Energy and Buildings 42 (2010) 728–734

Facilities intelligence and evaluation: A multi-criteria assessment approach

Zhen Chen *

School of the Built Environment, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK

A R T I C L E I N F O

Article history:

Received 9 September 2009

Accepted 17 November 2009

Keywords:

Facilities

Intelligence

Buildings

Infrastructures

Automation

Intelligent systems

A B S T R A C T

This paper presents a novel method for evaluating facilities in regard to their designed intelligence.

Facilities intelligence is defined as the designed capacity of a facility to acquire and process data and

information to perform its adaptability to lifecycle circumstance changes in terms of people’s

requirements of wellbeing and energy efficiency. This definition is then formulated to quantify the Index

of Facilities Intelligence, the level of facilities intelligence, and the reliability of facilities evaluation.

STEEP (social, technical, economic, environmental, and political) criteria and their sub-criteria are used

to set up an ANP (analytic network process) model, and ANP result such as synthesized priority weights

is then used to calculate those parameters related to facilities intelligence. An experimental case study is

given to prove the effectiveness of applying the proposed method to evaluate the intelligence of facilities

in practice.

� 2009 Elsevier B.V. All rights reserved.

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1. Introduction

According to Edlund [1], Royal Dutch Shell plc, one of thedefining challenges of our age is finding more energy whileemitting less carbon dioxide in energy-fueled lifestyles. Forprofessionals in the built environment domain, they confrontboth unprecedented environmental and energy-security impera-tives [11,12], and it is becoming increasingly imperative todiminish energy use across the lifecycles of all facilities, includingbuildings and infrastructures, along with traditional and/orinnovative engineering and managerial procedures in productionin the construction sector.

Intelligent facilities, as a new subject term in the builtenvironment domain and a unique group of facilities which areoptimal solutions for people in terms of a higher level of wellbeingand energy-use efficiency [2,13], can be defined based on thedefinitions of intelligent systems allied to all types of buildingsand infrastructures, which are generally regarded as the two mainconceptual components of facilities built for people. In anotherwords, it can be a logical way to describe an intelligent facility byincorporating systems of genuine and artificial computationalintelligence with all functional characteristics of buildings andinfrastructures. Meanwhile, although professionals in the builtenvironment domain are increasing their acceptance of rankingsystems for building’s energy performance, environmentalattributes and emergency preparedness [14], it is also an initiative

* Tel.: +44 7979 830 187.

E-mail address: Zhen.Chen@hw.ac.uk.

0378-7788/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.enbuild.2009.11.012

[15] to develop innovative approaches for various purposes ofevaluation in regard to overcoming weaknesses of rankingsystems [16] and achieving more accurate assessment byeffectively reusing experts knowledge and real-time data to becollected.

This paper presents an innovative approach to evaluate theintelligence level of facilities. This evaluation method is putforward based on (1) the definition of the intelligence of facilities inboth engineering or management perspectives with regard to theircapacity to acquire and process data and information, and theiradaptability to circumstance changes in terms of people’srequirements of wellbeing and energy-use efficiency, (2) a genericset of evaluation criteria to measure social, technical, economic,environmental, and political (STEEP) conditions related to facili-ties’ capacities in acquiring and processing data and information toperform their adaptabilities to circumstance changes, and (3) acybernetic analysis of the system of facilities evaluation to regulateits scope and process in regard to all related inputs and outputs ofthe cybernetic system. In addition, this paper also providesequations to quantitatively evaluate facilities intelligence, and thereliability of evaluation. In formulating the Index of IntelligentFacilities, the analytic network process (ANP) [10] is adopted toobtain synthesized priority weights. Finally, this paper presents anANP model with an experimental case study to demonstrate theprocedure of the evaluation of facilities intelligence.

2. Definitions

As intelligent facilities is a new term comparing with what is sogenerally called intelligent buildings or intelligent infrastructures,

Z. Chen / Energy and Buildings 42 (2010) 728–734 729

it is necessary to define the intelligence of facilities. In addition, it isalso required to define the levels of facilities’ intelligence and thecriteria to reliably measure the intelligence in terms of theevaluation of intelligent facilities.

