sets its internal parameters to match the characteristics of theactual combination of building and heating system. This configuresto meet the requirements of the actual control strategy to beimplemented. The averaging module is an example of pre-pro-grammed control models which is used to produce a mean value ofa number of inputs. The systemmay be set up to control mean zonetemperature, averaged over several temperature sensors. Sophisti-cated versions may be programmed to ignore extreme values.

4.2.4. Addressable fire detection and alarm (AFA) systemThe key function of the AFA system is to provide effective

fire detection, control and fighting in the building. Other thanthe statutory requirements of the fire detection systems, a totalof ten indicators were determined by stakeholders as importantin justifying the system intelligence of fire detection system.The top two indicators include the ‘alarm deployment algorithmwithin the building and notification to Fire Department’, and‘self-diagnostic analysis for false alarm reduction’. Facilitymanagers emphasized three other intelligent properties of AFAsystem as 1st ranking: ‘interface with security systems’, ‘Runcontinually with minimal human supervision’, and ‘interfacewith HVAC systems’. In principle, during the fire incident, it isimportant for the fire detection system to effectively andefficiently notify the IBMS (or BAS) for a fire and the BASinstructs the security system to unlock access. Emergency doorsand other security entrance controllers should be disabled toallow easy egress of the building occupants [27]. In addition,the control strategy for each subsystem of the HVAC plantshould set up the control action to be taken in the event ofreceiving a fire alarm signal. Much of the plant should be shutdown in response to a fire alarm. The air handling unit (AHU)

Table 3Perceptions of ‘suitable’ intelligent variables/indicators by various industry experts and practitioners

IBS Level 1attributes

Level 2 indicators/variables Mean (SD, ranking) t-value

All (N=44) Designconsultants(N=27)

Developers(N=9)

Facilitymanagers(N=8)

Integratedbuilding

AUT Adaptive limiting control algorithm 3.32 (.740, 12) 3.33 (.832, 11) 3.11 (.601, 5) 3.50 (.535, 3) 2.852⁎

management AUT Self-diagnostic of operation deviations 3.45 (.761, 7) 3.56 (.751, 8) 3.56 (.527, 1) 3.00 (.926, 7) 3.961⁎

system(IBMS)

AUT Year-round time schedule operation 3.25 (.751, 14) 3.41 (.844, 10) 3.00 (.000, 6) 3.00 (.756, 7) 2.208⁎

CCD Ability to link multiple stand alone building controlsystems from a variety of manufacturers

3.93 (.900, 1) 4.15 (.770, 1) 3.56 (.882, 1) 3.63 (1.188, 2) 6.871⁎

CCD Remote control via Internet 3.30 (.978, 13) 3.56 (1.050, 8) 2.56 (.726, 8) 3.25 (.463,5) 2.003⁎

CCD Ability to connect multiple locations 3.61 (.618, 3) 3.81 (.557, 2) 3.22 (.667, 4) 3.38 (.518, 4) 6.585⁎

CCD Alarms and events statistics 3.59 (.816, 4) 3.74 (.813, 3) 3.44 (.726, 2) 3.25 (.886, 5) 4.803⁎

CCD Control and monitor HVAC equipments 3.57 (.759, 5) 3.81 (.736, 2) 3.33 (.500, 3) 3.00 (.756, 7) 4.963⁎

CCD Control and monitor lighting time schedule/zoningoperation

3.39 (.722, 10) 3.63 (.742, 6) 3.11 (.333, 5) 2.88 (.641, 8) 3.548⁎

CCD Control and monitor security system 3.20 (.930, −) 3.59 (.747, −) 2.44 (.882, −) 2.75 (.886, −) 1.460CCD Control and monitor fire detection 3.23 (1.031, −) 3.63 (.926, −) 2.67 (1.000, −) 2.50 (.756, −) 1.462CCD Control and monitor vertical transportation operation. 3.14 (.878, −) 3.37 (.839, −) 2.89 (.782, −) 2.63 (.916, −) 1.030MMI Web base interface to any location and wireless

terminal for functional access3.02 (.976, −) 3.26 (.903, −) 2.78 (.972, −) 2.50 (1.069, −) 0.154

