Evaluating the system intelligence of the intelligent building systems: Part 2: Construction and...

17 (2008) 303–321www.elsevier.com/locate/autcon

Automation in Construction

Evaluating the system intelligence of the intelligent building systemsPart 2: Construction and validation of analytical models

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 Facilities Management, Pacific Century Premium Developments Ltd., Hong Kong

Accepted 8 June 2007

Abstract

This paper describes the development of an analytical model for computing the system intelligence score (SIS) of key building control systemsin the intelligent building. The models presented in this paper applied the system intelligence theory, and the conceptual analytical frameworkdeveloped in Part 1 [J.K.W. Wong, H. Li, J. Lai, Evaluating the system intelligence of the intelligent building systems — Part 1: Development ofkey intelligent indicators and assessment approaches, (submitted and under review)]. Two multi-criteria decision making (MCDM) approaches,the analytic hierarchy process (AHP) and analytic network process (ANP), were employed in this study to evaluate the system intelligence of theintelligent building systems. AHP was utilized to determine the relative importance of the intelligent attributes and indicators in the model, whilethe ANP supermatrix was employed to examine the interdependence between the intelligent attributes and the operational benefits of theintelligent building system. A comparison of findings obtained from AHP and ANP approaches indicated that most of the values of the intelligentindicators changed. The interdependence between operational goals/benefits and the intelligent attributes modified the original ratings obtainedfrom AHP. The ANP approach allows inclusive decision making on the system intelligence. Such approach not only examines the intelligentproperties of intelligent building systems, but also considers the operational benefits brought by intelligent technologies. The paper uses theintegrated building management system (IBMS) as an illustrative example to present a framework. The proposed analytical model is illustratedwith an example to demonstrate its assessment procedures and test its effectiveness in application.© 2007 Elsevier B.V. All rights reserved.

Keywords: System intelligence score (SIS); Intelligent building system (IBS); Analytic hierarchy process (AHP); Analytic network process (ANP); Intelligentindicators (IIs)

1. Introduction

This paper explores and illustrates an analytical frameworkfor computing the ‘system intelligence score (SIS)’ of the keybuilding control systems in the intelligent building. In Part 1 ofthis study [34], it presented the development of indicators, andintroduced analytical approaches for appraising system intelli-gence of the key intelligent building systems. In this paper (Part2 of the two-part research project), the analytic network process(ANP) approach is utilized to construction the analytic model.ANP is a systemic analytical approach that can not only be used

⁎ 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.2007.06.003

for multi-criteria decision making (MCDM), but can also beemployed for factors/criteria prioritization in academic research[8]. ANP is the generic form of analytic hierarchy process (AHP)which canmodel the interdependent relationships in the decisionmaking frameworks by relaxing the hierarchical and unidirec-tional assumptions. AHP structures the problems throughclassifying them into a hierarchy of elements influencing asystem by incorporating the following levels: decision problem,criteria, sub-criteria, alternatives [14]. Similar to AHP, ANP isfounded on ratio-scale measurement and pairwise comparisonsof elements to derive priorities of selected alternatives, but ANPrelations among criteria and subcriteria are included in evalu-ations, allowing dependencies both within a cluster and betweenclusters [25,33]. As discussed in Part 1 [34], the model of systemintelligence [3] suggested that any intelligent system holding

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304 J. Wong et al. / Automation in Construction 17 (2008) 303–321

identified intelligent attributes (i.e., autonomy, controllabilityfor complicated dynamics, human–machine interaction, andbio-inspired behaviour.) can lead to improved safety, enhancedreliability, high efficiency, and economical maintenance. Basedon this supposition, it is posited that the implementation ofintelligent systems in the building would facilitate the accom-plishment of various operational goals and benefits. Thesebenefits include: improved operational effectiveness and energyefficiency, enhanced cost effectiveness, increased user comfortand productivity, and improved safety and reliability [7,30,2,12,11,22]. On the other hand, previous studies supported that theadoption of intelligent technologies in building should not belimited to the advances in technology, the abilities of the in-stalled intelligent systems to enhance the goals or benefits of theclients, stakeholders, and end-users are equally significant [30].Therefore, the current study encourages the employment of ANPin dealing with the relationships between the intelligent attri-butes of the intelligent building systems and the operationalgoals/benefits. For example, if system designers want topromote the maximum operational benefits from implementingintelligent building technologies, they need to determine whichintelligent properties or attributes should be emphasized. Incontrast, each intelligent attribute might have different degreesof importance in generating each goal/benefit. The remainder ofthis paper is structured into several sections. The procedures andmethodological differences between AHP and ANP approachesare first highlighted, followed by the development of the ANP-based analytical model. The results obtained from AHP andANP will be compared and discussed. A case study example isused to validate the proposed analytical model and to evaluatetheir practicality. Future research directions are suggested for theimprovement of the current work.

2. Analytic hierarchy process (AHP) and analytic networkprocess (ANP)

Both AHP and ANP are two related concepts developed byProfessor Thomas Saaty [23–26]. AHP is mathematic theory ofvalue, reason, and judgement, based on ratio-scales for theanalysis of multi-criteria decision making (MCDM) problems[23,24,33]. It helps to model a hierarchical decision problemframework. It also adopts the pair-wise comparison to assignweights to the elements at the criteria and subcriteria levels andfinally calculates ‘global’ weights for assessment taking place atthe bottom level [9]. The pair-wise comparison judgments weremade with respect to the attributes of one level of hierarchygiven the attribute of the next higher level of hierarchy (from thecriteria to sub-criteria). In addition, AHP is able to solicitconsistent subjective expert judgment via the consistency test.Despite such achievements, AHP is only able to solve problemswith a hierarchically structural model or unidirectional relation-ships, and it is inappropriate for the models that specifyinterdependent relationships.

ANP is an advance version of AHP which can model theinterdependent relationships in the decision making frameworksby relaxing the hierarchical and unidirectional assumptions.This approach is also defined as the ‘system-with-feedback’

approach [9,19]. 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 [25,33].Interdependencies may be represented by two-way arrowsamong levels, or if within the same level of analysis, a loopedarc [19]. In ANP, the preferences of components and attributesare established on a series of pairwise comparisons where thedecision maker will compare two components at a time withrespect to an upper level ‘control criterion’. In addition, ahierarchical relationship is allowed within the ANP networkmodel, but the existence of a feedback relationship among thelevels is only found in ANP. The ANP approach is capable ofhandling interdependence among elements by obtainingweights through the development of a ‘supermatrix’ [19].