2.1. The intelligence of facilities

Facilities are physically designed, built and/or installed to servespecific functions for an organisation with convenience or service,and in most cases in the built environment domain they arebuildings, infrastructures, and their functional units. In this regard,the definition of intelligent facilities depends on the features ofthem, and it is therefore essential to define this term based on thedefinitions of its sub-terms, i.e., intelligent buildings and intelli-gent infrastructures.

For intelligent buildings, CABA (Continental Automated Build-ings Association) [3] defined them as buildings that can provide theowner, operator and occupant of a building with an environmentthat is flexible, effective, comfortable, and secure through the useof integrated technological building systems, communications andcontrols.

For intelligent infrastructures, Adeli and Jiang [4] presentedtheir foundations, which focus on smart structures, and intelligenttransportation systems, by using artificial neural networks,wavelets, and chaos theory. For these two specific fields, theyalso emphasized that the premise of intelligent transportationsystems is intelligent and maximum utilization of the existingfreeway systems rather than adding to them thereby minimizingthe destruction of the nature and resulting in environmentallyconscious and sustainable designs and solutions; while smartstructures use intelligent sensors and actuators for healthmonitoring and vibration control of structures under extremeexternal loading.

These definitions representatively described the main twotypes of intelligent facilities by focusing on either the functions ofsuch facilities or the systems to support those functions of suchfacilities. Nevertheless these definitions did not provide an exactdescription about the ‘‘intelligence’’ of facilities. In order to definethe intelligence of facilities so as to evaluate the level of theirintelligence, it is necessary to characterize it based on the nature offacilities’ intelligence by incorporating all their functions orcapacities being supported by adaptable system components. Inother words, the intelligence of facilities can make facilitiesthemselves more adaptable to various changes [2], which consistof not only technological changes but also other changes such asthe environmental changes. In this paper, all these changes areregarded as circumstance changes.

According to the Compact Oxford English Dictionary, intelli-gence is the ability to acquire and apply knowledge and skills. Forfacilities, on the other hand, if we follow this way to define theirintelligence, the adaptability to circumstance changes is one of themost appropriate words to precisely interpret their abilitiesregarding the intelligence of facilities, while relevant data andinformation processed by computer systems of facilities iscomparably identical with knowledge processed by human beings.Therefore the intelligence of a facility can be defined as thedesigned capacity of a facility to acquire and process data andinformation to perform its adaptability to lifecycle circumstancechanges in terms of people’s requirements of wellbeing and energyefficiency.

This definition does not follow the previously adopted criteriaof measuring either building intelligence [2] or infrastructureintelligence [4] in many assessment systems, and it ignores somefactors that are less identifiable in formulating facilities intelli-gence. For example, Barden [2] proposed to use fluid vs.crystallized intelligence factors, including

- General function measure (durability, environmental resource,reliability, and response to environmental change) vs. specificfunction measure (Client’s Specific brief).

- Adaptability (ease of change of use) vs. flexibility (ability torespond to short-term change demands of occupants).

- Capital utilization (initial cost of construction) vs. fixed asset runrate (occupancy fixed costs).

- Environmental impact (energy, water, and pollution) vs. effi-ciency (outputs/inputs ratios, and service changes).

- Social impact (parking, access, and safety) vs. effectiveness(benefits and increased value from occupier satisfaction).

This set of factors, which can also be recognized in many otherbuildings assessment systems including the six systems for theassessment of intelligent buildings as mentioned in the Acknowl-edgement section of this paper, can be regarded as general criteriaderived from both theories and practice of building assessment withmore apt measures about intelligent buildings. However thedefinition of facilities intelligence as given in this paper deliberatelystimulates the comparability between human intelligence andfacilities intelligence in the evaluation so as to incorporate thebeautiful nature of human intelligence into facilities.

2.2. The intelligence level of facilities

For an effective evaluation of the intelligent performance offacilities, this paper proposes a scope of intelligent levels offacilities. In order to define such a scope, it is necessary toformulate the value of facilities intelligence.

The intelligence of one facility can be calculated by the ratioderived from ANP result. And this can be formulated in thefollowing equations:

IIF ¼ 100� el (1)

l ¼ P

100�w1

w0(2)

In Eq. (1), IIF is the value of intelligence of the facility underevaluation, and it is called here the Index of Intelligent Facilities; lis the coefficient of facilities intelligence.

