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Building asset management with deficiency trackingand integrated life cycle optimisationAhmed Elhakeem a & Tarek Hegazy ba Civil Engineering Department , Helwan University , Cairo , Egyptb Civil Engineering Department , University of Waterloo , Waterloo , Ontario , N2L 3G1 ,CanadaPublished online: 19 May 2010.

To cite this article: Ahmed Elhakeem & Tarek Hegazy (2012) Building asset management with deficiency tracking andintegrated life cycle optimisation, Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Designand Performance, 8:8, 729-738, DOI: 10.1080/15732471003777071

To link to this article: http://dx.doi.org/10.1080/15732471003777071

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Building asset management with deficiency tracking and integrated life cycle optimisation

Ahmed Elhakeema and Tarek Hegazyb*

aCivil Engineering Department, Helwan University, Cairo, Egypt; bCivil Engineering Department, University of Waterloo,Waterloo, Ontario, N2L 3G1, Canada

(Received 9 July 2009; final version received 30 January 2010; accepted 15 March 2010; published online 19 May 2010)

This paper introduces a comprehensive building asset management system with a unique formulation in which allfunctions from inspection, to deterioration modelling, and life cycle analysis, track the dynamics of buildingdeficiencies. The system also incorporates an integrated life cycle analysis that determines both the optimum repair-types and the optimum repair-timings for a large network of buildings with hundreds of components. To handle thelarge-scale optimisation involved, a two-phase optimisation procedure has been introduced and its powerfulperformance validated on various size networks. The paper provides a description of the proposed assetmanagement system and discusses its implementation in a user-friendly prototype that suits a large school board inNorth America. The proposed asset management system is innovative and helps organisations with large buildingassets improve the overall condition of their inventory with highest return on the limited repair budget.

Keywords: buildings; asset management; computer application; optimisation; life cycle analysis; capital renewal

Introduction

Sustaining the serviceability and safety of infrastruc-ture networks is a highly challenging task, particularlyunder stringent budgets. Various asset managementtools have therefore been introduced to help assetmanagers in the difficult decisions regarding how andwhen to repair/replace their existing building stockcost-effectively.

In general, large building owners have two functionsto care for their asset inventory: preventive/reactivemaintenance; and capital asset renewal (Figure 1).Whilemaintenance functions support day-to-day operations,capital asset renewal upgrades or completely replacesthe asset, or some of its components. To support capitalrenewal decisions, existing asset management toolseither focus on a certain type of assets (e.g. buildings)or a group of similar components (e.g. roofs only). Theengineered management systems (EMSs) implementedby the USA Corps of Engineers, for example, handleindividual asset types, e.g. PAVER (Shahin 1992),ROOFER (Bailey et al. 1989), and BUILDER (Uzarski2002). Other general purpose systems, e.g. ReCAPP(PPTI 2006) and TOBUS (Brandt andRasmussen 2002)are also available commercially. A survey among assetmanagement systems that are used at themunicipal level(Halfawy et al. 2005) revealed that the vast majority ofsuch systems focus primarily on supporting day-to-daymanagement activities, and only an extremely smallnumber of them offer limited support for long-term

renewal planning. Also, many fundamental assetmanagement functions, such as performance modellingand repair prioritisation, are not supported by mostof these systems. While existing systems involve partialsolutions such as condition assessment surveys, CADand/or GIS, there still exists considerable difficultiesrelated to the formulation of an integrated lifecycle analysis that considers both repair-type decisionsand repair-timing decisions within an optimisationframework.

This paper focuses on supporting a generalisedasset management strategy for buildings. In additionto improving the inspection and deterioration model-ling processes, the paper addresses the complexity inincorporating a large number of components and inintegrating repair-type and repair-timing decisions intoa unified life cycle analysis.

Proposed asset management framework

The proposed asset management system that supportscapital renewal decisions incorporates four mainfunctions (Figure 2):

(1) Accurate inspection and assessment of thecurrent condition of all building components.

(2) Predicting the future conditions of thesecomponents along a five-year planning horizon.

*Corresponding author. Email: tarek@uwaterloo.ca

Structure and Infrastructure Engineering

Vol. 8, No. 8, August 2012, 729–738

ISSN 1573-2479 print/ISSN 1744-8980 online

� 2012 Taylor & Francis

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(3) Proposing repair solutions that suit the dete-rioration trends of asset components.