In this study, the ANP is appropriate for solving problemsthat can be structured into network-like decision models, whilethe AHP method is appropriate for hierarchical decisionproblems. To be specific, the only interdependencies that areconsidered and form the supermatrix, are between the intelligentattributes and the operational goals/benefits promoted by theimplementation of the intelligent building systems. Due tospace limitations, specific technical components of setting upANP will not be discussed in this paper. Further information canbe acquired in the ANP publications and papers by Saaty[25,26]. The ANP methodology and implementation of themodels are reviewed in the following section.

3. Development of system intelligence analytical model

3.1. Decision model development and problem structuring

The development of a system intelligence analytical modelfirst requires the formation of conceptual model for the decisionproblem which is to be evaluated. The conceptual framework ofanalytical model was proposed and developed in Part 1 [34]. Forthe sake of brevity, and maintaining simplicity in the pre-sentation, only the integrated building management system(IBMS) is presented and used as an illustrative example in thispaper. The proposed system intelligence analytical model for theIBMS is illustrated in Fig. 1(a). At the top of the controlhierarchy, it is the ultimate objective that we want to achieve.The ultimate objective is to determine the overall intelligence ofthe integrated building management system (IBMS) in thisillustrative example. The top level is broken down intointelligent attributes (Level 2) and their corresponding indicators(Level 3). Another separate but related component as depictedabove the intelligent attributes in the decision models relates tothe building's operational goals/benefits. The relationshipbetween the intelligent attributes and the identified operationalbenefits is explored based on the fact that if system designerslook for the best operational goals/benefits from implementingintelligent building components, which intelligent attribute(s) is/are relatively important in leading to such benefits. In contrast,each intelligent attribute might have varied degree of importancein promoting these operational goals/benefits. Thus, the fouroperational goals/benefits (operational goals/benefits of


305J. Wong et al. / Automation in Construction 17 (2008) 303–321

intelligent building) act as external variables and form networkrelationships with the intelligent attributes in the analyticaldecision model. It is noteworthy that the list of operational goals/benefits is not exhaustive, but they are considered as prominentgoals or benefits promoted by the intelligent technologies in theavailable intelligent building literature. The remainder of thedecision network hierarchy is more conventional in that theelements have a hierarchical relationship (i.e., the relationshipbetween the intelligent attributes and their corresponding

Fig. 1. (a) ANP decision model for the system intelligence appraisal of the integratedintelligence appraisal of the telecom and data system (ITS). (c) ANP decision moddecision model for the system intelligence appraisal of the fire detection and alarm (Asecurity monitoring and access control (SEC) system. (f) ANP decision model for th(LS). (g) ANP decision model for the system intelligence appraisal of the digital addintelligence appraisal of the computerized maintenance management system (CMM

indicators). The proposed analytical models for the rest of theintelligent building control systems were illustrated in Fig. 1(b)to Fig. 1(h).

3.2. Pair-wise comparisons matrices of interdependentcomponent levels and variables of intelligent attributes

Once the model is proposed, the matrices should be designedfor pairwise comparison. In order to collect the views on the

building management system (IBMS). (b) ANP decision model for the systemel for the system intelligence appraisal of the HVAC control system. (d) ANPFA) system. (e) ANP decision model for the system intelligence appraisal of thee system intelligence appraisal of the smart/energy efficient lift control systemressable lighting control (DALI) system. (h) ANP decision model for the systemS).

Fig. 1 (continued ).


relative importance of elements, the ANP-based questionnairewas designed in accordance with the intelligent attributes andtheir associated variables of the decision model to allow the ratersto assign weights to the elements. Since the information solicitedrequired in-depth knowledge and rich experience of the intelligentbuilding design and development, a purposivemethod [5,4,15,21]was employed to select the expert group. A number of criteria

were developed for the selection of the eligible participants: (1)experts to be involved in the intelligent building developmentcurrently, recently and directly, especially relating to the designand decision on the building control systems and components; (2)experts to have a comprehensive knowledge of the intelligentbuilding technologies; and (3) experts to have extensive workingexperience in the building field. Only those experts who satisfy all



the sampling criteria were invited to participate in the ANP-basedsurvey by providing their opinions in completing a questionnaire.A total of 13 experts in the general survey (discussed in Part. 1[34]) satisfied all criteria, and nine experts accepted our invitation

and participated in ANP survey. A list of the experts and theirpositions in the corresponding companies is summarized inTable 1. The names of experts were undisclosed in order to respecttheir anonymity.




In fact, both AHP and ANP are subjective methods that focuson specific issue where a large sample is not mandatory [18,8].First, both AHP and ANP approaches may be impractical for asurvey with a large sample size as ‘cold-called’ respondents mayhave a great tendency to provide arbitrary answers, resulting in avery high degree of inconsistency [8]. Second, survey with smallsample has been conducted in previous AHP and ANP research.For example, Cheng and Li [8] invited nine construction expertsto undertake a survey to test comparability of critical successfactors for construction partnering. Lam and Zhao [18] includedeight experts for a quality-of-teaching survey. A very limitedamount of experts were also included in construction of the ANP

model in other studies [for examples, 10,33,13,16,9,17]. Con-sidering the time and efforts that are required for the experts tocomplete a sixteen-page questionnaire composed of cumber-some pairwise comparisons for the eight key intelligent buildingcontrol systems, a total of nine respondents in the currentsurvey were considered quite reasonable and acceptable. Aftercollecting the data from the experts, simple averaging of theweights was completed for final evaluation [29] since itwas assumed that the importance of knowledge, expertise, andperceptions of all experts were equal.

