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Roofing system product model: life cycle economic implications Vanier, D. J.; Nesje, A.
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Roofing System Product Model: Life Cycle Economic Implications
Dana J. Vanier
National Research Council Canada, Institute for Research in Construction, Ottawa, Canada Arne Nesje
SINTEF, Civil and Environmental Engineering, Trondheim, Norway
Second European Conference on Product and Process Modeling in the Building Industry BRE, Watford, Oct. 19-21, 1998 (NRCC-42662).
ABSTRACT: The objectives of the Building Envelope Life Cycle Asset Management (BELCAM) project is to assist asset managers by developing methods to predict the remaining service life of building envelope components, and by providing tools to optimize maintenance expenditures. The project concentrates on roofing systems, as a “proof of concept”, but will apply the tools, procedures and methods to other building envelope systems in future stages of the BELCAM project.
The product modeling activity for the BELCAM Project has five phases. The first phase of the activity deals with condition assessment surveys. Phase II deals with risk and reliability. The objective of the current phase of the product modeling activity is to incorporate life cycle economics into the roofing system product model. Future phases include: Phase IV will deal with maintenance management, while Phase V will deal with CADD and geometric data.
Phase II of the project developed models to predict the remaining service life of roofing systems, but they also can predict the probability of failure. Once the remaining service life is calculated, and the risks of failure are known, these data can be used to calculate the life cycle costs of the roofing system. Computer modeling using life cycle costing, alongside data from condition assessment surveys, risk analysis and a costing database, makes it possible to calculate the life cycle costs of various repair strategies and present these to the user. Product modeling plays a key role in the integration of data in the BELCAM project.
RÉSUMÉ
Les objectifs du projet de gestion des biens au cours du cycle de vie de l’enveloppe du bâtiment (BELCAM) vise à aider les gestionnaires immobiliers en établissant des méthodes de prédiction de la durée de vie résiduelle des éléments de l’enveloppe des bâtiments et en leur fournissant des outils pour optimiser les ressources investies dans l’entretien. Le projet est actuellement axé sur les toitures, afin de valider les principes, mais ses outils, procédés et méthodes seront appliqués à d’autres éléments de l’enveloppe lors des prochaines phases de l’initiative.
L’activité de modélisation des produits du projet BELCAM comporte cinq phases. La première phase porte sur les études d’évaluation de l’état des couvertures. La phase II porte sur les risques et sur la fiabilité. L’objectif de la phase actuellement en cours est d’incorporer l’économie du cycle de vie à la modélisation des produits de toiture. Les phases à venir incluent : la phase IV, où les chercheurs se pencheront sur la gestion de l’entretien, et la phase V, qui sera axée sur la CAO et sur les données géométriques.
La phase II du projet a vu le développement de modèles permettant de prédire non seulement la durée de vie résiduelle des systèmes de toiture, mais aussi leurs probabilités de défaillance. Une fois que la durée de vie résiduelle est calculée et que les risques de défaillance sont connus, ces données peuvent être utilisées pour calculer les coûts pendant toute la durée de vie utile de la toiture. Une modélisation informatique fondée sur l’évaluation du cycle de vie, ainsi que sur les données connexes tirées des études d’évaluation de l’état des ouvrages, des analyses des risques et d’une base de données d’établissement des coûts, permet de calculer les coûts du cycle de vie de différents stratégies de réparation et de les présenter à l’utilisateur. La modélisation des produits joue un rôle clé dans l’intégration des données au projet BELCAM.
1. INTRODUCTION
Building Envelope Life Cycle Asset Management (BELCAM) is a three year project dealing with service life prediction and the durability of building materials, components and systems. The project concentrates on roofing systems, as a “proof of concept”, but will apply the tools, procedures and methods to other building envelope systems in future stages of the BELCAM project.
1.1 Objectives and Goals of BELCAM Project The objective of the BELCAM project is to assist asset managers by developing tools, procedures and methods to predict the remaining service life of building envelope components (Vanier & Lacasse 1996).
The two goals of the BELCAM project are: (1) predict the remaining service life of building envelope components, and (2) assist the asset manager to optimize their maintenance management expenditures. The first goal is addressed by using engineering management systems and condition assessment surveys (Bailey et al. 1989) to assess the current performance of building envelope components and then by manipulating this data to predict future performance. The second goal is addressed by using multi-objective optimization techniques (Lounis et al. 1998) to prioritize conflicting, and sometimes contradictory, alternatives. Using these techniques the asset managers can minimize the probability and cost of failure, and can optimize life cycle costs (LCC), while maximizing the resulting performance of their portfolio.