(4) Life cycle optimisation to prioritise the compo-nents for repair purposes, under budgetconstraints.

Details on these functions are explained in thefollowing subsections along with the description of aprototype system developed for the Toronto DistrictSchool Board (TDSB), which administers more than600 school buildings.

Condition assessment

Condition assessment can be performed using variousmethods including:

. visual inspection,

. photographic and optical methods,

. non-destructive evaluation methods, and

. smart sensors (Hudson et al. 1997).

Among these methods, visual inspection can beconsidered as the most suitable approach for themajority of building components.

For a detailed condition assessment, the inspectorevaluates the severity of various deficiencies associatedwith the component being inspected or each individualinstance of that component (e.g. Boiler no. 1, orGymnasium roof, etc.). Instances could be a pre-defined standard part of the building hierarchy (e.g.Figure 3) or could be defined by the user (inspector) torepresent one or more objects that belong to a certaincomponent type. As shown in Figure 3, an example ofa component type is ‘Wood-windows’ with the typicalwindow deficiencies shown. An example instance ofthat type is ‘Wood-windows on east side of thebuilding’, with the severity of each deficiency (frame

problem, hardware problem, glazing problem, etc.)recorded during inspection. It is noted that the list ofpossible deficiencies for a certain component is notlimited to physical problems only, but can also includedeficiencies related to inability to meet energy, service,human, or any other parameters (as applicable to thecomponent).

Based on the inspector-entered severity level ofeach deficiency, it is possible to compute the overalldeterioration index (DI) of an instance using Equation(1). The DI values vary from 0 to 100; where a DIvalue of 0 implies no deterioration (i.e. excellentcondition), while a DI of 100 implies an extremelycritical condition.

DI ¼

Pd

i¼1Wi � Si

100ð1Þ

where (Si)s are the inspected severities for d deficien-cies, on a scale from 0 to 100, and (Wi)s are the weightsof the various deficiencies, which reflect their relativeimpacts on the overall condition of the instance. Forthis research, complete list of deficiencies for allbuilding components were obtained from the TorontoDistrict School Board (TDSB), with their weightsdetermined through surveys among experienced build-ing inspectors and operators at the TDSB.

Deficiency tracking

It is important to note that existing asset managementsystems do not calculate a deterioration index (DI).Rather, they calculate a condition index (CI) based onthe inspected severities (Uzarski 2007), where CI100–DI. Also, in current methods, the role of theinspected severities stops at calculating the CIs. In this

Figure 2. Components of the proposed asset managementsystem.

Figure 1. Asset management dimensions.

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research, on the other hand, DI not CI, is used sincethe deficiencies directly contribute to deterioration.Also, in the proposed framework, the role of theinspected severities goes beyond inspection. Since thedeficiencies with highest severities directly indicatespecific areas of needed repairs, it is reasonable toformulation repair decisions as a function of theseseverities. In addition, it is also reasonable to track thevarious changes to these deficiencies as a function ofany repair-type and repair-timing. Such a dynamicprocess of tracking deficiencies in the proposed assetmanagement system becomes a unique approach tosynchronise decisions among the various functions ofthe system. It ties inspected deficiencies to predictedfuture deficiencies, repair-type, repair-timing, repair-cost, to after-repair condition improvement, andaccordingly to the yearly repair-fund allocation. De-tails of the process are explained in the followingsubsections along the description of the system’svarious functions.

Instance deterioration modelling

While current condition (i.e. deficiencies’ severities)can be accurately inspected, future condition (futureseverities) of any instance is difficult to predict and isbasically a function of aging, operational conditions,maintenance history, etc. Various deterioration mod-els, therefore, have been proposed in the literature andused in asset management systems (Madanat et al.1995, Madanat et al. 1997, Morcous et al. 2002).