MMI Reports generation and output of statistical and trendprofiling of controls and operations

3.39 (.868, 10) 3.59 (.931, 7) 3.00 (.707, 6) 3.13 (.641, 6) 2.951⁎

MMI Ability to provide operational and analytical functions 3.43 (.728, 8) 3.48 (.802, 9) 3.56 (.527, 1) 3.13 (.641, 6) 3.934⁎

MMI Single operation system/platform for multiple locationsupervision

3.32 (.740, 12) 3.41 (.797, 10) 3.22 (.441, 4) 3.13 (.835, 6) 2.852⁎

MMI Graphical representation and real-time interactiveoperation action icons

3.66 (.939, 2) 3.67 (1.038, 5) 3.44 (.726, 2) 3.88 (.835, 1) 4.658⁎

MMI Run continually with minimal human supervision 3.41 (.897, 9) 3.63 (.926, 6) 3.22 (.833, 4) 2.88 (.641, 8) 3.024⁎

BIB Analyze operation function parameters 3.34 (.745, 11) 3.48 (.753, 9) 2.89 (.333, 7) 3.38 (.916, 4) 3.034⁎

BIB Automatically adapt to daily occupied space changes 3.16 (.914, −) 3.41 (.888, −) 2.78 (.833, −) 2.75 (.886, −) 1.155BIB Provide adaptive control algorithms based on seasonal

changes3.52 (.902, 6) 3.70 (.912, 4) 3.00 (.707, 6) 3.50 (.926, 3) 3.845⁎

Telecom &data

AUT Adaptive limiting control algorithm 3.05 (.569, −) 3.11 (.641, −) 3.00 (.500, −) 2.88 (.354, −) 0.530

system(ITS)

AUT Self-diagnosis 3.09 (.640, −) 3.19 (.622, −) 2.89 (.333, −) 3.00 (.926, −) 0.942

CCD Integrate multiple network or service providers 3.77 (.774, 1) 3.81 (.879, 1) 3.56 (.527, 1) 3.88 (.641, 1) 6.627⁎

CCD Transmission capacity control & diversion 3.55 (.791, 3) 3.59 (.931, 3) 3.44 (.527, 2) 3.50 (.535, 2) 4.574⁎

CCD All digital system 3.14 (.734, −) 3.26 (.764, −) 2.78 (.667, −) 3.13 (.641, −) 1.232MMI Fixed hub/terminal port installed 3.57 (.661, 2) 3.67 (.734, 2) 3.33 (.500, 3) 3.50 (.535, 2) 5.701⁎

MMI System life & turn-round complexity 3.23 (.642, 4) 3.41 (.694, 4) 2.78 (.441, 4) 3.13 (.354, 3) 2.348⁎

MMI End-user terminal provisions 3.16 (.861, −) 3.37 (.839, −) 2.78 (.833, −) 2.88 (.835, −) 1.225BIB Interactive voice system 2.91 (.802, −) 2.93 (.781, −) 2.89 (.782, −) 2.88 (.991, −) −0.752BIB Transmission/processing analysis 3.09 (.709, −) 3.19 (.681, −) 2.89 (.782, −) 3.00 (.756, −) 0.850

HVACcontrol

AUT Adaptive limiting control algorithm 3.32 (.561, 8) 3.48 (.580, 5) 2.89 (.333, 6) 3.25 (.463, 4) 3.760⁎

system AUT Sensing the internal temperature and humidity, andauto-adjustment of systems

3.57 (.818, 3) 3.70 (.775, 1) 3.11 (.782, 4) 3.63 (.916, 1) 4.606⁎

AUT Sensing of external temperature and humidity, andauto-adjustment of systems

3.25 (.943, 10) 3.56 (.892, 3) 2.78 (.667, 7) 2.75 (1.035, 6) 1.758⁎

AUT Automated fault detection 3.50 (.849, 5) 3.52 (.802, 4) 3.44 (.527, 3) 3.50 (1.309, 2) 3.906⁎

AUT Self-diagnosis 3.23 (.677, 11) 3.33 (.679, 7) 2.89 (.333, 6) 3.25 (.886, 4) 2.226⁎