Service life/asset management is a complex science as it requires expertise from a number of engineering domains (Vanier & Lacasse 1996). Service life research establishes the technical service life of materials, components or systems (Lacasse & Vanier 1996). Life cycle economics establishes the full life costs of the design, build, operate, and demolish cycle. Maintenance management is critical to service life prediction as the level of maintenance has a high correlation to the long term performance of systems. User requirements must be modeled in order to establish the guidelines for the desired performance, both
initially and at the end of the service life (Vanier et al. 1996). Risk analysis calculates the probability of failure, as well as the consequences of failure (Lounis et al. 1998). The data required for these five enabling technologies will be in electronic format in the near future. The integration of these data is a large challenge and product modeling was selected as the integration technology for the BELCAM project. This paper deals primarily with product modeling the life cycle economics issues.
1.2 Roofing System Product Model
The product modeling activity for the BELCAM Project has five phases. The first phase of the activity dealt with condition assessment surveys (Vanier 1998). Phase II dealt with risk and reliability analysis (Lounis et al. 1998). The objective of the current phase is to integrate life cycle economic issues into the roofing system product model. Two additional phases are planned: Phase IV will deal with the maintenance management aspects, while Phase V will deal with CADD and geometric data.
1.3 Phase III - Life Cycle Economics
This phase addresses the needs for integrating the LCC implications of the maintenance, repair and renewal (MRandR) of roofing systems, as well as the cost implications of operations, predominantly energy usage.
In Phase I, MicroROOFER (Bailey et al. 1989) was used to establish the first conceptual product model for BELCAM. Although MicroROOFER contains a costing model, this feature was not used in BELCAM as it does not follow well-established ASTM standards (ASTM 1993) regarding the discounting of future costs.
The core software used in this phase of the product modeling activity is BLCC (Building Life Cycle Costing) from NIST (BLCC 1995). BLCC is one of several programs for LCC. BLCC was selected because it suited BELCAM’s needs: BLCC follows ASTM (1993) standards for building economics; it is operating on a Windows95™/DOS™ platform, and it contains an energy calculation model.
Costing databases are used to calculate the cost of MRandR. These data will be augmented with MRandR historical data captured during the BELCAM regional surveys (Lounis et al. 1998).
2. METHODOLOGY
The conceptual modeling tool used for the current phase of this exercise is EDMS 3.5 (EDMS 1998). The modeling activity follows the sequence below: 1. Use the existing roofing system product model
(Lounis et al. 1998; Vanier 1998)
2. Use existing data related to costing, whenever possible.
3. Model existing application data requirements (BLCC, costing databases).
4. Integrate the data requirements into the existing roofing system product model.
5. Populate product data model with data from BELCAM regional.
6. Test and validate the results.
Steps 5 and 6 are beyond the scope of this paper, but future research will address these steps (Vanier and Nesje 1999).
2.1 Existing Roofing System Product Model The product model outlined in Fig. 1 was developed to accommodate the needs of the condition assessment survey data (Vanier 1998). Fig. 2 identifies the risk analysis requirements for BELCAM (Lounis et al. 1998). The tops of Figs. 1 and 2 show the hierarchical “has” information related to facilities, buildings, roofs, sections and membranes. Research related to risk analysis has identified a number of prerequisite data attributes, shown in Fig. 2. These attributes will be populated by data from the regional surveys; these data include the annual maintenance expenditures and the estimated replacement costs.
Fig. 2: Risk Analysis Data (Lounis et al. 1998)
2.2 STEP/IAI modeling
Unfortunately, very little has been done to date in the field of product modeling of roofing systems (Vanier 1998). It also appears that there is little hope of work in this field either by the STEP (1998) or the IAI (1998) community.