In the proposed system, actual data have been usednot only to develop a generic model for predicting thedeterioration of a component (e.g. roof), but also tocustomise the generic model to each instance of that

component (e.g. ‘Gymnasium roof for school no. 9’).Considering the window component, as shown inFigure 4, the Markov Chain approach was used togenerate deterioration curves. First, the 2003 TDSBinspection data of similar components (e.g. allaluminium windows) were collected from differentbuildings. Afterwards, a custom deterioration curve isdeveloped for each individual instance, knowing itsspecific inspection history. An example instance is thealuminium windows on the east-side of a specificschool (Figure 4(a)). The Markov chain optimisationmodel of Elhakeem and Hegazy (2005b) was then usedto determine the values in the transition probabilitymatrix (TPM) that generate a deterioration curve(dark black) that best corresponds to the data trend,and the previously inspected DIs (two points shown).This approach to deterioration modelling is moreadvantageous than using a generic deterioration curvefor aluminium windows, as it uses few inspection datato customise the generic model to consider thevariability among the specific operational environ-ments of various instances. Once the custom deteriora-tion curve is developed, it can be used to predict(extrapolate) the DIs in future years of the planninghorizon (dotted line in Figure 4(a)). These future DIsrepresent the deterioration indexes under no-repairs.

In the second step of the deterioration model, thepredicted future DI from step 1 needs to be convertedinto their corresponding deficiency severities. Since DIincreases with time (i.e. condition worsens), thedeficiencies are expected to reflect increase in theseverities. The calculation of the future severities,however, is not straightforward. One practical ap-proach used in this research is to increase the inspectedseverities (year 1 in Figure 5) proportional to their

Figure 3. Instances of a building component.

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relative values, so that Equation (1) produces thepredicted (DI) in future years. In future research, it ispossible to improve this deterioration model further, asdiscussed later. Once the future severities are deter-mined, they represent defined deficiencies that need tobe repaired in the future (Figure 5).

Integrated life cycle analysis

Given a limited repair budget and a huge number ofbuilding instances (TDSB, for example, defines about100 instances per school), the decisions of whichinstances to repair; repair types; and repair timingsbecome very difficult. For example, for a small networkof 10 buildings, each having 50 instances with average of

four deficiencies each, then the search space for anoptimum solution is basically 21065064 or 22000, which isextremely large and prohibitive. To facilitate thesedecisions for a large-scale network of assets, the analysishas been carefully segmented into two phases to reduceproblem size, without compromising the two objectivesof best network condition and also best value formoney. The proposed integrated life cycle analysistechnique is illustrated in Figure 6.

In the proposed life cycle approach, the analysis isperformed in the first phase at the instance level. Givena 5-year planning horizon, each instance undergoes fivesmall optimisations (one for each year), to determinethe best repair scenario in that year (cheapest repairs tokeep the instance at acceptable condition). As such,each small optimisation is only for one instance, oneyear. The resultant of all small optimisations is a poolof six best-repair alternatives (in years 1 to 5, plus a do-nothing) for each instance. The whole pool is then usedas input to a network-level optimisation that decidesthe best repair-timings (i.e. selects one of the sixalternatives for each instance) so that the overallnetwork condition is maximised over the planninghorizon. Details of the analysis are provided in thenext subsections.

Instance-level: Generating a pool of best-repair scenarios

Using the list of deficiencies and their severities for anyinstance, it is possible to define a repair scenario (RS) asa combination of actions towards repairing thesedeficiencies. For example, if a component has fourdeficiencies (D1, D2, D3, and D4), one possible repairscenario is represented in the binary form as (1, 1, 0, 1),which implies repairing deficiencies 1, 2, and 4 andkeeping deficiency 3 without repair (Figure 7). If thenumber of deficiencies is d, then the possible number ofrepair scenarios in any year is equal to 2d. All possiblescenarios are assumed to be valid and feasible (a filter tocheck the practicality of any repair scenario can beeasily incorporated). It is noted that in this representa-tion, component replacement is basically a special repairscenario where all the deficiencies are repaired (1, 1, 1,

Figure 5. Sample results of the deterioration model for an instance.

Figure 4. Process for instance deterioration prediction.

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1). Among the various scenarios, including full replace-ment, it is therefore necessary to identify the cheapestscenario that brings the condition of the instance to anacceptable level. To facilitate this analysis, it isnecessary to use a cost estimation model and also amechanism to estimate the resulting improvement in theinstance condition due to the repair.

For each repair scenario (RS), it is possible tocalculate its repair cost as a percentage of the totalinstance replacement cost, based on two simpleassumptions:

(1) repair cost for a deficiency (as a percentage offull replacement cost) is proportional to theweight of this deficiency; and

(2) repairing one deficiency individually will cost25% more than its share of replacement cost.