Like AHP, ANP is established on the ratio scale measure-ment and pairwise comparisons of element to determine the

Table 1List of experts for the ANP survey

Name Position (organization type) Years of experience No. of intelligent building project(s) participated

Expert 1 Manager (mechanical and electrical engineering consultancy) 16 6Expert 2 Manager (mechanical and electrical engineering consultancy) 25 6Expert 3 Senior BS Engineer (building contractor) 10 5Expert 4 Project BS Engineer (mechanical and electrical engineering consultancy) 6 3Expert 5 Senior Project BS Engineer (mechanical and electrical engineering consultancy) 15 4Expert 6 Manager (architectural services) 10 3Expert 7 Director (engineering department of property developer) 30 6Expert 8 BS Engineer (building contractor) 4 2Expert 9 Director (mechanical and electrical engineering consultancy) 17 5

Table 2Pair-wise comparison scale for the AHP and ANP model (Source: Saaty, 1996, p.24) [25]

Intensity of importance Definition Explanation

1 Equal importance Two activities contribute equally to the objectives3 Weak/moderate importance

of one over anotherExperience and judgment slightly favoured one activity over another

5 Essential or strong importance Experience and judgment strongly favour one activity over another7 Very strong or

demonstrated importanceAn activity is favoured very strongly over another; its dominance demonstrated in practice

9 Absolute importance The evidence favouring one activity over another is of the highest possible order of affirmation2, 4, 6, 8 Intermediate values between the

two adjacent scale valuesUsed to represent compromise between the priorities listed above

Reciprocals of abovenon-zero numbers

If activity i has one of the above non-zero numbers assigned to it when compared toactivity j, then j has the reciprocal value when compared with i.


relative importance or priorities of selected alternatives. Inorder to estimate the relative importance of the two-comparedelements, the relative importance weight (denoted as aij) ofinterdependence in the current study was determined by using a9-point priority scale of pair-wise judgment which wasdeveloped by Saaty [25]. This 9-point priority scale ofmeasurement [7,8] consist of a score of 1 representing equalimportance between paired elements, and 9 being demonstra-tive dominance of the row element over the column element.Table 2 tabulates the details and meanings of the 9-pointpriority scale. In this illustrative example of IBMS, thecomparison matrix (i.e., the relative importance) of the fourintelligent attributes with respect to the decision problem (i.e.,the most ‘intelligent’ IBMS) was first determined. The fourintelligent attributes (level 2) were rated pair-by-pair with

Fig. 2. Summary of comparison matrix results (eigenvectors) of intelli

respect to the decision problem (level 1) in Fig. 2 (Matrix 1).Then, the relative importance of the intelligent attributes (e.g.autonomy vs. man–machine interaction) with respect to aspecific operational goals or benefits of the intelligent buildingwas investigated (Fig. 3). A pairwise comparison matrix wasrequired for each of the goals/benefits for calculation ofimpacts of each intelligent attribute (Matrix 2 to 5). In addition,four pairwise comparison matrices (Matrix 6 to 9) wererequired to calculate the relative impacts of the each goals/benefits (i.e., enhance reliability vs. higher efficiency) on aspecific intelligent attribute (Fig. 4). As a result, a total of eightpairwise comparison matrices were required to describe thetwo-way relationship. Once the pairwise comparisons werecompleted, the local priority was then calculated (Fig. 5). Thelocal priority vector is an array of weight priorities containing a

gent attributes with respect to the decision problem from experts.

Fig. 3. Summary of comparison matrix results (eigenvectors) of the intelligent attributes with respect to the buildings' operational goals/benefits of the IBMS from experts.

Fig. 4. Summary of comparison matrix results (eigenvectors) of the buildings' operational goals/benefits with respect to the intelligent attributes of IBMS from experts.


Fig. 5. Summary of comparison matrix results (eigenvectors) of the indicators with respect to respective intelligent attributes from experts.

Fig. 6. The combined matrix (Matrix A) formed from eigenvectors (relative importance weights) for the implications of operational goals/benefits on systemintelligence attributes of the IBMS.

Fig. 7. The combined matrix (Matrix B) formed from eigenvectors (relative importance weights) for the implications of intelligence features/attributes of the IBMS onpromoting the buildings' operational goals/benefits.


single column, whose components (denoted as wi) are derivedfrom a judgment comparison matrix. The local priority vector iscomputed as the unique solution to

Aw ¼ kmaxw ð1Þwhere λmaxw is the largest eigenvalue of A providing severalalgorithms for approximating w [20,23]. The process ofaveraging over normalized columns can be done by dividingeach element in a column by the sum of the column elementsand then summing the elements in each row of the resultant

matrix and dividing by the n elements in the row. This can bededucted by Eq. (2) [19,25].

wi ¼

XI

i¼1

aijPJj¼1

aij

0BBB@

1CCCA

Jð2Þ

where wi is the weighted priority for component i; aij is amatrix value assigned to the interdependence relationship ofcomponent i to component j.

Fig. 8. Supermatrix relationship of an intelligent building system [19].


Similar to AHP, the consistency of the judgements is significantin the ANP measurement as it aims to eliminate the possibleinconsistency revealed in the criteria weights through the compu-tation of consistency level of each matrix [8]. The consistency ratio(CR) is used to determine and justify the inconsistency in the pair-wise comparison made by the respondents. Saaty [24], and Chengand Li [8] set the acceptable CR values for different matrix's sizes:(1) the CR value is 0.05 for 3×3 matrix; (2) 0.08 for a 4×4 matrix;and (3) 0.10 for larger matrices. If the CR value is lower than theacceptable value, the weight results are valid and consistent [24]. Incontrast, if the CR value is larger than the acceptable value, thematrix results are inconsistent and were exempted for the furtheranalysis. In the current study, all completed questionnaires ap-peared to have acceptable consistency.

After the calculation, the weighted priorities for each of theexpected operational goals/benefits were combined to form amatrix A with four columns and four rows as shown in Fig. 6.The local priority weights for the relative importance of thegoals/benefits on the intelligent attributes were then investigat-ed. An illustrated example in Fig. 6 shows that if systemdesigner places high emphasis on the ‘autonomy’ feature, therewill be a larger influence on the system reliability and safety ofIBMS. As a result, the weighted priorities for each of intelligent

Fig. 9. Supermatrix ‘E’ for complied from matrices A and B for the linkag

attributes were combined to form a 4×4 matrix B as shown inFig. 7.

