raising the bar for educational buildings · the 2005 report card by the asce gave failing grades...

Elhakeem, A. and Hegazy, T. 1

Raising the Bar for Educational Buildings

Date of Submission: July 22, 2005

Word Count: Text Only = 2,828 words

1 Table x 200 = 200 words

7 Figures x 200 = 1,400 words

Total = 4, 428 words

Authors: Ahmed Elhakeem1, and Tarek Hegazy2

1. Ph.D. Candidate, Civil Engineering Dept.,

University of Waterloo, Waterloo, Ontario, N2L 3G1 Canada Tel: (519) 888-4567 ext.: 3869 Fax: (519) 888-6197 E-mail: [email protected]

2. Corresponding Author Associate Professor, Civil Engineering Dept., University of Waterloo, Waterloo, Ontario, N2L 3G1 Canada Tel: (519) 888-4567 ext.: 2174 Fax: (519) 888-6197 E-mail: [email protected]


ABSTRACT

Educational buildings are essential components of the civil infrastructure. Currently in North America, a large percentage of educational buildings are rapidly deteriorating due to age, aggressive environment, and insufficient capacity for population growth. In this paper, a novel framework for building asset management is presented. The framework involves developments in four main areas: 1) identifying the most frequent deficiencies for various building components; 2) predicting the time-dependent deterioration levels associated with the various building components; 3) searching for the most economic repair scenario for each component in each year of the planning horizon; and 4) using the data obtained in the previous step to optimize the year of repair for each component. The paper highlights the development details and the implementation of a user-friendly computer program. The proposed framework is expected to help school boards “raise the bar” and improve the overall conditions of their inventory of educational buildings, under the prevailing budget constraints.


INTRODUCTION

As the infrastructure touches almost all aspects of life such as transportation, water/sewer, health care facilities, and educational buildings, its ailing signs worry a broad category of the public. While natural disasters have certainly contributed to many infrastructure failures, the collapse history of many others has primarily been due to the lack of repair and maintenance, improper funding and spending, or more precisely, to inadequate asset management. The followers of the infrastructure report cards by the ASCE from 1998 to 2005 can easily conclude that, current asset management systems and/or practices are deficient, as there are no improvements achieved in all infrastructure sectors, despite of the current spending levels.

The 2005 report card by the ASCE gave failing grades to many infrastructure systems, and identified the need for $1.6 trillion (US) to bring the assets to acceptable condition (ASCE 2005). Similarly, the environmental, social, and transportation infrastructure systems in Canada require huge investments that amount to approximately $10 billion (US) annually for 10 years (Federation of Canadian Municipalities 1999). Since the environmental, social, and transportation sectors represent about one third of the Canadian infrastructure expenditures (Figure1, Statistics Canada 1995), it can be assumed that the infrastructure system as a whole requires an investment of about $30 billion per year for ten years. Despite of this large need, the Infrastructure Canada Program allocated $2 billion (US) for the year 2000 for all infrastructure sectors (Federation of Canadian Municipalities 2001), thus covering less than 7% of the need. With the non-residential buildings being approximately 40% (largest sector) of the infrastructure, such sector is expected to suffer the largest shortfall in expenditures on rehabilitation and repair.

Sustaining the serviceability and safety of infrastructure networks is a highly challenging task, particularly under stringent budgets. Such networks are characterized by being huge in size, complex in nature, and costly to build/operate/ maintain. In addition, education buildings have a unique challenge due to the fact that their maintenance period is largely restricted to the short summer vacation in which the schools are not operational. In view of the stringent shortfall of expenditures, therefore, it is essential to establish an effective maintenance / repair strategy to keep the school facilities safe and serviceable with least expenditures. A framework for building asset management that achieves this objective is proposed in this paper, details are in the following sections.

PROPOSED BUILDING ASSET MANAGEMENT SYSTEM

Planning the repair process is a time-consuming and costly task that requires accurate assessment of the condition of all asset components; predicting the future conditions of these components along the planning horizon; proposing repair solutions that suite the deterioration trends of asset components; and finally prioritizing the components for repair purposes, under stringent budget constraints. The previous four processes of condition assessment, deterioration modeling, selecting repair options, and asset prioritization represent the main facets of the proposed asset management system. The proposed developments within each process aim at overcoming the drawbacks of existing systems. Figure 2 shows the main components of the proposed system, which are explained in detail in the following sections.