Based on these two assumptions, the total cost (TC) ofany repair scenario (as a percentage of the instancereplacement cost) can be calculated by summing thecosts of repairing all the deficiencies of this scenario(Equation (2)), as follows:

TCð%Þ ¼Xd

i¼11:25�Wi

�RSi ð2Þ

An example of the analysis is shown in Figure 7,where a repair scenario is associated with a total costof 75% of the instance replacement cost (calculatedusing Equation (2)). To convert repair cost to actualdollars, Equation (3) is used as follows:

$IRCj ¼ TCj � Zj � $CRC ð3Þwhere $IRC is the instance repair cost in dollars, TC isthe repair cost (obtained from Equation (2)) as apercentage of instance replacement cost, Z is theinstance relative size which is a characteristic of theinstance (e.g. 20%of roof area will be re), and the $CRCis the total replacement cost of the component (e.g. allthe roof). The $CRC can be obtained from theorganisation’s replacement cost tables per unit (a unitcan be square foot of gross school area, or square foot ofeducational area, or each). At the TDSB, for example,the $CRC replacement cost for a roof component is$8.04/ft2 of educational area.

In addition to estimating the cost of a repairscenario, it is possible to estimate the after-repair DI(ARDI). This is easily accomplished by looking at therepair scenario and assigning 0 severities to thedeficiencies that will be repaired (last column in Figure7). Accordingly, carrying out a repair scenario (RS) inyear k, the instance’s after-repair condition (ARDIk) iscalculated using Equation (4):Figure 6. Integrated life cycle analysis approach.

Figure 7. Analysis of a repair scenario.

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ARDIk ¼

Pd

i¼1Wi � Sik � ð1� RSiÞ

100ð4Þ

In the example repair scenario in Figure 7, ARDI3is 10.4, which is a great improvement on the 39.3deterioration index (DI) before repair. Upon formulat-ing the analysis of any repair scenario, it is possible toanalyse all the 2d repair scenarios in each year usingoptimisation to determine the best repair scenario forthat year, and repeat the process for all the 5 years. Theoptimisation variables are the repair options (1 or 0)associated with the deficiencies. The objective functionis to minimise the repair cost (Equation (2)) to fewerthan two constrains:

(1) the after-repair deterioration ARDI should beless than or equal to a desired value (acceptabledeterioration level); and

(2) the cost should not exceed a given limit. In thisformulation, the cheapest scenario that meetsthe acceptability level is determined.

It is noted that, for practicality, once the optimumscenario is determined, its cost is compared with thefull-replacement cost before a final decision is made. Inthis study, a repair scenario costing more than 75%(user can change this value) of replacement cost willjustify replacement instead.

As an alternative to the proposed approach ofminimising cost, the algorithm allows the user tomaximise the benefit/cost (B/C) ratio of the repairs.This, however, may increase spending in order to keep itat best condition. It is also noted that the developedmodel does not use a fixed value for the acceptabledeterioration constraint used in the optimisation.

Rather, a more practical approach is used to set thelimit relative to the importance of the instance(component relative importance factors (RIF) weredetermined from surveys at the TDSB). The RIF valuesrange from 100 (most important) to 0 (least important)and reflect the component’s impact on safety, function-ality, and other components. The higher the RIF of acomponent, therefore, the more desire to repair thecomponent to a better condition, as such, the constraintfor each component was set as (100-RIF).

Because of the small size analysis involved,optimising the repair scenario for an instance in agiven year becomes fast and easy to do. In theproposed system, an Excel model has been developed(Figure 8) and the built-in optimisation Solverprogram proved to work efficiently.

Network level: Prioritisation and fund allocation

The small instance-level optimisations, discussed ear-lier, produce best potential repair actions for eachinstance in alternative years 1 to 5. Added to these, ano repair (0) decision is also an option. It is possiblethen to consider this pool of best repairs as inputs to anetwork-level analysis to determine the best year torepair each instance. A network-level optimisation hastherefore been developed as a second analysis phase.As the optimisation will cover a large network ofassets, it is expected to be a large-scale optimisationproblem. Hence, a non-traditional optimisation tech-nique, genetic algorithm (GA), is used.