In Eq. (2), P is the reliability of the evaluation of a specificfacility, and it is formulated in Eq. (3); in addition, two synthesizedpriority weights derived through ANP are used together, and theseinclude

- w1, the synthesized priority weight of the facility underevaluation, and

- w0, the synthesized priority weight of the reference facility usedas a comparable one in evaluation.

By using these two equations, an essential scope of the Index ofIntelligent Facilities can be derived.

As shown in Table 1, the minimum value of the Index ofIntelligent Facilities is 100 when l = 0, and the maximum value ofthe Index of Intelligent Facilities is 272 when l = 1. The level offacilities intelligence can be then subjectively measured by a scale ofthe Index of Intelligent Facilities, and the scale is given in Table 2.

According to Table 2, there are five levels of facilities intelligence,and the intelligence level of any facilities can accordingly measuredby the accurate IIF value, which can be gained from the result of ANPmodeling. Comparing with generally adopted rating method ineither buildings or infrastructures assessment, the ANP, as anadvanced generic multi-criteria decision-making approach with theunique capacity to quantitatively measuring interrelations betweenpaired evaluation criteria, now can provide valuable support to thequantitative measurement of facilities intelligence. Further to this

Table 1The Intelligence Index of Facilities.

l el IIF

0.0 1.00 100

0.1 1.11 111

0.2 1.22 122

0.3 1.35 135

0.4 1.49 149

0.5 1.65 165

0.6 1.82 182

0.7 2.01 201

0.8 2.23 223

0.9 2.46 246

1.0 2.72 272

Table 2The intelligence level of facilities.

Levels The nature of intelligence IIF scalea

1 Less intelligent [100, 130]

2 Moderately intelligent [130,160]

3 Intelligent [160,200]

4 Very intelligent [200,240]

5 Extremely intelligent [240,272]

a This scale is subjectively defined.

Table 3The STEEP criteria.

STEEP criteriaa The nature of criteria in evaluation

Social criteria Internal and external social requirements

and impacts related to the intelligent

performance

Technical criteria Engineering specifications

related to the intelligent performance

Economic criteria Lifecycle expenses related to the intelligent

performance

Environmental criteria Internal and external environmental requirements

and impacts related to the intelligent performance

Political criteria Internal and external management specifications

related to the intelligent performance

a Each type of criteria may consists of a number of related sub-criteria.

Fig. 1. A cybernetic system for facilities evaluation.

Z. Chen / Energy and Buildings 42 (2010) 728–734730

innovative solution, facilities can be classified into five specific levelsregulated in Table 2, and it therefore provides much convenient toolin facilities evaluation. For application, Section 5 demonstrates howto get such values in a real evaluation.

2.3. STEEP criteria

For those emphasized circumstance changes with which intelli-gent facilities must be able to deal with, it is generally regarded thatthose changes can be classified under a regime formed with STEEPissues. And it is a general consideration that both intelligentbuildingsand intelligent infrastructure are facilities that compromise withspecific specifications on functional andusable settlements indesign,construction and operation of facilities. However, the STEEP criteria,which are adopted in this paper as a driver in the evaluation offacilities intelligence against circumstance changes aims to explorefrom a traditional understanding of either intelligent buildings orintelligent infrastructures to a more generic view on what really needto be evaluated in terms of facilities intelligence as mentioned inSection 2 above. In another words, it is assumed that intelligentfacilities as built by people should have capacities like human beingwho has natural intelligencetoeffectivelyandefficientlydealwithallcircumstance changes regulated by the STEEP criteria.

The STEEP criteria for evaluating facilities intelligence againstthe circumstance changes across their lifecycles are furtherdescribed in Table 3.

3. Cybernetic analysis

According to the definition of intelligent facilities, it is required toeffectively measure their capacity to acquire and process data andinformation, and their adaptability to circumstance changes in termsof people’s requirements of wellbeing and energy-use efficiency,therefore a cybernetic model is set up to regulate the process offacilities evaluation with regard to a more reliable estimation.