In the current study, it is assumed that no interdependentrelationship between the level of intelligent attributes and theirassociated variables exists. The pairwise comparison of theelements at the indicators/variables level (level 3) is conductedwith respect to their relative influence (eigenvector determina-tion) towards their control criteria (i.e. intelligent attributes inlevel 2). The eigenvectors of separate pairwise comparisonmatrices (Matrix 10 to 13) developed between level 2 and 3from the experts were summarized in the illustrative example ofIBMS (Fig. 5).

3.3. Supermatrix formation and analysis

The supermatrix promotes a resolution of the effects of theinterdependence that exist between the elements of ANP model[19]. This can be achieved by entering the local priority vectors inthe supermatrix, which in turn obtains the ‘global’ priorities. Theinitial supermatrix includes local priority vectors obtained fromthe pairwise comparisons among clusters and nodes. Three math-ematical steps are involved in calculating the ‘supermatrix’[25,33]: (1) unweighted supermatrix; (2) weighted supermatrix;

es of the IBMS intelligent attributes and the operational goals/benefits.

Table 3The final weights of IBMS intelligent indicators

Intelligentattributes andindicators ofIBMS

The normalized value ofcategory from the averagelimiting supermatrix

The relativeweight ofindicator

The finalweight ofindicator(ANP)

AUT 0.2664AL 0.3437 0.0916SD 0.3477 0.0926YT 0.3086 0.0822

CCD 0.3006CML 0.1208 0.0363LMS 0.1543 0.0464AES 0.2184 0.0657MHVAC 0.2253 0.0677ML 0.1880 0.0565RCO 0.0932 0.0280

MMI 0.2406GR 0.2098 0.0505OAF 0.1605 0.0386RG 0.1148 0.0276RC 0.3337 0.0803SOS 0.1812 0.0436

BIB 0.1924AO 0.4659 0.0896AC 0.5341 0.1028

Note: AUT = autonomy; BIB = bio-inspired behaviour; CCD = controllability ofcomplicated dynamics; and MMI = man–machine interaction; AL = adaptivelimiting control algorithm; SD = self-diagnostic of operation deviations; YL =year-round time schedule operation; AO = provide adaptive control algorithmsbased on seasonal changes; AC = automatically adapt to daily occupied spacechanges; LMS = ability to link multiple standalone building control systemsfrom a variety of manufacturers; RCO = remote control via internet; CML =ability to connect multiple locations; AEC = alarms and events statistics;MHVAC = control and monitor HVAC equipments; ML = control and monitorlighting time schedule/zoning operation; RG = reports generation and output ofstatistical and trend profiling of controls and operations; OAF = ability toprovide operational and analytical functions; SOS = single operation system/platform for multiple location supervision; GR = graphical representation andreal-time interactive operation action icons; and, RC = run continually withminimal human supervision.

Table 4Comparison of the weightings of IBMS intelligent indicators from ANP andAHP approaches

Intelligent attributes andindicators of IBMS

Indicators' weightfrom ANP

Indicators' weightfrom AHP

AUTAL 0.0916 0.0977SD 0.0926 0.0988YT 0.0822 0.0878

CCDCML 0.0363 0.0388LMS 0.0464 0.0496AES 0.0657 0.0702MHVAC 0.0677 0.0724ML 0.0565 0.0604RCO 0.0280 0.0300

MMIGR 0.0505 0.0476OAF 0.0386 0.0364RG 0.0276 0.0260RC 0.0803 0.0757SOS 0.0436 0.0411

BIBAO 0.0896 0.0780AC 0.1028 0.0895

Note: AUT = autonomy; BIB = bio-inspired behaviour; CCD = controllability ofcomplicated dynamics; and MMI = man–machine interaction; AL = adaptivelimiting control algorithm; SD = self-diagnostic of operation deviations; YL =year-round time schedule operation; AO = provide adaptive control algorithmsbased on seasonal changes; AC = automatically adapt to daily occupied spacechanges; LMS = ability to link multiple standalone building control systemsfrom a variety of manufacturers; RCO = remote control via internet; CML =ability to connect multiple locations; AEC = alarms and events statistics;MHVAC = control and monitor HVAC equipments; ML = control and monitorlighting time schedule/zoning operation; RG = reports generation and output ofstatistical and trend profiling of controls and operations; OAF = ability toprovide operational and analytical functions; SOS = single operation system/platform for multiple location supervision; GR = graphical representation andreal-time interactive operation action icons; and, RC = run continually withminimal human supervision.


and (3) limit supermatrix. The unweighted supermatrix is firstcalculated from all local priorities derived from pairwise com-parisons among elements influencing each other. The elementswithin each cluster are compared with regard to influencingelements outside the cluster, and the eigenvector of influence ofall clusters on each other. The weighted supermatrix is thencreated by multiplying the values of the unweighted supermatrixwith their affiliated cluster weights. To make the column stochas-tic (i.e. sum of the column amount to 1), the weighted supermatrixneeds to be normalized [25]. In Fig. 8, the matrices A and Brepresent interdependence between two levels of components,while the relationships C andD represent the interdependence of alevel of components on itself. The work of Cheng et al. [10] andMeade and Sarkis [19] suggested that if the same level impacts arenot deemed to be significant and all the values in sub-matrices(i.e., sub-matrices C and D in this illustrative example) will be

assigned a zero value. Otherwise, the normalization step will berequired to make the column stochastic if the sub-matrices werenon-zero matrices. In this study, the same level impacts areassumed not to be significant, and the matrices A and B are thenrequired to combine to form the supermatrix E shown in Fig. 9.The supermatrix includes the eigenvectors associated with thefour intelligent attributes with respect to the decision problems. Italso illustrates the eigenvectors from the interdependent influ-ences between the four intelligent attributes (i.e. autonomy; con-trollability of complicated dynamics; man–machine interaction;and bio-inspired behaviour) and the four operational goals/bene-fits (i.e. enhanced cost effectiveness; improved operational effec-tiveness and energy efficiency; improved user comfort andproductivity; and, increased safety and reliability). The final sub-step of ANP calculation relates to the formation of a limit super-matrix. This is a process by raising the entire weighted

Fig. 10. Relative importance of individual IBMS intelligent indicators as derived from ANP and AHP methods provided equal preferences in direct pairwisecomparison [33].


supermatrix to a limiting power until convergence in terms of alimes (i.e., a Cesaro sum) [25,33]. The average priority weightsare obtained by the following formula:

limk1N

� �XNk¼1

Wk ð3Þ

whereW is the weighted supermatrix, N is the sequence, and k isthe exponent determined by iteration. In this study, the SuperDecisions© software was employed to calculate the limit super-matrix. Table 3 summarizes the results of average limitingsupermatrix with the relative importance and final weights of eachIBMS intelligent indicator.