CONDITION ASSESSMENT

In 1993, Rugless defined condition assessment as ”a process of systematically evaluating an organization’s capital assets in order to project repair, renewal, or replacement needs that will preserve their ability to support the mission or activities they were assigned to serve”. Condition assessment can be performed using various methods including: visual inspection, photographic and optical methods, non-destructive evaluation methods, and smart sensors (Hudson et al. 1997). Among these methods, visual inspection can be considered as the most suitable approach for the majority of building components. Visual inspection however, is subjective, labour intensive and expensive (Hudson et al. 1997; Greimann et al. 1997).

Overcoming these drawbacks represents the main objective in the condition assessment module of the proposed system. To do that, a visual guidance system is developed to support inspectors during their evaluations. The computerized system can be described as a dynamic pictorial database that provides pictures of various components in various condition states. The use of this tool during the inspection process is expected to minimize the subjectivity associated with the process, make it faster and less expensive. The new system suits non professional personnel which help performing the inspection in a parallel fashion to save more time and cost. The new inspection tool is called V-CAP (Visual Condition Assessment Program) and shown in Figure 3.


As shown in Figure 3, the proposed condition assessment system provides three main components:

1. An asset hierarchy to the left, which is a database of all building components, organized in different levels;

2. An approach for evaluating the condition of a component based on possible deficiencies in the top right; and

3. A visual guidance system to ensure accurate and less subjective assessments.

The asset hierarchy determines the main components of an asset. The lowest level in the asset hierarchy represents the individual instances (e.g., Boiler no. 1) that will be visually inspected and given condition ratings so that a repair budget can be allocated to them.

The evaluation approach relays on distresses (a distress survey base), where a set of possible deficiencies are evaluated to compute the condition based on their values. This approach is selected, because it is accurate and provides a record of what’s wrong with the inspected instances (Uzarski 2002).

A visual guidance system is proposed in this paper as a complementary part to any condition assessment tool, to minimize the subjectivity associated with the process. It consists of a pictorial database of various building components at different condition states. To design the visual guidance system, an initial study has been carried out to collect a database of pictures. Possible system designs have also been investigated and the most appropriate system is used to implement the visual guidance system. Accordingly, the implemented design is shown in figure 4.

Condition Evaluation

The end product of the condition assessment module is a numerical evaluation of severities associated with the possible deficiencies of each inspected component. Based on these individual severities, a Deterioration Index (DI) of 100 points is calculated and used to represent the overall condition of the component. The DI values indicate the level of component deterioration, i.e., a DI value of 0 implies excellent condition (no deterioration) while a DI of 100 implies extremely critical condition. With a component having d possible deficiencies, its DI is calculated as the weighted sum of the measured severities during the inspection, as follows:

1001

∑=

⋅=

d

iii SW

DI (1)

The weights (Wi)s used in Equation 1 for the various deficiencies reflect the relative impact of each deficiency on the overall condition of the component and can be obtained through a questioner surveys among expert building inspectors and operators.

DETERIORATION MODELING

Decisions related to Maintenance and Repair (M&R) of infrastructure assets depend not only on their current conditions, but also on their deterioration behavior. While current condition can be accurately measured, future condition of an asset is difficult to predict and is basically a function of aging, operational conditions, maintenance history, etc. Various deterioration models, therefore, have been proposed in the literature and have become essential for any asset management system (11, 12, 13).

As opposed to establishing a general deterioration model for each component, the proposed system generates customized deterioration models for each inspected instance of a component. To generate these models, the Markov Chain approach is used but with optimized transition probability matrices (TPM). The TPM optimization has been developed to minimize the errors of predicting actual deterioration trends based on historical records. The optimization spreadsheet is shown in Figure 5 with the optimum TPM providing a deterioration curve that passes through actual measured DIs.

SELECTING THE BEST REPAIR OPTIONS

In this section, a model is introduced to determine the best repair options for each instance, based on its actual deficiencies and predicted deterioration behavior. To do that, a detailed analysis is carried out of the various repair options (scenarios) that suit the severities at each year of the planning horizon. Optimization is then used to select


the best repair scenario among alternatives to satisfy specific user constraints. The proposed approach also sets the basis for calculating the expected DI immediately after repair, which represents the after-repair improvement in condition, and also provides a methodology for estimating the repair cost.

Repair Scenarios

Repair scenarios (RSs) can be generated for any component using its list of deficiencies. These scenarios represent the decisions regarding whether to repair or not to repair any combination of the component deficiencies. For example, if a component has four deficiencies (D1, D2, D3, and D4), one possible repair scenario is represented in the binary form as (1, 1, 0, 1), which implies repair deficiencies 1, 2, and 4 and keep defect 3 without repair. Another scenario can be (0, 1, 1, 0), which means don’t repair, repair, repair, and don’t repair defects 1, 2, 3, and 4, respectively. If the number of deficiencies is denoted as d, then the possible number of repair scenarios is equal to 2d. All the possible scenarios are assumed to be valid and feasible (a filter to check the constructability of any repair scenario can easily be incorporated).