CCD Operation control mechanism 3.52 (.952, 4) 3.56 (.801, 3) 3.56 (.882, 2) 3.38 (1.506, 3) 3.642⁎

CCD Interface with EMS, BAS, or IBMS 3.61 (.689, 2) 3.70 (.669, 1) 3.44 (.527, 3) 3.50 (.926, 2) 5.905⁎

CCD Interact with lighting and sun-blinds systems 2.80 (.904, −) 3.07 (.829, −) 2.11 (.601, −) 2.63 (1.061, −) −1.500MMI Provide management staff with database & analytical

tools for operation & service evaluation3.27 (.845, 9) 3.44 (.801, 6) 2.89 (.601, 6) 3.13 (1.126, 5) 2.140⁎

MMI Pre-programmed responses and zoning control 3.64 (.685, 1) 3.63 (.688, 2) 3.67 (.707, 1) 3.63 (.744, 1) 6.161⁎

MMI Graphical representation and real-time interactiveoperation action icons

3.34 (.834, 7) 3.48 (.849, 5) 3.00 (.707, 5) 3.25 (.886, 4) 2.712⁎

BIB Adaptive to occupancy work pattern 2.89 (.841, −) 3.11 (.892, −) 2.33 (.500, −) 2.75 (.707, −) −0.896BIB Utilize natural ventilation control 3.43 (.759, 6) 3.56 (.751, 3) 2.89 (.333, 6) 3.63 (.916, 1) 3.772⁎


IBS Level 1attributes

Level 2 indicators/variables Mean (SD, ranking) t-value


Developers(N=9)


Addressable AUT Alarm deployment algorithm within the building andnotification to Fire Department

3.73 (.949, 1) 3.96 (.759, 1) 3.56 (1.130, 1) 3.13 (1.126, 5) 5.083⁎

fire detectionAUT Adaptive limiting control algorithm 2.91 (.640, −) 2.96 (.759, −) 2.89 (.333, −) 2.75 (.463, −) −0.942& alarm

system AUT Self-diagnostic analysis for false alarm reduction 3.68 (.601, 2) 3.74 (.656, 4) 3.56 (.527, 1) 3.63 (.518, 1) 7.522⁎

(AFA)AUT Self test of sensors, detectors and control points 3.45 (.791, 8) 3.78 (.506, 3) 2.44 (.726, 7) 3.50 (.756, 2) 3.811⁎

AUT Self-diagnosis to detect the timeworn parts 2.98 (.590, −) 3.07 (.616, −) 2.78 (.441, −) 2.88 (.641, −) −0.255CCD Integration and control of sensors, detectors, fire-fighting

equipment3.48 (.952, 7) 3.56 (.934, 6) 3.33 (1.00, 2) 3.38 (1.061, 3) 3.325⁎


CCD Interact with security systems 3.66 (.861, 3) 3.81 (.834, 2) 3.22 (.833, 3) 3.63 (.916, 1) 5.077⁎

CCD Interact with HVAC systems 3.61 (.813, 4) 3.78 (.801, 3) 3.11 (.782, 4) 3.63 (.744, 1) 5.006⁎

CCD Interact with lift systems 3.45 (.848, 8) 3.67 (.877, 5) 2.89 (.333, 5) 3.38 (.916, 3) 3.556⁎

CCD Interact with lighting/emergency generator systems 3.50 (.976, 6) 3.67 (.961, 5) 3.22 (.833, 3) 3.25 (1.165, 4) 3.397⁎

MMI Run continually with minimal human supervision 3.57 (.974, 5) 3.81 (.834, 2) 2.78 (.972, 6) 3.63 (1.061, 1) 3.869⁎

MMI Provide database and analytical tools for operation andservice evaluation

3.25 (.991, −) 3.41 (.888, −) 2.56 (1.130, −) 3.50 (.926, −) 1.673

MMI Provide concurrent information of the services provision 2.70 (.765, −) 2.96 (.706, −) 2.11 (.782, −) 2.50 (.535, −) −2.562MMI Pre-scheduled of special events and incidents 3.07 (.661, −) 3.22 (.641, −) 2.78 (.667, −) 2.88 (.641, −) 0.684BIB Analysis of alarm and false alarm events patterns 2.86 (.765, −) 3.04 (.854, −) 2.67 (.500, −) 2.50 (.535, −) −1.182