4. Findings, discussion and recommendations

In this study, two MCDM approaches were employed for theconstruction of the system intelligence analytical model.AHP was adopted to determine the relative importance of theintelligent attributes and indicators in the model, while the ANPsupermatrix was incorporated to consider the influence ofintelligent attributes of each key intelligent building system inaccomplishing their operational goals/benefits. As previouslymentioned, the theory of system intelligence supposed that theimplementation of intelligent systems in intelligent buildingscan lead to the accomplishment of a number of building'soperational goals/benefits. This possible relationship wasinvestigated and examined by ANP approach in this study.The analytical model was enhanced through the inclusion ofadditional (i.e., feedback/systems) relationship. Contrastingnetworked ANP with the hierarchy AHP model by applyingboth to the system intelligence evaluation, some distinctionswere found. First, the resulting outcomes of the normalizedrelative weights of the intelligent indicators obtained from ANPand AHP are varied. The priorities of the individual indicatorsof the IBMS obtained from ANP and AHP approaches were

tabulated and are graphically presented in Table 4 and Fig. 10respectively. Applying AHP and ANP with the same input, as inour example, resulted in different ranking of indicators. Whilethe self-diagnostic (SD) (0.0988) and adaptive limiting controlalgorithm (AL)(0.0977) was the most prominent as revealed bythe AHP model, the provide adaptive control algorithms basedon seasonal changes (AC) (0.1028) in ANP model was found tobe the most significant intelligent indicator, not the SD and ALas revealed by the AHP model. Surprisingly, the ability to linkmultiple standalone building control systems (LMS) (0.0464),which have been considered as top intelligent indicator in thesurvey in Part 1 [34], ranked as No.10 and 11 intelligentindicators in AHP and ANP approaches respectively in thecurrent study. This is possibly due to the fact that expertsconsidered the LMS as basic feature and requirement of theIBMS. The importance of providing adaptive control algo-rithms based on seasonal changes (AC) in this expert surveysuggests that an optimum ‘intelligent’ IBMS should be able toself-tune and act continually to optimise the control settings [6].In addition, a further comparison of findings from AHP andANP approaches found that most of the values of the intelligentindicators had changed. This implied that the interdependentrelationships between the operational goals/benefits and theintelligent attributes altered the original hierarchical ratings bythe experts. The network-analysis approach allows a morecomprehensive consideration of the system intelligence as it notonly tries to deliberate on the intelligent properties, but alsotakes the operational benefits brought by the intelligent tech-nologies into account.

For the application of the proposed analytical model, twoproposed IBMS options (i.e., Option A and B) from a realintelligent building project in Hong Kong were used to illustrateits assessment procedures and demonstrate its effectiveness.In this study, the brands of the IBMS systems and theirmanufacturers were not disclosed in order to secure theconfidentiality of the information providers and to prevent the



intention of any guesses. Instead, fictitious names (i.e. Option Aand B) were assigned. The first IBMS alternative (i.e., OptionA) is developed by a European manufacturer and containsunique features of peer-to-peer operation with a flexible andremote alarm management system. The second IBMS alterna-tive (i.e., Option B) is produced by a US manufacturer withsimilar system features as Option A. A score from ‘1’ to ‘5’ wasassigned to each intelligent indicator based on both quantitativeand qualitative assessment methods as shown in Appendix A.These assessment methods were developed based on previousresearch [31,32] and opinions from experts. The pairwisecomparisons were completed by asking the relative impact of anoption on an intelligent indicator. For instance, the existence/level of adaptive limiting control algorithm (AL) is comparedbetween Option A and B. The final results are summarised inTable 5. It suggested that under the ANP approach, Option Bwas considered more ‘intelligent’ as it has a higher weightedscore (3.7977) than Option A (3.7606) with the interdependentrelationship between the intelligent building goals/benefits andthe intelligent attributes was taken into consideration. However,when the interdependent relationship was not considered (i.e.,AHP approach), Option A (3.8124) has a higher score thanOption B (3.7686). The ‘system intelligence score’ (SIS) of twoproposed IBMS options can also be graphically represented inform of radar diagram plots (Fig. 11). This example supports theapproach of Cheng et al. [10] that ANP is an effective andcomprehensive approach for decision problem solving when theinterdependent relationship has considerable impacts on thedecision model. The interdependent relationships are able toinfluence the decision to be made for the system intelligenceevaluation. Due to space limitation, the findings and discussionof both AHP and ANP analysis on the remaining seven building

Table 5Summary of the ‘System Intelligence Score (SIS)’ of two IBMS options under ANP

Indicator (attribute) ANP approach

Indicator'sweight

Option A Option B

Score Weight Score

AL(AUT) 0.0916 5 0.4580 3SD(AUT) 0.0926 4 0.3704 4YT(AUT) 0.0822 4 0.3288 3CML(CCD) 0.0363 5 0.1815 4LMS(CCD) 0.0464 4 0.1856 4AES(CCD) 0.0657 5 0.3285 3MHVAC(CCD) 0.0677 4 0.2708 4ML(CCD) 0.0565 4 0.2260 3RCO(CCD) 0.0280 4 0.1120 3GR(MMI) 0.0505 3 0.1515 5OAF(MMI) 0.0386 3 0.1158 4RG(MMI) 0.0276 3 0.0828 5RC(MMI) 0.0803 3 0.2409 4SOS(MMI) 0.0436 3 0.1308 5AO(BIB) 0.0896 3 0.2688 4AC(BIB) 0.1028 3 0.3084 4Weighted mean 3.7606

Note: Intelligent indicators weights were normalized. The indicators were rated basMaximum score of SIS=5.0000.

control systems in intelligent building are not presented in detailin this paper. The same methodologies can be applied to thecomputation of SIS for the remaining intelligent buildingcontrol systems. Appendix B tabulates the prioritization of theintelligent indicators of the rest of the seven key intelligentbuilding systems.