2.3 Building Life Cycle Cost (BLCC)
“The NIST [National Institute of Standards and Technology] Building Life-Cycle Cost (BLCC) computer program provides economic analysis of proposed capital investments that are expected to reduce long-term operating costs of buildings or building systems. In addition to calculating the discounted cash flows to present value, it is especially useful for evaluating the costs and benefits of energy conservation projects in buildings. Two or more competing designs can be evaluated to determine which has the lowest life-cycle cost. Or a project can be compared against a
‘do-nothing’ base case where no capital improvements are made to reduce future costs. Economic measures, including net savings, savings-to-investment ratio (SIR), adjusted internal rate of return (AIRR), and years to payback can be calculated for one alternative relative to the base case or to another related alternative” (BLCC 1995). The current implementation of BLCC is on the DOS™ operating system. The BLCC program is updated each April to coincide with the February release of the Department of Energy’s energy price projections. Future enhancements may include a JAVA interface (Fuller 1998).
2.3.1 BLCC Overview
The screen capture in Fig. 3 displays baseline data about the building in question and the type of analysis required. Figs. 3 through 5 provide typical examples of the data requirements for BLCC.
Fig. 3: BLCC Baseline Data
file: WATFORD.DAT Private Sector Design--Energy Related Studies GENERAL PROJECT DATA:
Project Alternative: Existing System
Date of Study (DOS) (month/year): 4/1998 (e.g., 4/1998) Beneficial Occupancy Date (BOD) (month/year): 4/1998 (e.g., 4/1998) Study Period (years/months from DOS): 25/0 (e.g., 25/0) Discount rate: 4.10% (real, excluding inflation)
End-of-year or Mid-year discounting of annually recurring costs (E/M): M
Fig. 4: BLCC Tax-Related Data for Private Sector
Marginal Federal Income Tax Rate: 50.00% Marginal State Income Tax Rate: 0.00% Property Tax Rate (nominal): 0.00% Capital Gains Adjustment Factor: 0.00% Depreciation Recapture Code*(0-3): 1
Depreciation Basis Adjustment Factor**: 0.00% *Depreciation recapture codes:
0 = none; 1 = recapture all as ordinary income; 2 = recapture all as capital gain; 3 = combined.
Fig. 5: BLCC Tax-Related Data for Private Sector
Base Case Renewal Savings Initial Investment item(s): -
Capital Requirements as of Serv. Date $0 $10,000 -$10,000 Subtotal $0 $10,000 -$10,000
Annual and Non-An. Recurring Costs $39,315 $29,486 $9,829 Energy-related Costs $16,611 $14,950 $1,661 Capital Replacements $78,353 $78,353 $0 Income Tax Adjust over Service Period -$27,963 -$22,218 -$5,745 Subtotal $106,316 $100,571 $5,745 Total P.V. Life-Cycle Cost $106,316 $110,571 -$4,255
Table 1: Entities and Attributes
Facility Roof Section
State (2 Chars.) Energy Type (0-9) Project Name (Chars.) Discount Factor (%) Unit Code (0-12) Initial Cost ($)
End/Mid Year Discounting (E/M) Unit/Year (#) Base & Service Date (yr.) Tax Calculations (Y/N) Price/Unit ($) Expected Life (yrs.) Study Period (yrs.) Annual Demand Charges ($) Price Escalation (%) General Inflation Rate (%) Escalation Type (1,2,3) Recurring Costs ($) Depreciation Recapture (0,1,2,3) Energy Rate Schedule (1,2,3) (e.g.
Residential, Commercial, Industrial)
Recurring Cost Inflation (%)
Depreciation Adjustment Factor (%) Financing (Y/N) List of Non-Recurring Costs and Year ($ and yr.) up to Study Period (yrs.)
Federal Tax Rate (%) Amount Borrowed (%) Replacement Date (yrs.) State Tax Rate (%) Loan Type (0,1,2,3) Replacement Cost ($) Municipal Tax Rate (%) Interest Rate /yr. (%) Residual (Resale) Value (%) Capital Gains Rate (%) Payments per year (#) Property Tax Assessment (%) Tax Credit Rate (%) Points for Loan (%) Project Alternative (Chars.) Depreciation Code (0,1,2,3,4) Amortization (yrs.) Comments (Chars.)
2.3.2 BLCC Data Input and Output
Using the existing roofing system product model detailed in Figs. 1 and 2, attribute requirements for BLCC have been classified in Table 1. This process attempts to establish the attribute at the highest level possible in the hierarchy, to group common attributes, and to remove duplicates that
should not affect the LCC calculations (e.g. two attributes for Rate of Depreciation).