To formulate the network-level optimisation, it isimportant to quantify both the benefit gained from adecision (selecting an instance for repair at a specificyear), as well as the cost involved. In terms of benefit, itis possible to calculate a representation of the impactof selecting an instance (j) to be repaired in year (k),

Figure 8. Optimisation model to determine the best repair scenario.

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termed the ‘expected performance’ (EPjk) of thisinstance, and evaluated at the network-level. Suchperformance embodies the impact of repair timing onthe deterioration before-repair, the improvement dueto repair, and the deterioration in future years. Assuch, the expected performance is quantified as theaverage of the DI values of the various years (as shownin Figure 9), using Equation (5), as follows:

EPjk ¼ Average ðDIiÞj 8 i 2 Planning Horizon ð5Þ

As shown in Figure 9, in the case of no repair, theexpected performance (average of all values shown) is60, which indicates high deterioration. If this instanceis repaired in the fifth year, the expected performancebecomes 51.7, showing little improvement. However, ifthe instance is repaired in the third year, its expectedperformance improves to become 35. As such, theexpected performance represents a single measure ofthe impact of a repair on the planning horizon. Thismeasure, therefore, becomes useful at the networklevel. It is noted that once the expected performance ofan instance is calculated, it can be rolled up to calculatea building performance, from its individual instances.

Assuming that the repair-year decisions for theinstances become known (after network-level optimi-sation, discussed later), the consequent networkdeterioration index (DIN) can be calculated, on a scalefrom 0 to 100, by averaging the instances’ expectedperformances (EPs), weighted by their relative im-portance factors, as shown in Equation (6):

DIN ¼

Pj

RIFj � EPjk

� �

Pj

RIFj8 j 2 the Network ðNÞ ð6Þ

The network condition (DIN), as such, reflects theimpact of the instances’ repair timings on the overallnetwork. It is important to note that as the decisionsregarding the year of repair of each instance directlyimpact the network (DIN), it also accumulates repaircosts for each year of the planning horizon. Tooptimise network-level decisions, the objective

function is to minimise the overall network deteriora-tion index (Equation (6)) by selecting the best year torepair each component. The yearly budget limitsrepresent the main constraint for the optimisation(Equation (7)), where the sum of costs for all instancesrepaired in year k which should be less than the yearlybudget in that year ($Bk), where $IRCjk is theinstance’s repair cost in year k, calculated usingEquation (3).

X

j

$IRCjk � $Bk 8 k ð7Þ

It is possible to design and implement network-level optimisation in different ways; each has its uniqueformulation and corresponding solution quality. Threealternative designs (Figure 10) have therefore beeninvestigated. The first formulation is an integerrepresentation of N variables (for N instances), whereeach variable can take one of six values (0 to 5) of therepair year. Alternatively, the second formulation is asimple and linear binary representation that considersall years at the same time. This, however, increases thenumber of variables to 6N. To reduce search space,therefore, a year-by-year binary representation wasdesigned as shown in Figure 10 (formulation 3), wherefive yearly optimisations of a much smaller searchspace (2N) are used to determine the best decisions foreach year, consecutively, that maximise the overallnetwork condition at the end of the plan. One of themajor benefits of this representation is that after theinstances are selected in one year, they do not enterinto competition in further years. As such, the yearlynetwork optimisations become less in size, year afteryear. Also, the optimisation maintains focus on oneobjective of maximising overall network condition,without tradeoff, since the pool of decision options(repairs) was already optimised at the instance level interms of cost efficiency.

Once formulation 3 for the optimisation design wasdecided, it was implemented in a convenient spread-sheet format, as shown in Figure 11, and was linked toall system components through a user-friendly inter-face. Powerful commercial GA software (Evolver

Figure 9. Expected repair impact over the planning horizon.

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1998) was also linked to the system through visualbasic code to carryout the optimisation processing. Asshown in Figure 11, the cells that represent thevariables need to be filled with 1s or 0s. Also, thenetwork condition (overall deterioration level) involvesonly simple multiplication and summation functions,which greatly simplify the optimisation process. Oncethe user selects the list of instances, optimisation setupof the variables and constraints becomes fullyautomated.