Although the approach adopted in the current paper hasgenerated some interesting findings, the approach has proved tobe excessively time-consuming and complex. The complexityof the ANP approach increases exponentially with theadditional interdependency relationships as they increase thenumber of pairwise comparison matrices and pairwise compar-ison questions required for an evaluation [19,27]. Elicitinginformation from the raters for the large amount of pairwisecomparison become tedious. The models in this study requiresignificant time and effort for the experts in completing thisANP process. For example, in the IBMS, they required 13pairwise comparison matrices and 83 pairwise comparisonquestions. Overall, a total of 88 pairwise comparison matrices,with 398 pairwise comparison questions were required for alleight intelligent building systems in this study. To facilitate easycompletion of the matrices by the raters, clear and unambiguousdefinitions and delineations of intelligent attributes andindicators are required.

Also, the current model that is presented may still needadditional extensions. Further research is needed to analyse thesensitivity and uncertainty of ANP priority. The current study isalso limited to domestic intelligent building development, itwould be interesting to undertake a more ambitious study alongthese lines in other geographical locations as the perceptionsand applications of system intelligence of intelligent buildingcomponents would be varied. Dynamic software approaches

and AHP approaches

AHP approach

Indicator'sweight

Option A Option B

Weight Score Weight Score Weight

0.2748 0.0977 5 0.4885 3 0.29310.3704 0.0988 4 0.3952 4 0.39520.2466 0.0878 4 0.3512 3 0.26340.1452 0.0388 5 0.1940 4 0.15520.1856 0.0496 4 0.1984 4 0.19840.1971 0.0702 5 0.3510 3 0.21060.2708 0.0724 4 0.2896 4 0.28960.1695 0.0604 4 0.2416 3 0.18120.0840 0.0300 4 0.1200 3 0.09000.2525 0.0476 3 0.1428 5 0.23800.1544 0.0364 3 0.1092 4 0.14560.1380 0.0260 3 0.0780 5 0.13000.3212 0.0757 3 0.2271 4 0.30280.2180 0.0411 3 0.1233 5 0.20550.3584 0.0780 3 0.2340 4 0.31200.4112 0.0895 3 0.2685 4 0.35803.7977 3.8124 3.7686

ed on a 5-point scale based on their existence and level of functions/services.

Fig. 11. Radar diagram plots of the ‘System Intelligence Score (SIS)’ of two IBMS options under ANP and AHP approaches [32].

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would also be recommended to promote the use of ANP as atool for comprehensive decision-making and evaluation ofsystem intelligence of the intelligent system. Eventually, somemight argue that complicated decision making methods maybe inferior to intuition in making sound judgements [10], butdue to the complexity and qualitative natures of system in-telligence justification, the proposed systematic ANP and AHapproaches are able to deal with many (if not all) of thelimitations.

This research was deliberately limited to an investigation ofeight of the most general building control systems in theintelligent building since it would be too difficult in a researchpaper, to try to identify all specific building control systems inthe intelligent building. Further research could be undertaken byextending to the systems related to water, waste and pollutioncontrol as well as sensor technology networks, to provide abroader picture of the system intelligence of the building controlsystems in the intelligent building. Furthermore, the scope ofthis research is confined to the investigation of building controlsystems in the general commercial intelligent building (i.e.,office). The uses and requirements of building control systemsdepend on the building types (for example, office buildings,residential towers, shopping malls, hospitals and airportbuildings) and their ultimate usages [1]. This implies that theintelligence attributes and indicators identified in this researchmight not be generalised to all types of intelligent buildings.However, it should be noted that there are still some aspectssuch as adaptability, flexibility, aesthetics and functionalitywhich are common to all building. In addition, the size of thesample of this research was limited. Since the intelligentbuilding industry is new and developing, a large sample ofprofessionals was not available. Only a very limited number ofexperts could be identified for the surveys. The major group ofexperts was the design consultants (i.e., M&E engineers),together with a small number of developers and facilitymanagers. A larger sample would help for improving the extentto which these models represent human decision makingprocesses. Future study should also include the buildingoccupants as part of the survey sample because they are theend-users of the intelligent building. For example, the factorsthat the end-users adopt for assessing and comparing theusefulness of intelligent building control systems can beinvestigated. Their feedback provides a better understandingand reflection on the actual performance (or degree ofintelligence) of the building control systems.

5. Conclusions

This study ([34] and this paper), presents the development ofindicators, and develops analytical decision models forappraising system intelligence of the key intelligent buildingsystems. In Part 1 [34], the authors first reviewed the currentresearch in intelligent building appraisal, and highlighted theexisting research deficiencies and practical problems. Due to thelack of satisfactory consensus for characterizing the system‘intelligence’ of the building systems in the intelligent building,there is a demonstrable need to develop a list of ‘suitable’

intelligent attributes and indicators. A general survey was firstconducted to elicit a group of suitable indicators for use inmeasuring the system intelligence of the key IB systems. In thispaper, the innovative analytic network process (ANP) wasemployed for the development of analytical model forintelligence evaluation. ANP was utilized as it allows allsignificant intelligent indicators to be taken into account. Thismeans that not only consider their values but also theinterrelationship between the intelligent attributes (i.e. autono-my; controllability of complicated dynamics; man-machineinteraction; and bio-inspired behaviour) and the operationalgoals/benefits (i.e. enhanced cost effectiveness; improvedoperational effectiveness and energy efficiency; improved usercomfort and productivity; and, increased safety and reliability)of the intelligent building systems. The findings obtained fromANP were compared and discussed with the results obtained byAHP approach based on the same set of data obtained from real-case scenerios input by experienced practictioners in the pro-perty development and building services sectors. It suggestedthat ANP is a robust approach if the decision model issignificantly influenced by the interdependent relationships.The illustrative example in this paper demonstrated how theinterdependencies between the intelligent attributes and oper-ational benefits of intelligent building affect the results ofprioritization and decision of choices.