Security AUT Adaptive limiting control algorithm 3.02 (.731, −) 3.15 (.770, −) 2.78 (.441, −) 2.88 (.835, −) 0.206monitoring & AUT Sabotage proof 3.41 (.693, 4) 3.48 (.700, 3) 3.11 (.782, 4) 3.50 (.535, 2) 3.917⁎

access control AUT Self-diagnosis 2.91 (.563, −) 2.93 (.616, −) 2.78 (.441, −) 3.00 (.535, −) −1.071system (SEC) CCD Dynamic programming 3.32 (.909, 6) 3.37 (.884, 5) 3.22 (.833, 3) 3.25 (1.165, 3) 2.321⁎

CCD Configurable to accurately implement the security policiesfor the premises

3.61 (.722, 1) 3.74 (.764, 1) 3.33 (.500, 2) 3.50 (.756, 2) 5.636⁎

CCD Interface with other system, e.g. communication network,phone system, etc

3.59 (.622, 2) 3.74 (.594, 1) 3.44 (.527, 1) 3.25 (.707, 3) 6.302⁎


CCD Multiple detection or verification mechanism 3.11 (.895, −) 3.44 (.751, −) 2.22 (.667, −) 3.00 (.926, −) 0.842MMI Run continually with minimal human supervision 3.57 (.950, 3) 3.70 (.912, 2) 3.11 (.782, 4) 3.63 (1.188, 1) 3.968⁎


3.34 (.834, 5) 3.41 (.844, 4) 2.89 (.782, 6) 3.63 (.744, 1) 2.712⁎

MMI Provide concurrent information of the services provision 2.98 (.792, −) 3.22 (.641, −) 2.22 (.833, −) 3.00 (.756, −) −0.190MMI Pre-scheduled set up 3.20 (.734, 8) 3.30 (.775, 7) 3.11 (.601, 4) 3.00 (.756, 4) 1.849⁎

BIB Human behaviour analysis and diagnostic 2.68 (.800, −) 2.85 (.770, −) 2.44 (.726, −) 2.38 (.916, −) −2.637BIB Adaptive to demands in high traffic or occupancy situations 2.91 (.772, −) 3.04 (.706, −) 2.56 (.726, −) 2.88 (.991, −) −0.781

Smart/energy AUT Adaptive limiting control algorithm 3.18 (.843, −) 3.26 (.944, −) 3.00 (.707, −) 3.13 (.641, −) 1.431efficient liftcontrol

AUT Auto-controlled navigation at emergency 3.61 (.841, 1) 3.59 (.844, 1) 3.44 (.726, 2) 3.88 (.991, 1) 4.838⁎

system (LS) AUT On-line data logging 3.16 (.608, 7) 3.19 (.681, 6) 3.22 (.441, 4) 3.00 (.535, 5) 1.736⁎

AUT Self-diagnosis 2.93 (.759, −) 3.00 (.832, −) 2.78 (.667, −) 2.88 (.641, −) −0.596CCD Accommodate changes of passenger traffic pattern 3.43 (.974, 3) 3.48 (.975, 2) 3.44 (.882, 2) 3.25 (1.165, 3) 2.941⁎

CCD Remote monitoring 3.16 (.939, −) 3.37 (.839, −) 2.56 (1.014, −) 3.13 (.991, −) 1.124CCD On-line investigation and analysis of lift activity 3.30 (.765, 5) 3.33 (.734, 4) 3.11 (.601, 5) 3.38 (1.061, 2) 2.562⁎

CCD Interface with EMS, BAS, or IBMS 3.41 (.972, 4) 3.41 (.971, 3) 3.67 (.707, 1) 3.13 (1.246, 4) 2.791⁎

MMI Human engineering design 3.48 (.849, 2) 3.59 (.797, 1) 3.33 (.866, 3) 3.25 (1.035, 3) 3.730⁎