Cost Calculations

For each repair scenario (RS), it is possible to calculate its repair cost as a percentage of the total component replacement cost, based on two simple assumptions: deficiency weight is a good representative of the repair cost of the defect, and repairing the defects individually will cost more than its share in case of full replacement (i.e., repairing them all at the same time). Based on these two assumptions and an assumed 25% cost increase in the case of repairing the individual defects, the cost to repair defect i, denoted as RDi , is expressed as

ii WRD *25.1= , (2)

Therefore, the total cost (TC) of any repair scenario (RS) can be determined by summing the costs of repairing all the defects that are decided to be repaired in this repair scenario, as follows:

∑=

=d

iii RSWTC

1**25.1 . (3)

For instance, for an instance of a component having four deficiencies of weights 0.20, 0.25, 0.30, 0.25, respectively, then, considering a repair scenario RS = (1, 0, 0, 1), i.e., to repair only the first and fourth deficiencies, then the total repair cost, TC, as a percentage of the complete replacement cost, is calculated as: 1.25*W1*1 + 1.25*W2*0 + 1.25*W3*0 + 1.25*W4*1 = 1.25 (W1 + W4) = 1.25 * 0.45 or 56.25% of replacement cost. Accordingly, this repair cost in dollars can be easily calculated from known replacement cost tables, where the replacement cost for any component per square foot of school area is known (e.g., the replacement cost for the roof component is $ 8.04 / ft2).

Condition Improvement after Repair

Once a repair scenario is generated, the DI after repair needs to be calculated. This is easily accomplished by looking at the repair scenario and assigning 0 severities to the defects that will be repaired. Accordingly, the component’s after-repair condition (ARDI) can be calculated using Equation 4, as follows:

100

)1(1

∑=

−⋅⋅=

d

iiiki

k

RSSWARDI (4)

ARDIk is the After-Repair-Deterioration Index in year k. Table 1 provides an example of the calculations. This approach is formulated as an optimization process to determine the best repair scenario. The objective function minimizes the repair cost and selects the repair options (1 or 0) for all possible defects. Two main constrains are used in this optimization to direct the solution towards meeting the objective: 1) the selected scenario should provide an ARDI less than or equal to a desirable DI, and 2) the cost should not exceed a given limit.

The combination of deterioration and repair modeling provide the necessary life-cycle cost analysis of any component. A graphical representation of the results of this life cycle analysis is shown in figure 6.


ASSET PRIORITIZATION AND REPAIR FUND ALLOCATION

The deterioration and repair optimizations discussed in the previous two sections provide optimal LCC for any component at any year in the plan. Once these LCCs are known for all components in the network of assets, it becomes possible to determine the best repair year for each component in the network. A prioritization framework is developed to determine the proper year for repairing each component using optimization. The possible values for repair years range from 1 to 5 in addition to 0 to compensate for the case of no repair. As the optimization will cover the whole network of assets and their components, it is expected to be a large-scale optimization problem. Hence, a non-traditional optimization technique (GA) is planned to be used.

The objective function is to minimize the overall network deterioration index by selecting the best year for repair for each component. The yearly budget limits represent the main constraints for the optimization. The model as such, makes it possible to improve the overall network condition within the available yearly funds. Figure 7 shows the optimization framework.

CONCLUSION

In this paper, a framework for asset management for the building infrastructure has been presented. The framework touches in details the main functions of any asset management system and provides a new methodology to deal with condition assessment, deterioration modeling, repair selection, and asset prioritization. In the area of condition assessment, this paper introduced a practical guidance system for inspectors that suit the diverse nature of building components. It uses a pictorial database of components at various condition states, thus helps to reduce the subjectivity of the condition assessment process and makes it faster and less expensive. In the area of deterioration modeling, a model has been introduced to optimize the transition probability matrices of Markov Chains so that to accurately predict the future deterioration of building components. With respect to selecting suitable repair options, a new approach is introduced to create and compare among various repair scenarios based on actual deficiencies and life cycle cost analysis. Finally, a model is introduced for prioritizing components and allocating limited repair funds. The model uses genetic algorithms as a non traditional optimization technique capable of dealing with large scale problems. The proposed building asset management framework is expected to help school boards “raise the bar” and improve the overall conditions of educational buildings, under the prevailing budget constraints.