This paper has helped to promote and enhance understandingof the key intelligent indicators for system intelligenceappraisal, and develop a comprehensive methodology foranalyzing the system intelligence of the building systems inthe intelligent building. The system of evaluation proposed inthis paper is one approach along the pathway towards a truegenetic platform. From a commercial perspective, the estab-lishment of SIS provides a way that allows developers or designteams to estimate the building control system products using theindex to manifest their intelligence superiority. It provides abenchmark to measure the degree of intelligence of one controlsystem candidate against another. Building control systemconsumers are provided with an alternative approach tocompare and contrast several building control system productsfrom the viewpoint of intelligence [28]. The proposed appraisalmodel would assist clients and stakeholders to consider a widerange of intelligent attributes and indicators before committingto a particular choice of system alternative or to evaluate anyexisting building's intelligence in fulfilling the users or ownersexpectations. The SIS can be viewed as a reference for existingbuildings as well as future developments to systematicallyanalyze the intelligence performance of specific buildingsystems which value to the modern building. This paper alsodemonstrated the application of ANP as a tool to quantify thesystem intelligence of the intelligent building systems.

Acknowledgements

The authors wish to thank the anonymous experts whoparticipated in the interviews and surveys for their invaluableresponses that provided the basis of the empirical analysis inthis study.



Appendix A

List of ‘suitable’ intelligent indicators/measures of intelligent building systems as elicited by the survey respondents and measurement methods of each indicator

Intelligent building systems Intelligent attribute group Indicators/variables Assessmentmethod(s)

Integrated building management system (IBMS) Autonomy Adaptive limiting control algorithm (AL) Method 1Autonomy Self-diagnostic of operation deviations (SD) Method 1Autonomy Year-round time schedule operation (YT) Method 1Controllability for complicateddynamics

Ability to link multiple standalone building control systems from a variety of manufacturers (LMS) Method 1,2

Controllability for complicateddynamics

Remote control via internet (RCL) Method 1


Ability to connect multiple locations (CML) Method 1


Alarms and events statistics (AES) Method 1


Control and monitor HVAC equipments (MHVAC) Method 1,2


Control and monitor lighting time schedule / zoning operation (ML) Method 1,2

Man–machine interaction Reports generation and output of statistical and trend profiling of controls and operations (RG) Method 1Man–machine interaction Ability to provide operational and analytical functions (OAF) Method 1Man–machine interaction Single operation system/ platform for multiple location supervision (SOS) Method 1Man–machine interaction Graphical representation and real-time interactive operation action icons (CR) Method 1Man–machine interaction Run continually with minimal human supervision (RC) Method 1,3Bio-inspired behaviour Analyze operation function parameters (AO) Method 1Bio-inspired behaviour Provide adaptive control algorithms based on seasonal changes (AC) Method 1

Telecom and data system (ITS) Controllability for complicateddynamics

Integrate multiple network or service providers (IMS) Method 1


Transmission capacity control & diversion (TCCD) Method 1

Man–machine interaction Fixed hub/terminal port installed (FHTP) Method 1Man–machine interaction System life & turn-round complexity (SLTC) Method 1

HVAC system Autonomy Adaptive limiting control algorithm (ALCA) Method 1Autonomy Sensing the internal temperature and humidity, and auto-adjustment of systems (ITS) Method 1Autonomy Sensing of external temperature and humidity, and auto-adjustment of systems (ETS) Method 1Autonomy Automated fault detection (FDD) Method 1Autonomy Self-diagnosis (SD) Method 1Controllability for complicateddynamics

Operation control mechanism (OCM) Method 1,4


Interface with EMS, BAS or IBMS (INTF) Method 1,2

Man–machine interaction Provide management staff with database & analytical tools for operation & service evaluation (DAT) Method 1Man–machine interaction Pre-programmed responses and zoning control (PPR) Method 1Man–machine interaction Graphical representation and real-time interactive operation action icons (GR) Method 1Bio-inspired behaviour Utilize natural ventilation control (UNVC) Method 1,5

Addressable fire detection and alarm system (AFA) Autonomy Alarm deployment algorithm within the building and notification to Fire Department (ADA0 Method 1,6Autonomy Self-diagnostic analysis for false alarm reduction (SD) Method 1Autonomy Self test of sensors, detectors and control points (STS) Method 1Controllability for complicateddynamics

Integration and control of sensors, detectors, fire-fighting equipment (ICSD) Method 1,7




Interact with security systems (INTS) Method 1,7


Interact with HVAC systems (INTHVAC) Method 1,7

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Interact with lift systems (INTL) Method 1,7


Interact with emergency generator systems (INTES) Method 1,7

Man–machine interaction Run continually with minimal human supervision (RC) Method 1,3Security monitoring and access control system (SEC) Autonomy Sabotage proof (SP) Method 1


Dynamic programming (DP) Method 1


Configurable to accurately implement the security policies for the premises (CSP) Method 1


Interface with other system, e.g. communication network, phone system, etc (INTSY) Method 1,8



Man–machine interaction Run continually with minimal human supervision (RC) Method 1, 3Man–machine interaction Provide database and analytical tools for operation and service evaluation (DAT) Method 1Man–machine interaction Pre-scheduled set up (PSSU) Method 1

Smart/energy efficient lift system (LS) Autonomy Auto-controlled navigation at emergency (ACNE) Method 1Autonomy On-line data logging (ONDL) Method 1Controllability for complicateddynamics

Accommodate changes of passenger traffic pattern (ACPTP) Method 1


On-line investigation and analysis of lift activity (ONIA) Method 1



Man–machine interaction Human engineering design (HED) Method 1Man–machine interaction Provide database and analytical tools for operation and service evaluation (DAT) Method 1Man–machine interaction Pre-scheduled of special events and normal routines (PSSE) Method 1

Digital addressable lighting control system (DALI) Controllability for complicateddynamics

Presence detection (PD) Method 1


Control of individual luminaries, groups of luminaries or lighting zone (CIL) Method 1,4