MMI Provide database and analytical tools for operationand service evaluation

3.20 (.795, 6) 3.22 (.698, 5) 3.11 (.782, 5) 3.25 (1.165, 3) 1.707⁎

MMI Provide concurrent information of the servicesprovision

2.91 (.741, −) 2.96 (.706, −) 2.78 (.667, −) 2.88 (.991, −) −0.813

MMI Pre-scheduled of special events and normal routines 3.20 (.734, 6) 3.22 (.698, 5) 3.11 (.601, 5) 3.25 (1.035, 3) 1.849⁎

BIB User designation, verification and specific control 3.02 (.762, −) 3.07 (.730, −) 2.78 (.441, −) 3.13 (1.126, −) 0.198BIB Integration with building usage schedule for

travel programming3.18 (.815, −) 3.22 (.698, −) 2.89 (.782, −) 3.38 (1.188, −) 1.480

Digital AUT Adaptive limiting control algorithm 3.14 (.668, −) 3.19 (.622, −) 3.11 (.601, −) 3.00 (.926, −) 1.354addressable AUT Monitoring capabilities 3.18 (.815, −) 3.22 (.801, −) 3.22 (.667, −) 3.00 (1.069, −) 1.480lighting control AUT Self-diagnosis 3.00 (.682, −) 2.96 (.808, −) 3.00 (.500, −) 3.13 (.354, −) 0.000system (DALI) CCD Adaptive to occupancy work schedule 3.18 (1.018, −) 3.44 (.892, −) 2.33 (.866, −) 3.25 (1.165, −) 1.185

CCD Presence detection 3.23 (.803, 6) 3.37 (.742, 4) 2.78 (.441, 4) 3.25 (1.165, 5) 1.877⁎

(continued on next page)



Table 3 (continued )IBS Level 1

attributesLevel 2 indicators/variables Mean (SD, ranking) t-value


Developers(N=9)


CCD Control of individual luminaries, groups of luminariesor lighting zone

3.80 (.734, 1) 3.81 (.736, 1) 3.78 (.667, 1) 3.75 (.886, 2) 7.190⁎



3.27 (.845, 4) 3.19 (.736, 6) 3.33 (.866, 2) 3.50 (1.195, 3) 2.140⁎

Digital

MMI Provide concurrent information of theservices provision

2.77 (.774, −) 2.74 (.813, −) 2.78 (.667, −) 2.88 (.835, −) −1.949

addressable

MMI Pre-programmed response and control 3.25 (.839, 5) 3.26 (.764, 5) 3.33 (.866, 2) 3.13 (1.126, 6) 1.977⁎

lightingcontrol

MMI User interface 2.91 (.802, −) 2.93 (.675, −) 2.67 (1.118, −) 3.13 (.835, −) −0.752

system(DALI)

BIB Provide multiple level and control mode 3.18 (.896, −) 3.33 (.877, −) 2.67 (.707, −) 3.25 (1.035, −) 1.346

BIB Sensing the light intensity and angle of projection and solarradiation

3.64 (.967, 2) 3.67 (.877, 2) 3.22 (.833, 3) 4.00 (1.309, 1) 4.367⁎

BIB Automatic lighting or shading controls 3.39 (.841, 3) 3.44 (.801, 3) 3.22 (.441, 3) 3.38 (1.302, 4) 3.046⁎

Computerizedmaintenancemanagementsystem(CMMS)

AUT Automatically generation of routine maintenancework schedule with alert of system contract expiration

3.34 (.914, 3) 3.37 (.884, 3) 3.00(.707, 4)

3.63 (1.188, 1) 2.475⁎

AUT Statistical evaluation 3.48 (.549, 1) 3.52 (.580, 2) 3.33 (.500, 1) 3.50 (.535, 2) 5.763⁎

AUT Self-diagnosis 3.09 (.741, −) 3.15 (.718, −) 2.89 (.601, −) 3.13 (.991, −) 0.813

CCD Deployment mechanism 3.23 (.605, 4) 3.30 (.609, 4) 3.22 (.667, 2) 3.00 (.535, 5) 2.493⁎