3.1. A cybernetic model

Generally speaking, cybernetics is about having a goal andtaking actions to achieve the goal, and it can be defined as thescience of control and communication of complex systems; and theintelligence, in the field of cybernetics, is determined by observed

conversations or interactions, among various components of thecybernetic system [5]. According to Robert Vallee [6], dynamicalsystems can possess input, state and output, and consequently anevolution equation; and a cybernetic system has an observationalsequence of the inputs, followed by a decisional sequence leadingto the effector organs related to outputs, being well understoodthat the observational sequence allows the system to observe itsenvironment and itself. In the built environment domain,cybernetic analysis has been adopted in decision making forconstruction projects. For example, Chen [7] put forward acybernetic system model of remote intelligent services, i.e., real-time decision-making support for facilities. In this paper, it isassumed that a cybernetic system can be adopted in another areaof facilities management [8,9] for a specific purpose such asevaluating the intelligence levels of facilities, and a cyberneticsystem prototype is illustrated in Fig. 1.

A cybernetic system of facilities evaluation may consist of anumber of specific sub-systems in regard to various areas of

Table 4The components of the cybernetic system.

System components Functions

Facilities work stages Across-stage data and information process for facilities, and knowledge retrieval from professionals.

Functions of facilities management Across-stage decision making, and knowledge retrieval from professionals.

STEEP criteria Quantitative filter to measure facilities adaptability to circumstance changes with regard to professional

judgments and dynamic performances of facilities.

Facilities evaluation An entity to evaluate facilities in regard to their designed capacities to acquire and process data and

information to perform their adaptability to circumstance changes in terms of people’s requirements of wellbeing

and energy-use efficiency.

Connections Inter-linked processes with inputs and outputs for the cybernetic system.

Fig. 2. ANP model for facilities evaluation.

Z. Chen / Energy and Buildings 42 (2010) 728–734 731

activities and knowledge that are related to the goal of facilitiesevaluation; and those sub-systems can be illustrated in differentmanners depending on the direction from which the entirecybernetic system is formed. Depending on the usefulness of acybernetic system model, its sub-systems should be identifiedaccording a specific classification which could provide the mostsuitable description about all relevant components of the system.For example, a cybernetic system of buildings may involve energymanagement, fire detection, security, and transport systems; and itmay also consist of interactions with outside service providers,utilities, load aggregators, and emergency services [5]. In thispaper, it is the functional components related to facilitiesevaluation that are used to illustrate the cybernetic system (seeFig. 1), which is then used as a guide in facilities evaluation.

According to Fig. 1, the cybernetic system of facilities evaluationconsists of four inter-connected sub-systems, and they arefacilities evaluation, STEEP criteria, facilities work stages, andthe functions of facilities management. Table 4 further explainsreasons why these components are adopted in terms of theirfunctions embedded in the entire cybernetic system.

For these sub-systems, they are inter-linked based on theirlogical relations in facilities evaluation process. In addition, thesystem model is simply built up to maximize the reliability ofevaluation by effectively measuring related parameters based onevaluation criteria throughout all related processes of facilitieswork stages. Consequently, the ANP technique, as an advancedgeneric multi-criteria decision-making method [10], is used inSection 5 to demonstrate an evaluation with higher reliability.

3.2. Reliability of facilities’ evaluation

The reliability of such an evaluation is defined here as theprobability of effectiveness in comprehensively reusing highprofessional knowledge in one facility evaluation. Eq. (1)describes how to calculate the value of reliability in facilitiesevaluation:

P ð%Þ ¼ b� n1

N� 100 (3)

In Eq. (3), P is the reliability of the evaluation of one specificfacility, N is the number of professionals whose knowledge havebeen retrieved and used in ANP modeling, n1 is the number ofprofessionals at Facilities Manager level who contribute theirknowledge in ANP modeling, b is a subjective parameter of relianceon professionals’ expertise of facilities management in regard tointelligent systems. For any decision-making exercise, the value ofb is between 0 and 1, and it can be subjectively defined by eitherdecision-makers’ or those professionals’ preference according totheir knowledge of intelligent facilities. Regarding the value of b, itmeans that those professionals may have reliable expertise in theevaluation of facilities intelligence if it is close to its upper limit,i.e., b = 1; while it means that those professionals may have quitelimited expertise in intelligent facilities if it is close to lower limit,i.e., b = 0. And the evaluation should be totally unreliable if b = 0.