2.4 Risk Analysis Calculations
The risk analysis portion of the calculations is treated as non-recurring costs within BLCC. For example, any data generated from the risk analysis
module of BELCAM (Lounis et al. 1998) can be treated as a non-recurring cost that is equal to the probability of failure multiplied by the consequences of failure. That is, if the do nothing strategy establishes the probability of failure in year five at 10% and the consequence of failure at $10,000 (cleanup and relocation of tenants), then a non-recurring cost of $1,000 is entered for year five in the BLCC data file.
2.4.1 Maintenance, Repair and Renewal
On careful analysis of the input data requirements displayed in Table 1, it becomes apparent that many of the BLCC data requirements are generic for any type of facility within an organization, and for any type of MRandR strategy.
Many of the data requirements are the same for the three strategies with the exception of the expected life, the recurring costs (i.e. maintenance costs) and the energy costs. For example, at any time a repair is proposed, then the do nothing strategy may have a higher maintenance cost, a shorter expected life, potentially higher energy costs; while still having the same expenses for replacement, inflation rates, etc. The same is true for considering the renew strategy of the roof section instead of performing the repair; in this case there could possibly be lower maintenance costs, a longer expected life (now equal to the replacement life), and lower energy costs.
Therefore, it is relatively straight-forward to calculate all three strategies (i.e., do nothing, repair, and renew) using the attributes already stored in the BELCAM roofing system product model and by augmenting these with the corresponding maintenance and energy costs.
2.5 Building Costing Databases
There are three sources for costing data in the building sector in North America: R.S. Means (Means 1997; 1998), Whitestone (Whitestone 1998), and Yardsticks for Costing (1997).
R.S. Means (1997) develops the cost data based upon material cost, labour cost, equipment cost, overhead and profit. In addition there are other factors such as location, size of projects, and productivity. All costs represent national averages,
but city indexes adjust costs to a particular location. There are over 20,000 unit price line items available. R.S. Means also delivers its data through its own software, such as Means CostWorks ’98, or uses third party developers to distribute its data (http://www.rsmeans.com/ means/demo/shortlst.html). Whitestone (1998) markets its own product on CD-ROM format. The data from both firms provides costing for unit measures for specific North American locations and, like Yardstick for Costing (1997), they follow the North American 16-Division specification format, i.e., Masterformat (1995). Yardsticks for Costing, a Canadian product, contains metric and imperial unit costs for the eight major Canadian cities; however, there are no plans to market an electronic version at this time.
Using Masterformat (1995), it is relatively straight-forward to interface these types of costing databases to the BELCAM product model. All that is required is the type of roof, the location, and the roof area. From this data, the total cost of the repair or renewal including labour, overhead and profit is calculated using a database lookup, along with some simple addition and multiplication.
3. CASE STUDY
The calculation of the LCC for any given series of MRandR strategies follows this sequence:
1. Enter BELCAM condition assessment data. 2. Calculate service life and probabilities of
failure (risk analysis).
3. Enter BLCC data (discount rates, inflation). 4. Enter maintenance, repair and renew data for
the three MRandR strategies.
5. Run BLCC.
6. Provide LCC output to user.
3.1 Integration Requirements
The following case study demonstrates the need for integrated data. In this simulation a number of software applications are required to produce the desired results: (1) data is collected regarding the condition of the roofing system using MicroROOFER (Bailey et al. 1989), along with some additional BELCAM attributes related to
risk; (2) the service life remaining is calculated using Markov Chain modeling (Lounis et al. 1998) based on the previous data as well as the research data collected in the regional surveys; (3) the probability of failure is calculated using risk analysis software; (4) baseline data regarding financing and life cycle economics such as discount and inflation rates are entered in the database; (5) baseline data regarding roof section areas, construction dates, renewal dates, and repair history is entered in the BELCAM database by the user; (6) costing data is calculated using the
building baseline data and costing databases; (7) BLCC calculates the LCC for the three MRandR strategies, and (8) output is presented to the user for decision-making, with an Excel™ interface.