CCD Interfacing with telephone, mobile phone, SMS, fax, e-mailsystem, etc

3.05 (.861, −) 3.11 (.751, −) 2.89 (.782, −) 3.00 (1.309, −) 0.350

CCD System configuration allows multiple locations, multiple trade,multiple client database

3.20 (.668, 5) 3.26 (.594, 5) 3.00 (.500, 4) 3.25 (1.035, 4) 2.033⁎

CCD Service quality and customer feedback management 2.75 (.811, −) 3.11 (.698, −) 2.00 (.500, −) 2.38 (.744, −) −2.046MMI Input/output display custom design for management

requirements3.05 (.680, −) 3.26 (.594, −) 2.56 (.726, −) 2.88 (.641, −) 0.443

MMI Dispatch and works tracking on demand 3.00 (.610, −) 3.19 (.557, −) 2.56 (.527, −) 2.88 (.641, −) 0.000MMI Management programming to upkeep changes of labour, work

type and material inventory3.36 (.810, 2) 3.59 (.572, 1) 2.67 (1.118, 6) 3.38 (.744, 3) 2.979⁎

MMI Set up maintenance scheduling and special services 3.23 (.937, −) 3.52 (.753, −) 2.56 (1.130, −) 3.00 (.926, −) 1.609BIB Diversion of work process on busy schedule 3.20 (.632, 5) 3.26 (.594, 5) 2.89 (.601, 5) 3.38 (.744, 3) 2.148⁎

BIB Interactive communication through system with site workersand operator to maintain up-to-the-minutes status

3.23 (.743, 4) 3.26 (.712, 5) 3.11 (.782, 3) 3.25 (.886, 4) 2.029⁎

Note: AUT=Autonomy; CCD=Controllability for complicated dynamics; MMI=Man-machine interaction; and, BIB=Bio-inspired behaviour.⁎Represents the t-values which is higher than cut of t-value (1.6820) indicating the significance of the indicators.



plant will be shut down either continuing the supply and extractfans with inlet and exhaust dampers closed, or with the extractfan continuing to run with the exhaust damper open [27].However, the overall ranking of these controls and interlockingfunctions were ranked 3rd and lower among the intelligentindicators. This outcome indicated that all participants mighthave regarded these functions as default rather than beingintelligent attributes.

4.2.5. Security monitoring and access control (SEC) systemThe SEC system is developed to provide surveillance and

access control to detect unauthorized entry and enhance securityand safety inside the building. Eight intelligent indicators weredetermined. Three indicators that were quite consistently inrankings among developers, design consultants and facilitymanagers include ‘configurable to accurately implementation ofthe security policies for the premises’, ‘interfacing with othersystems’, and ‘continual operation with minimal human

supervision’. The indicator ‘provision of database and analyticaltools for operation and service evaluation’ was given higheremphasis by the facility managers only.

4.2.6. Smart/energy efficient lift system (LS)The smart/energy efficient lift system aims to transport

passengers to the desired floor quickly, safely, and comfortably.A total of 8 key intelligent indicators were determined by therespondents. The four highest rank of intelligent indicatorsincluded ‘auto-controlled navigation at emergency’; ‘humanengineering design’; ‘accommodate changes of passenger trafficpattern’; and, ‘interface with EMS, BAS, or IBMS’.

The ‘auto-controlled navigation at emergency’ relates to theautomatic control and monitoring of lift navigation/operationduring special or emergency events [122]. Lifts can be monitoredby control centre operated by the maintenance companiesremotely so that the performance and real-time status of lift canbe analyzed and recorded but this only ranked No.6. In addition,


human engineering design should be allowed to facilitateconvenience of passengers [24]. Examples include voiceannouncement, suitability for the disabled, and, in-car informationdisplay. Furthermore, an ‘intelligent’ lift control system should beable to accommodate changes of passenger traffic pattern [24].For example, use of artificial intelligence techniques to identifythe number of passengers, and the existence of supervisory controlalgorithm (i.e. dynamic and static sectoring control algorithm) todetect passenger traffic patterns and peak traffic.