To experiment with the system data were collectedfrom the TDSB related to 800 instances from 40schools. The instances represent the top componentsof concern to the TDSB, including windows, roofing,boilers, and fire alarm systems. Once this data wasentered and processed by the system’s earlier modules,various optimisation experiments were conducted. A$10 million dollar yearly budget limit (not muchrestrictive) was used for the experiments to allownoticeable performance improvement to be made.Before optimisation, the overall network conditionwas 54.32. To try improving network condition basedon priority ranking, first, the instances were rankedbased on their weighted before-repair condition(column (a) in Figure 11) and repairs were allocatedto top deteriorated instances in each year, succes-sively, until the budget limit was exhausted. The resultof this process (often is the only mechanism used byasset owners) is improved network condition to adeterioration level of only 44.89 (lower number meansless deterioration). An experiment was then conductedon the 800 instances and the year-by-year optimisa-tion improved the overall network condition muchfurther, to a deterioration level of 33.18, as shown inFigure 11. Optimisation took an average of 5 minuteseach year to reach this result. This shows thatoptimisation substantially improved network-leveldecisions.

The results of many other optimisation experimentson networks with larger numbers of instances are

summarised in Figure 12. To facilitate comparison ofresults, larger-size networks were created by repeatingthe 800 instances several times, and adjusting thebudget limit accordingly. As expected, there has been anoticeable degradation in optimisation performance asthe network size increases (search space gets exponen-tially larger). Despite the degradation, optimisationperformance was still better than simple ranking for upto 8000 instances.

To put the optimisation performance in perspective,for 800 instances it takes double the budget for thesimple ranking process to achieve the improved networkcondition of the optimisation. At 5000 instances also,optimisation provides 10% improvement than simpleranking (i.e. assumed 10% reduction in budget). It isnoted that 8000 instances can cover a large number ofassets (hundreds of buildings), particularly when con-sidering one or two types of components only (e.g. aplan for renewing electrical and mechanical compo-nents only), or a smaller subset of the buildings, bygeographical location for example.

Discussion and future extensions

After the analysis was carried out, an effort was madeto test the results against a set of logical trends. Figure13 shows a portion of a simple statistical analysis of theresults. As shown in the left side of the figure, thepercentages of instances with high, medium, and lowimportance that were not repaired are shown andindicate that none of the instances with high impor-tance were omitted. The right side also shows that alarge percentage of the instances with high importanceinstances are selected for repair in year 1 (the rest ofthese high importance instances are repaired in thesecond year). It can be concluded from this simple testthat the optimisation provides reasonable selectionsfor the instances to be repaired in various years of theplan. Such a simple test is beneficial and indicates theimportance of establishing standard metrics to judge

Figure 10. Alternative formulations for network-level optimisation.

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the performance of asset management systems, whichis beyond the scope of this paper.

Due to its unique formulation of integrated instance-level and network-level decisions, the proposed frame-workproved to be an efficient building assetmanagementsystem. It has a unique focus on tracking the dynamics ofdeficiencies in the various building components and inoptimally repairing these deficiencies. It also considersthe practical situation where asset replacement, asopposed to repair, is the most viable option. It is notedthat the design of the proposed framework allows it to bea fully independent system or can collaboration withother existing systems. The user organisation, forexample, may use its own deterioration models, or itsown methods to select instance-level repairs. It isexpected, however, that the benefits of the frameworkcan be maximum if all its modules are utilised.

While the presented framework can deal withthousands of instances, future experimenting withvarious evolutionary optimisation techniques can leadto better performance. Some other improvements canalso be made to speed the optimisation. Because it takestime to carry out five optimisations for each instance inthe building inventory, this step can be carried out inparallel with the condition assessment to save time. Assuch, upon user entry of an update to the inspectedcondition of any instance, the optimisation process is

automatically activated to first generate a deteriorationmodel, and then generate the best repair options (a totalof about 5minutes of processing timeper instance).Otherpotential improvements include: improving the dete-rioration model by allowing new deficiencies to emergewith time, in addition to existing deficiencies gettingworse; applying the life cycle analysis tomore than5years(10 or 15 years is practical for buildings); using detailedrisk-based analysis for critical building components;modifying the network-level optimisation to allow aninstance to be repaired more than once in the planning

Figure 11. Spreadsheet model for network-level optimisation.

Figure 12. Optimisation performance on large-scalenetworks.

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horizon to suit some organisations; examining theapplicability of the system to other asset types; adding afeedback mechanism to record/update actual costs; andusing GIS and visualisation techniques to present inputsand outputs such as condition indices, level of funding,backlog, and actual versus planned data.