4. Evaluation model

A multi-criteria decision-making model is proposed in thispaper by using ANP to realize holistic capacity evaluations forspecific facilities. There are some reasons regarding why the ANP isadopted to evaluate facilities intelligence. Despite the purpose ofinnovation in research and development, the following reasons aresignificant [7,10,16]:

- The ANP is a unique tool to effectively quantify the interrelationsbetween paired evaluation criteria,

- results from ANP modeling such as the synthesized priorityweights can be further applied for indexes,

- the ANP has been applied in intelligent buildings evaluation, and- the ANP can facilitate effective reuse of experts’ knowledge

through consolidation processes.

4.1. ANP model

An ANP model is designed here to effectively measure facilities’capacities to acquire and process related data and information toperform their adaptability to circumstance changes in terms ofpeople’s requirements of wellbeing and energy-use efficiency. AsSTEEP criteria are regarded as quantitative filter to measurefacilities adaptability to circumstance changes with regard toprofessional judgments and dynamic performances of facilities,they are selected to be five clusters of the ANP model (see Fig. 2).

As illustrated in Fig. 2, the ANP model consists of six clusters,including Options, Social criteria, Technical criteria, Economiccriteria, Environmental criteria, and Political criteria. Excluding theOptions cluster, the model integrates five STEEP clusters with 14nodes (see Table 6), and all these sub-criteria need to have definitevalues prior to pair-wise comparisons regarding different relevant

Table 5Judgments between paired clusters/nodes.

Scale of pair-wise comparisons Clusters/nodes

Cluster I Node Ii

Cluster J Node Jj

�1 � ��2 � ��3 � ��4 � ��5 � ��6 U U

�7 � ��8 � ��9 � �

Notes: The fundamental scale of pair-wise judgments: 1, not important; 2, not to

moderately important; 3, moderately important; 4, moderately to strongly

important; 5, strongly important; 6, strongly to very strongly important; 7, very

strongly important; 8, very strongly to extremely important; 9, extremely

important. The symbol � denotes item under selection for pair-wise judgment,

and the symbol U denotes selected pair-wise judgment. I and J denote the number

of clusters, while i and j denote the total number of nodes. The symbol � denotes

importance initiative between compared nodes or clusters.

Z. Chen / Energy and Buildings 42 (2010) 728–734732

characteristics of each option under evaluation. On the other hand,the Options cluster, which is one essential cluster of any ANPmodel, is used in this paper for an experimental case study tocontain alternative plans to be evaluated against evaluationcriteria; and in the mentioned case study there are two nodes,which represents two alternative plans for a commercial complexproject (see Section 5).

In addition to these clusters and their nodes, two-way andlooped arrow lines of the ANP model (see Fig. 2) describe possibleinterdependences that exist between paired clusters as well asnodes. In other words, there are fixed interrelations betweenpaired clusters as well as paired nodes of one cluster or from twodifferent clusters.

In order to quantitatively measure all interrelations inside theANP model, questionnaire survey in regard to pair-wise compari-son of relative importance between paired clusters as well as nodesis normally required. By using questionnaire survey, it can beexpected that experts’ knowledge in regard to each specific domainis collected and then converged into an ANP model. As a result, theANP model can perform as a decision-making support tool basedon knowledge reuse.

The beauty of ANP method is that it can provide an effectivemechanism for decision-makers to quantitatively evaluate inter-relations between either paired criteria or sub-criteria; and thismakes it possible for decision-makers to reuse expertise forfacilities in regard to the evaluation of their designed intelligence.

4.2. Pair-wise comparisons

In order to quantify all possible interdependent links inside theANP model as illustrated in Fig. 2, a pair-wise comparison processis then adopted using subjective judgments made according to thefundamental measuring scale of pair-wise judgments defined bySaaty [10].