Man–machine interaction Provide database and analytical tools for operation and service evaluation (DAT) Method 1Man–machine interaction Pre-programmed response and control (PPSC) Method 1Bio-inspired behaviour Sensing the light intensity and angle of projection and solar radiation (SLI) Method 1,7Bio-inspired behaviour Automatic lighting or shading controls (AUTLS) Method 1

Computerized maintenance management system (CMMS) Autonomy Automatically generation of routine maintenance work schedule with alert of system contactexpiration (AGRMW)

Method 1

Autonomy Statistical evaluation (SE) Method 1Controllability for complicateddynamics

Deployment mechanism (DM) Method 1


System configuration allows multiple locations, multiple trade, multiple client database (SCM) Method 1

Man–machine interaction Management programming to upkeep changes of labour, work type and material inventory (UC) Method 1Bio-inspired behaviour Diversion of work process on busy schedule (DWP) Method 1Bio-inspired behaviour Interactive communication through system with site workers and operator to maintain up-to-the-minutes status (INC) Method 1

Notes:Method 1: The assessment was based on the existence and level of functions/services. This was rated based on 5-point scale: from 5 marks (Excellent), 4 marks (Good), 3 marks (Fair), 2 marks (Poor), 1 mark (Very Poor), and 0 mark (Not Existence).Method 2: The assessment was based on the percentage of standalone building control systems were linked by IBMS. This was rated based on 5-point scale: from 5 marks (100%–81%), 4 marks (80%–61%), 3 marks (60%–41%), 2 marks (40%–21%), 1 mark (20%–1%), and 0 mark (None).Method 3: The assessment is based on the number of human intervention (permonth): 1 time or below to 30 times or above. This is rated based on 5-point scale: from 5marks (1 time or below), 4marks (1 to 7 times), 3marks (8 to 15 times), 2marks (16–22 times), 1mark (23–29 times), and 0mark

(30 times or above).Method 4: The assessment was based on the existence and level of automatic control. This was rated based on 5-point scale: from 5 marks (Fully automatic control), 4 marks (Automatic-timer control), 3 marks (Timer control), 2 marks (Timer-manual control), and 1 mark (Manual control).Method 5: The assessment was based on the percentage of natural ventilation used compared to the mechanical ventilation. This was rated based on 5-point scale: from 5marks (100%–81%), 4 marks (80%–61%), 3 marks (60%–41%), 2 marks (40%–21%), 1 mark (20%–1%), and 0mark (None).Method 6: The assessment was based on the average response/report time to buildingmanagement and Fire Dept: [5 s or shorter to 2min or longer]. This was rated based on 5-point scale: from5marks (5 s or shorter), 4marks (between 5 s and 45 s), 3marks (between 45 s and 90 s), 2marks (between

90 s and 2 min), and 1 mark (2 min or longer).Method 7: The assessment was based on the percentage of permanently installed devices under control and monitoring (by IBMS). This was rated based on 5-point scale: from 5marks (100%–81%), 4 marks (80%–61%), 3 marks (60%–41%), 2 marks (40%–21%), 1 mark (20%–1%), and 0 mark

(None).Method 8: The assessment was based on the level and scope of system interface. This was rated based on 5-point scale: from 5 marks (100%–81%), 4 marks (80%–61%), 3 marks (60%–41%), 2 marks (40%–21%), 1 mark (20%–1%), and 0 mark (None).

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Appendix B (continued )


Appendix B

Comparison of the relative importance of individualintelligent indicators of the remaining seven intelligent buildingsystems from AHP and ANP approaches

Indicators
Indicators' weight from ANP Indicators' weight from AHP
Telecom and data system (ITS)
CCD IMS 0.1773 0.1927 TCCD 0.2743 0.2981
MMI
FHTP 0.3514 0.3263 SLTC 0.1970 0.1830
HVAC system
BIB UNVC 0.2742 0.2244
AUT
ALCA 0.0263 0.0358 FDD 0.0429 0.0585 ETS 0.0556 0.0759 ITS 0.0709 0.0968 SD 0.0397 0.0542
CCD
INTF 0.1407 0.1489 OCM 0.1356 0.1435
MMI
GR 0.0721 0.0546 PPR 0.0760 0.0575 DAT 0.0659 0.0498
Addressable fire detection and alarm (AFA) system
AUT ADA 0.2081 0.2339 STS 0.1725 0.1939 SD 0.1492 0.1676
CCD
INTF 0.0303 0.0278 ICSD 0.0718 0.0658 INTLG 0.0358 0.0327 INTHVAC 0.0478 0.0438 INTES 0.0365 0.0334 INTS 0.0227 0.0208
MMI
RC 0.2252 0.1802
Security monitoring and access (SEC) system
AUT SP 0.4735 0.5057
CCD
CSP 0.0785 0.0766 DP 0.0395 0.0385 INTF 0.0467 0.0455 INTSY 0.0390 0.0380
MMI
PSSU 0.1165 0.1067 DAT 0.0945 0.0866 RC 0.1118 0.1024
Smart/energy efficient lift system (LS)
AUT ACNE 0.2910 0.2791 ONDL 0.1658 0.1590
CCD
ACPTP 0.1293 0.1342 INTF 0.0589 0.0612 ONIA 0.0713 0.0740
Indicators
Indicators' weight from ANP Indicators' weight from AHP
Smart/energy efficient lift system (LS)
MMI HED 0.1112 0.1148 PSSE 0.0810 0.0835 DAT 0.0914 0.0943
Digital addressable lighting control (DALI) system
BIB AUTLS 0.1747 0.1825 SLI 0.2104 0.2198
CCD
CIL 0.1189 0.1247 INTF 0.1215 0.1274 PD 0.0812 0.0852
MMI
PPSC 0.1881 0.1670 DAT 0.1051 0.0933
Computerized maintenance management system (CMMS)
BIB DWP 0.0888 0.0792 INC 0.1053 0.0940
AUT
AGRMW 0.1846 0.1954 SE 0.1353 0.1433
CCD
DM 0.0844 0.0822 SCM 0.1024 0.0997
MMI
UC 0.2992 0.3063
Note: AUT = autonomy; BIB = bio-inspired behaviour; CCD = controllability ofcomplicated dynamics; and MMI = man–machine interaction.

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