Summary and concluding remarks

In this paper, a framework for building asset manage-ment has been presented. The framework tracks thedynamics of deficiencies in the building instances fromcondition assessment to deterioration modelling, repairselection, prioritisation, and fund allocation. Uniquely,the framework also incorporates an integrated in-stance-based and network-based life-cycle analysis.Various optimisation experiments were carried outon real-life data and showed the efficient performanceof the system and its capability to optimise decisionsfor up to 8000 instances simultaneously. The frame-work is expected to help building owners who areinterested in practical tools to improve the conditionsof their buildings, under budget constraints. Withcontinuous improvements to the system as suggested inthe paper, the proposed framework can be adapted toother infrastructure domains such as bridges, high-ways, and airports. The proposed research is expectedto aid consultants and owner organisations, such asmunicipalities and government agencies, to makeappropriate decisions that ensure the efficient opera-tion of infrastructure assets, with least cost.

References

ASCE, 2005. Available from: http://www.asce.org/report-card/2005/index.cfm [Accessed 3 April 2007].

Bailey, D.M., Brotherson, D.E., Tobiasson, W., andKnehans, A., 1989. ROOFER: An engineered manage-ment system for bituminous built-up roofs. Technicalreport M-90/04/ADA218529. US Army ConstructionEngineering Research Laboratory, Champaign, IL.

Brandt, E. and Rasmussen, M.H., 2002. Assessment ofBuilding Conditions. Energy and Buildings, 34 (2), 121–125.

Elhakeem, A. and Hegazy, T., 2005a. Towards a visualguidance system for condition assessment of the buildinginfrastructure. In: Proceedings of the 1st CSCE specialtyconference on infrastructure Technologies, Management,and Policies, 2–4 June, Toronto, Ontario, Canada, FR-159. Toronto, Ontario, Canada: Canadian Society forCivil Engineers.

Elhakeem,A. andHegazy, T., 2005b. Improving deteriorationmodeling using optimised transition probability matricesforMarkov chains. In:Proceedings of the 84th Transporta-tion Research Board annual meeting, 9–13 January,Washington, DC, USA. Washington, DC, USA: Trans-portation Research Board, No. 05–2656.

Evolver, 1998. The genetic algorithms super solver: Usersmanual. Newfield, NY, USA: Palisade Corporation.

Greimann, L., Stecker, J., Rens, K., McKay, D., and Foltz,S., 1997. Condition Assessment of Lock and DamStructures. In: Proceedings of infrastructure conditionassessment: Art, science and practice, Boston, MA, USA.ASCE, 385–394.

Halfawy, M., Newton, L., and Vanier, D., 2005. Municipalinfrastructure asset management systems: State-of-the-artreview. National Research Council of Canada. ReportNo: 48339.

Hudson, W.R., Hass, R., and Uddin, W., 1997. Infrastruc-ture management. New York, USA: McGraw-Hill.

Madanat, S., Karlaftis, M.G., and McCarthy, P.S., 1997.‘Probabilistic infrastructure deterioration models withpanel data. Journal of Infrastructure Systems, 1 (3), 4–9.

Madanat, S., Mishalani, R., and Ibrahim, W.H.W., 1995.Estimation of infrastructure transition probabilities fromcondition rating data. Journal of Infrastructure Systems,1 (2), 120–125.

Morcous, G., Rivard, H., and Hanna, A.M., 2002. Case-based reasoning system for modeling infrastructuredeterioration. Journal of Computing in Civil Engineering,16 (2), 104–114.

Physical Planning Technologies Inc. (PPTI), 2006. RECAPP1

1.0 User Guide. Toronto, Canada: Physical PlanningTechnologies Inc.

Shahin, M.Y., 1992. 20 years experience in the PAVERpavement management system: Development and imple-mentation. In: F.B.Holt andW.L.Gramling, eds.Pavementmanagement implementation. Philadelphia, PA: ASTM.

Uzarski, D.R., 2002. Condition assessment manual forbuilding components for use with BUILDER Version 2.1.

Uzarski, D.R., Grussing, M.N., and Clayton, J.B., 2007.Knowledge-based condition survey inspection concepts.Journal of Infrastructure Systems, 13 (1), 72–79.

Figure 13. Analysis of optimisation decisions.

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