REFERENCES

1. ASCE Progress Report for America's Infrastructure. ASCE, Washington DC., USA, 1998. 2. ASCE Progress Report for America's Infrastructure. ASCE, Washington DC., USA, 2003. 3. ASCE Progress Report for America's Infrastructure. ASCE, Washington DC., USA, 2005. 4. Federation of Canadian Municipalities. Quality of life infrastructure program. Ottawa, Ontario, Canada. 1999. 5. Statistics Canada.. Capital expenditures on construction by type of asset., 1995 6. Federation of Canadian Municipalities. Early warning: will Canadian cities compete?, FCM, Ottawa, Ontario,

Canada., 2001 7. Rugless, J. Condition Assessment Surveys, Facilities Engineering Journal, 1993, 21(3), pp. 11-13. 8. Hudson, W.R., Hass, R., and Uddin, W. Infrastructure Management, McGraw-Hill, New York, 1997. 9. Greimann, L., Stecker, J., Rens, K., McKay, D., and Foltz, S. Condition Assessment of Lock and Dam

Structures. Proceedings of Infrastructure Condition Assessment: Art, Science and Practice, ASCE, Boston, MA, USA, 1997, pp.385-394.

10. Uzarski, D. R. Condition assessment Manual for Building Components for Use with BUILDER Version 2.1. 2002.

11. Madanat, S., Mishalani, R., and Ibrahim,W. H.W. Estimation of Infrastructure Transition Probabilities from Condition Rating Data. Journal of Infrastructure Systems, ASCE, Vol. 1, No. 2, 1995, pp. 120–125.

12. Madanat, S., Karlaftis, M. G., and McCarthy, P. S. Probabilistic infrastructure deterioration models with panel data. Journal of Infrastructure Systems, ASCE, Vol. 1, No. 3, 1997, pp. 4–9.

13. Morcous, G., Rivard, H., and Hanna, A. M. (2002a). Case-Based Reasoning System for Modeling Infrastructure Deterioration, Journal of Computing in Civil Engineering, ASCE, Vol. 16, No. 2, 2002a, pp. 104–114.


List of Tables and Figures:

TABLE 1 After-Repair Condition and Cost Calculations for any Repair Scenario FIGURE 1 Average yearly expenditures by type of infrastructure. FIGURE 2 Components of the proposed building asset management system FIGURE 3 Condition assessment tool FIGURE 4 Visual guidance system FIGURE 5 Optimized Markov Chain model for component deterioration FIGURE 6 LCCA model for repair selection FIGURE 7 Optimization model for asset prioritization


TABLE 1 After-Repair Condition and Cost Calculations for any Repair Scenario

Defect Weight

Wi Before Repair

Severities Repair Scenario

(RSi) After Repair

Severities Repair Cost

1.25* Wi* RSi

D1

D2

D3

D4

W1

W2

W3

W4

S1

S2

S3

S4

1*

0

0

1

0

S2

S3

0

1.25 * W1* 1

1.25 * W2* 0

1.25 * W3* 0

1.25 * W4* 1

Σ = 100% DI (Equation 1)

ARDI (Equation 1) Total Cost = Σ

*1 = Repair this defect; 0 =Do not repair


FIGURE 1 Average yearly expenditures by type of infrastructure.

Marine 1%

Non- Residential

Buildings 40%

Other 5%

Oil and Gas 21%

Communication 4%

Electric 10%

Transportation 14%

Water 2%Sewage 3%


FIGURE 2 Components of the proposed building asset management system

Condition Assessment Deterioration Modeling

Repair Options

Asset Prioritization


FIGURE 3 Condition assessment tool

System of picture database to guide the inspectors

User inputs


FIGURE 4 Visual guidance system

0 25 50 75 1001. 2. n.

Pls. scroll each defect till a comparable picture is shown.

User Interface

50%

Picture for def. 2 & severity 50%

Calculations:

DI = 63 i.e., Condition = Fair

Picture changes with user selection of each deficiency and each severity level.

Requires many pictures for each component (about 25).

Provides accurate and detailed deficiency evaluation.

Fast and most accurate.

Personal judgment is almost eliminated.

Scroll to comparable picture.

Comments


FIGURE 5 Optimized Markov Chain model for component deterioration

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40 45

AgeCo

nditi

on

Optimized TPM

From Markov based on average

From Markov based on average + actual records Predicted future DIs

Actual records from inspection

DI


FIGURE 6 LCCA model for repair selection


FIGURE 7 Optimization model for asset prioritization

Objective function

Yearly available funds

Variables, year of repair

Actual spending

a) Before optimization

b) After optimization

Optimization improves network overall condition from a DI of 52.4 to 42.2.

raising the bar for educational buildings · the 2005 report card by the asce gave failing grades...

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