Table 5 gives a general description about how to conduct pair-wise comparison between paired clusters or nodes in regard totheir interdependences defined in one ANP model (see Fig. 2) andrelative importance based on their specific characteristics andexperts’ opinion. In this paper, the ANP model is set up based onauthors’ knowledge regarding the STEEP criteria, which is used tomake judgments in quantifying interdependences for all evalua-tion criteria inside clusters 1–5 excluding alternative options incluster 0, and specific characteristics of alternative plans (see Table6), which is used to make judgments in quantifying interdepen-dences for alternative options in the experimental case study.

Table 6Assumptions of alternative development plans.

Criteria clusters Sub-criteria

1 Social criteria 1.1 Public satisfaction

1.2 Cultural compatibility

1.3 Community acceptability

1.4 Workforce availability

2 Technological criteria 2.1 Preparedness to site conditions change

2.2 Integration of multiple functionality wit

2.3 Easiness in facilities management

2.4 Easiness and security in accessibility an

3 Economic criteria 3.1 Preparedness to demand and supply cha

3.2 Possibility to maximize lifecycle value

3.3 Accessibility in local area

4 Environmental criteria 4.1 Degree of eliminating environment imp

4.2 Preparedness to climate change

5 Political criteria 5.1 Fitness to management specifications

Notes: Plan A, a retail-led mixed-use inner CBD development and Plan B, a scenario pl

5. Experimental case study

After the ANP model is set up based on the definition of facilitiesintelligence, it is necessary to demonstrate whether it is effectiveto use ANP to evaluate the relative intelligence level of a specificfacility, therefore an experimental case study is thus conductedbased on information collected from an ongoing urban regenera-tion project in Liverpool; and some scenarios such as alternativeplans for this commercial complex in regard to the requirements ofcomparison study using ANP as well as features of intelligentfacilities are made accordingly.

5.1. The project and scenarios

The commercial complex, which is one of the largest urbanregeneration projects in Europe, locates in central Liverpool. Thesite area is about 40 acres and it is between main retail areas, innercentral business district, residential areas, walk streets, mainroads, and the old dock along the River Mersey.

The Developer works closely in partnership with the CityCouncil to revitalize this deprived area not only for short-termattractions such as the local event of European Capital of Culture in

Unit Alternative plans

Plan A Plan B

% 90 100

% 80 80

% 90 90

% 90 80

% 30 60

h ICT systems % 70 90

% 90 100

d evacuation % 90 100

nges % 100 70

% 50 60

% 90 80

acts % 50 80

% 40 80

% 80 90

an based on Plan A with advanced features of intelligent facilities.

Z. Chen / Energy and Buildings 42 (2010) 728–734 733

2008 but also for long-term urban renaissance in regard toNorthwest regional economic strategy under sustainable develop-ment regime in Merseyside.

In regard to the purpose of this experimental case study, twodevelopment plans are considered, including

- a retail-led mixed-use inner CBD development (called Plan A),which is based on the commercial complex itself and

- a scenario plan based on Plan A (called Plan B), which has similarfunctions in commercial use but has more advanced features ofintelligent facilities.

The scenario plan is made also based on the philosophy of localurban renaissance, which aims to attract back to Liverpool a higherproportion of catchment population currently lost to outer retailparks and shopping centres such as the Trafford Centre, and tomaximize the use of current and future transport infrastructuressuch as the Merseytram, focused on the Liverpool City Centre [17].Accordingly, specific assumptions are made in regard to char-acteristics of a retail-led mixed-use inner CBD development andthe advance of intelligent facilities to be applied in such a largecommercial complex. Moreover, in order to make more reasonableassumptions, relative information from other real projects of thesame type is considered. For example, as the common network IPinfrastructure is a true integration of building automation andinformation technology systems and signifies a step change in theevolution of intelligent buildings in the retail sector [18], it isfurther adopted as key assumption under technological criteria.Based on the scenario of two alternative plans for the specificcommercial complex, details of those assumptions are summa-rized in Table 6.

5.2. ANP modeling

As mentioned above, although interdependences among the14 evaluation criteria can be measured based on experts’knowledge, the ANP model should comprehend all specificcharacteristics of each alternative plan, which are given in Table6. According to the fundamental scale of pair-wise judgments(see Table 5), all possible interdependences between eachalternative plan and each evaluation criterion, and betweenpaired evaluation criteria in regard to each alternative plan areevaluated.