4.2.7. Digital addressable lighting control (DALI) systemThe digital addressable lighting control system is expected to

provide acceptable levels of illumination for all aspects ofoccupations, and to enhance efficient lighting usage and energyconservation in the intelligent building [109]. Seven keyindicators were identified by the stakeholders in the survey.Both design consultants and developers ranked the ability of‘system control of individual luminaries, groups of luminaries,and, lighting zones’ and the ‘system interface with EMS, BAS,or IBMS’ as the prime intelligent indicators. In the lightingcontrol system, the luminaire incorporates a presence detectorand a downward-looking photocell which measures the level ofillumination [24]. The built-in controller ensures that illumina-tion is only provided when the space is occupied and provides aconstant level of illumination in varying ambient light levels.The luminaries can communicate with each other over a bussystem. A group of luminaries is switched on if a presence isdetected by any one of them. The luminaries can be programmed toprovide general background illumination to avoid the personworking in an isolated pool of light. Luminaires may beindividually controlled by permitted users over the telephonesystem or from a PC. Overall, the ‘automatically sensing the lightintensity, angle of projection, and the solar radiation’ ranked as topintelligent indicator. This reflects that the lighting control systemshould contain photoelectric switching and dimming control (i.e.photocells) to monitor the light level in the space and regulate thelighting accordingly. A ceiling-mounted photocell lookingdownwards responds to the combined daylight and artificialillumination and the control system is set to provide a constantlevel of illumination.One interesting observation is that developersand facility managers ranked ‘provision of database and analyticaltools for operation and service evaluation’No. 2 and 3 respectively,while design consultant ranked this indicator No. 6. This deviationmay reflect the demand of the end-users of the system, who maynot be satisfied due to the difference in perception of the requiredoperation intelligence in existing practice.

4.2.8. Computerized maintenance management system(CMMS)

The CMMS is designed to provide efficient and effectiveinventory control and service works management of the building.Seven indicators were suggested by the stakeholders to determinethe system intelligence. Overall, the 1st ranked intelligentindicator lies in ‘statistical evaluation’. The 2nd and 3rd rankedindicators reflected management of works deployments and upkeeping of changes. The result implies that the availability,reliability and maintainability of the CMMS are very common

and important in any facilities management operation systemsstrategy. This is a key to effective serviceability and maintenancemanagement of the building.

From Table 3, the coefficient of variation, were about 30%for most cases, which indicates that the perceptions of systemintelligence of different intelligent building systems varymoderately among different stakeholders. The variations inrankings of intelligent indicators in Table 3 reflected differentstakeholders' preference on the suitability of the indicators fordetermining the system intelligence of IBSs. The intelligentindicators to be applied should be project-specific. Utilizingweightings to each option of IBS would reflect such project-specificity [130]. Furthermore, the survey findings suggestedthat the interpretation of ‘intelligence’ is different from oneintelligent building system to another which implies in thisstudy that each IBS performs in a non-unique way and containsunique measures of system intelligence. Our findings wereconsistent with Bien et al. [15].

5. Proposed models of system intelligence appraisal for theintelligent building systems

Once the suitable intelligent indicators of various intelligentbuilding systems are identified, the analytical frameworks thatinvolve numerical analysis of distinct alternatives can beestablished. These analytical frameworks would facilitatedevelopers and stakeholders to justify a wide range of intelligentattributes and indicators before committing to a particular choiceof intelligent building system. As mentioned earlier, the theoryof system intelligence by Bien et al [15] suggested that anyintelligent system with these four identified intelligent attributescan lead to improved safety, enhanced reliability, high efficiency,and economical maintenance (Fig. 1). Reviewing the literatureon intelligent building also suggested that a range of operationalgoals and benefits (i.e. improved operational effectiveness andenergy efficiency, enhanced cost effectiveness, increased usercomfort and productivity, and improved safety and reliability)can be promoted from implementing intelligent technologies inthe intelligent building. Therefore, the proposed analyticalmodels should take the relationship between intelligentattributes and operational benefits of the intelligent buildingsystems into consideration, and resulting in the possibility toform a network-like structural framework. In addition, thecomplicated nature of justifying system intelligence requires aquantitative model that can be used to integrate qualitativeinformation and quantitative values and analysis. For thesereasons, the analytic network process (ANP), a systemicanalytical approach, was proposed and utilized to prioritize theintelligent indicators and dealing with network decision models.