3.2 Case Study Simulation
A number of runs were simulated using BLCC to demonstrate the capabilities of the proposed integrated system. Table 2 presents a collection of actual BLCC runs, but in a modified format. Table 2: BELCAM Results using BLCC: Net Present Values at 5% Discount Rate
Do nothing Repair Renew Financing Cash Requirements $0 $10,000 $100,000 $0 Financing-related costs $114,977 Annually Recurring MRandR Costs $39,315 $29,486 $19,658 $19,658 Non-An. Recurring Costs(*risk factor) $908 $454
Energy Costs $16,611 $14,950 $8,305 $8,305
Renewal Year 5 5 0 0
Replacement Costs $78,353 $78,353
Less: Tax Adjustments ($28,417) ($22,445) ($13,981) ($32,798) Less: Remaining Value ($0) ($0) ($0) ($0) Total LCC (without Tax Adjustments) $135,187 $133,243 $127,963 $142,940 Total LCC (with Tax Adjustments) $106,770 $110,798 $113,981 $110,142
All four strategies in Table 2 use identical data, except for cash requirements, the maintenance expenditures and the energy costs. The strategies were simplified for the sake of clarity of presentation. For example, in the do nothing strategy the maintenance cost is one-half that of the renew strategy, indicating that new roofs require less maintenance than old roofs. Again regarding the do nothing strategy, the energy costs are higher than the repair and the renew strategies because the old roof would probably be less energy-efficient than the renewed roof. It must also be noted that the risk factors in these four strategies are extremely insignificant, even though the probability of failure is 10% and the consequence is $10,000 worth of damage. The financing strategy represents 100% financing of the renew strategy ($100,000) with a 10 year amortization period at 8% interest rate.
Table 2 demonstrates there is relatively little difference in the LCC for the four strategies presented (±3.6%). An asset manager would most likely renew the roof, given these data. The significant factor in the calculations is the actual timing of the roof renewal cost of $100,000; the other costs identified in this case study such as the
energy, maintenance (MRandR cost) or risk factors have little impact on the total. However, if the eventual roof renewal is postponed further into the future (without adverse risk effects), the less significant it becomes. For example, if the renewal could be scheduled for year 15, then the modified Total LCC (with Tax Adjustments) is $80,547.
It is possible to carry out sensitivity analysis using this data framework. For example, the LCC impact of postponing the eventual renewal strategies could be calculated by performing multiple BLCC runs; passing the resulting data to the BELCAM roofing system product model; and using the model to feed data to a user interface such as an Excel™ spreadsheet, as in Fig. 6.
Fig. 6 displays the consequences of postponing the maintenance and repair decisions into the future. For example, the do nothing and repair strategy becomes more and more expensive as time goes on, because the risk of failure, MRandR costs, and the energy costs become increasingly more significant.
Fig. 6: Simulated Decision-Making Tool Life Cycle Costs (Net Present Value)
$90 $100 $110 $120 $130 $140 $150 $160 $170 $180 $190 $200 1 2 3 4 5 6 7 8 9 1 0 Years D o llar s (000) do nothing repair replace finance 4. DISCUSSION
Sophisticated applications in the future will have to rely on data integration as an essential framework of their design. Product modeling provides the capability for this integration. The BELCAM roofing system product model attempts to test this capability using existing software applications such as BLCC and using actual data obtained from regional roofing surveys.
Although the calculations for life cycle costs are well understood and documented (ASTM 1993), they are extremely expensive and difficult to program. In addition, many of the desired features in a comprehensive LCC program such as energy costs, MRandR costs, tax calculations, or inflation rates are already embodied in BLCC. BLCC and the roofing costing databases form integral parts of the BELCAM roofing system product model; however, product modeling techniques permit the replacement, updating or removal of some of these elements without affecting the remainder of the data framework.
5. CONCLUSIONS
The two BELCAM goals are: (1) to predict service life of components and systems, and (2) to assist asset managers to optimize their maintenance
expenditures. Markov Chain modeling (Lounis et al. 1998) not only predicts the remaining service life of roofing systems, but also can predict their probability of failure. Having calculated the remaining service life, and knowing the risks of failure, these data along with the costs of renewal can be used for calculating the life cycle costs of the roofing system. Computer modeling using BLCC, alongside data from condition assessment surveys, risk analysis and a costing database, makes it possible to calculate the life cycle costs of various MRandR strategies and present these to the user. This capability permits the BELCAM project to meet its second goal.
Product modeling plays a key role in the integration of data in the BELCAM framework. The conceptual design of the BELCAM roofing system product model is complete; the case study illustrates the capabilities and advantages of the framework, and future work will test and validate the conceptual design using data obtained from across Canada in the regional surveys (Vanier & Nesje 1999).
ACKNOWLEDGMENTS
The authors would like to recognize the National Institute of Standards and Technology as the developers of the BLCC software program, and to thank them for the use of their program.
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