The result of all pair-wise comparisons is then used to form atwo-dimensional super-matrix for further calculation. Thecalculation of super-matrix aims to form a synthesized super-matrix to allow for the resolution of the effects of theinterdependences that exists between the nodes and the clustersof the ANP model.

In order to obtain useful information for development planselection, the calculation of super-matrix is conducted followingthree steps, which transform an initial super-matrix, or un-weighted one based on pair-wise comparisons to a weightedsuper-matrix, and then to a synthesized super-matrix. Resultsfrom the synthesized super-matrix are given in Table 7.

Table 7Evaluation of facilities intelligence.

Results Plan alternatives

Plan A Plan B

Synthesized priority weights from ANP 0.40 0.60

Index of Intelligent Facilities 188 –

Level of facilities intelligence 3 –

Reliability of evaluation (%) 95 –

5.3. Level of facilities intelligence

As a summary, evaluation results are given in Table 7. For thisexperimental case study, the ANP model is set up based on pair-wise comparisons by the author himself and therefore thereliability of evaluation is subjectively given as 95%. Other resultsincluding the Index of Intelligent Facilities and the level offacilities intelligence are derived from calculation by usingequations formulated above and the synthesized priority weightsfrom ANP for the two plans.

6. Conclusions

This paper introduces a novel quantitative approach to theevaluation of facilities intelligence against STEEP criteria. A set ofevaluation methodology is presented in order to formulate theIndex of Facilities Intelligence, the level of facilities intelligence,and the reliability of one evaluation. An ANP model is then set upand used in an experimental case study on an urban regenerationproject in Liverpool in order to demonstrate the effectiveness of thedescribed methodology.

In terms of facilities intelligence, this paper provides acomprehensive definition, which emphasizes their designed capac-ities to acquire and process data and information to perform theiradaptability to circumstance changes in terms of people’s require-ments of both wellbeing and energy-use efficiency. Comparing withother related definitions about intelligent buildings or infrastruc-tures, this definition focuses on a comparable understandingbetween human intelligence and facilities intelligence. Therefore,it is assumed that this definition together with the methodology ofevaluation in terms of facilities intelligence is a unique complementto generally used rating method in facilities evaluation.

As the ANP model is set up by the author only at pilot studystage, further research may carry on to quantitatively assesssub-criteria; and the model will be further developed based onquestionnaire survey into a larger group of experts in facilities.

Acknowledgments

This paper is written for a proposed and approved session onintelligent facilities for the conference. There are many people whohave contributed to this paper. First, thanks to Professor Derek J.Clements-Croome at the University of Reading for leading me tothe solution-oriented research into intelligent buildings. Thanksalso to Professor Heng Li at the Hong Kong Polytechnic Universityfor leading me to use ANP to deal with decision-making problemsin construction management.

The ANP model is set up using Super Decisions software. Theprogram is written by the ANP Team, working for the Creative

Decisions Foundation; and it implements the ANP theory developedby Professor Thomas Saaty [10].

It has been noted that there is a module called IntelligentFacilities that is being taught at the National University of Singaporeafter the ‘‘facilities intelligence’’ is formulated in this paper.

Several evaluation systems for intelligent buildings have beenreviewed prior to the definition of facilities intelligence in thispaper, and those systems are

- IB Index, developed by Asian Institute of Intelligent Buildings(AIIB), Hong Kong, China;

- MATOOL—a matrix tool for assessing the performance ofintelligent buildings, developed by Building Research Establish-ment Ltd. (BRE), UK;

- Building Intelligence Quotient (BIQ), developed the BIQ Consor-tium of Continental Automated Building Association (CABA),Canada;

Z. Chen / Energy and Buildings 42 (2010) 728–734734

- Assessment Standards for Certifying Intelligent Buildings,developed by Intelligent Building Society of Korea (IBSK), Seoul,Korea;

- Intelligent Building Rating System, developed by ShanghaiConstruction Council, Shanghai, China;

- Intelligent Building Assessment System, developed by theArchitecture and Building Research Institute, Ministry of theInterior, Taiwan, China.

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