The analytic hierarchy process (AHP) and ANP are two relatedconcepts developed by Saaty [96,97] to handle complicatedMCDMproblems. AHP is a general theory of measurement whichmodels a hierarchical decision problem framework but it isrestrictive to solve problems with a hierarchically structural modelor unidirectional relationships. ANP is the generic form of AHPwhich can model the interdependent relationships in the decisionmaking frameworks by relaxing the hierarchical and unidirectional

Fig. 3. Graphical representation of relationship for the proposed ANP system intelligence appraisal framework for an intelligent building system.


assumptions. The ANP model can be generically designed as acontrol hierarchy (i.e., a hierarchy of subsystems with innerdependencies) or a non-hierarchical network which includesdecision alternatives as an original element cluster [97,140]. In thisstudy, the only interdependencies that are identified and will formthe supermatrix, are between the intelligent building benefits andthe intelligent attributes of the intelligent building systems.Investigating the relationships between these benefits and theintelligent attributes of intelligent building systems is based on theinquiry that if a system designer wants to achieve the maximumbenefits from implementing intelligent building components,which intelligent attribute(s) of the building components orsystems should be relatively more important in promoting thesebenefits. In contrast, each intelligent attribute might have varieddegree of importance in promoting the four identified benefits. Thehierarchy-network framework was proposed to assist the problemstructuring. Fig. 3 provide snapshots of the proposed frameworkfor the model for evaluating the system intelligence of intelligentbuilding system. The model illustrates the interaction andinterdependent relationship among the intelligent attributes andthe operational goals/benefits. The framework builds on priorapplication on performing multi-criteria dimensional evaluationof decision alternative as well as the interrelationship with ex-ternal components among researchers [32,86,87,131]. The detailsof the ANP process and the development of system intelligenceanalytical models will be presented Part 2 of the research.

6. Conclusions and recommendations

This paper, as the first part of this two-part research project,has presented the development of indicators, and introducedanalytical approaches for appraising system intelligence of thekey intelligent building systems. This study commenced with areview of the current research in intelligent building appraisal,reviewed the existing research, and described the practicalproblems. The lack of satisfactory consensus for characterizingthe ‘intelligence’ of the intelligent systems in a quantitativemanner, and the paucity of integrated structured evaluationmethodologies aggravated the difficulties in appraising thesystem intelligence of the intelligent building systems.

In this paper, a general survey was conducted to elicit thesuitable intelligent indicators of the intelligent building systems.A total of 69 suitable intelligent indicators were identified. Thesurvey results further suggest that the interpretation of ‘intelli-gence’ is different from one intelligent building system to anotherwhich implies that each intelligent building system performs in anon-unique way and contains unique measures of systemintelligence. The findings further reveal that ‘autonomy’ wasnot judged as an important intelligence attribute to reflect thedegree of system intelligence in the ITS and DALI systems. Thisresearchwas deliberately limited to an investigation of eight of themost general building control systems in the intelligent building.Future study should include other building systems related towater, waste and pollution control, and sensor technologynetworks in order to provide a better understanding and reflectionon the degree of intelligence of the building control systems in theintelligent building.

Previous research suggested that the development of intelligentbuildings is not only limited to the advances in technologies, theability to achieve the clients and stakeholders’ desires and toenhance their benefits are real objectives of the intelligent buildingdevelopment, but also suggests that the implementation ofintelligent systems in the building lead to a number of operationalgoals and benefits. The authors put forward the use of an analyticnetwork process (ANP) which allows all suitable intelligentattributes, as well as their corresponding indicators, to be takeninto account, not only their relative importance, but also theirinterrelationship with the building's operational goals/benefits. Aconceptual ANPdecision frameworkwas proposed and developedin this paper. In the next paper, the methodology and analyticalmodel set upwill be discussed and presented.A real-life intelligentbuilding project will be used as a case study to validate theproposed system intelligence models and to test their practicality.

Acknowledgements

The authors wish to thank the anonymous respondents whoparticipated in the interview and questionnaire survey for theirinvaluable responses that provided the basis of the empiricalanalysis in this study.


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