Deterioration Rates of Building Component Groups Using Nominal Replacement Costs of Components
Publication: Journal of Performance of Constructed Facilities
Volume 35, Issue 6
Abstract
Predictive modeling of deterioration is typically done based on visually inspected condition ratings of building components. This paper presents the concept of a cumulative lost value ratio (CLVR) for component groups to track their deterioration with time. The CLVR is computed through the estimated nominal replacement costs and times for the components that form the group in a given building. This eliminates the need for condition surveys and enables a much larger proportion of building assets to be modeled. Data from the City of Melbourne indicated that eight building component groups encapsulated 87% of building value. The similarities observed in their deterioration patterns were used to allocate them into four categories: (1) superstructure; (2) finishes; (3) fittings, plumbing, and water; and (4) air-conditioning, fire, and electrical. Next, for each year, the proportions of buildings with a given component group in each of five CLVR ranges (C1–C5) were established and fitted with Markov deterioration models. The best fits were obtained for the superstructure and finishes groups and also for Conditions C1 and C5.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The raw data are the property of the City of Melbourne. They have been provided confidentially to RMIT University only in the context of a long-standing relationship of research partnership.
Acknowledgments
The authors acknowledge access to the raw data provided by the City of Melbourne to the RMIT University.
References
Alegre, H., D. Vitorino, and S. Coelho. 2014. “Infrastructure value index: A powerful modelling tool for combined long-term planning of linear and vertical assets.” Procedia Eng. 89 (Jan): 1428–1436. https://doi.org/10.1016/j.proeng.2014.11.469.
ASHRAE. 2019. Owner’s project requirements. Atlanta: American Society of Heating, Refrigerating, and Air Conditioning Engineers.
Bahr, C., and K. Lennerts. 2009. “Validation of maintenance cycles for public buildings.” In Proc., Int. ECCE Conf.: Current State and Challenges for Sustainable Development of Infrastructure; EUROINFRA 2009. Athens, Greece: European Council of Civil Engineers.
Barringer, H. P. 2003. “Predict future failure from your maintenance records.” In Proc., Int. Maintenance Conf. Fort Myers, FL: ReliabilityWeb.Com.
Brooks, S. 1998. “Markov chain Monte Carlo method and its application.” J. R. Stat. Soc. 47 (1): 69–100. https://doi.org/10.1111/1467-9884.00117.
BSI (British Standards Institution). 1992. Guide to durability of buildings and building elements, products and components. BS 7543. London: BSI.
Chanter, B., and P. Swallow. 2007. Building maintenance management. 2nd ed. Oxford: Blackwell.
Edirisinghe, R., S. Setunge, and G. Zhang. 2013. “Application of gamma process for building deterioration prediction.” J. Perform. Constr. Facil. 27 (6): 763–773. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000358.
Ferreira, C., L. C. Neves, A. Silva, and J. de Brito. 2018. “Stochastic Petri net-based modelling of the durability of renderings.” Autom. Constr. 87 (Mar): 96–105. https://doi.org/10.1016/j.autcon.2017.12.007.
Goh, B., and Y. Sun. 2016. “The development of life cycle costing for buildings.” Build. Res. Inf. 44 (3): 319–333. https://doi.org/10.1080/09613218.2014.993566.
Hatry, H. P., and E. B. Liner. 1994. Issues in deferred maintenance. Washington, DC: Urban Institute.
Huang, P., G. Huang, and Y. Sun. 2018. “A robust design of nearly zero energy building systems considering performance degradation and maintenance.” Energy 163 (Nov): 905–919. https://doi.org/10.1016/j.energy.2018.08.183.
Kim, J.-M., T. Kim, Y.-J. Yu, and K. Son. 2018. “Development of a maintenance and repair cost estimation model for educational buildings using regression analysis.” J. Asian Archit. Build. Eng. 17 (2): 307–312. https://doi.org/10.3130/jaabe.17.307.
Lin, P., X.-X. Yuan, and E. Tovilla. 2019. “Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data.” Comput.-Aided Civ. Infrastruct. Eng. 34 (8): 677–695. https://doi.org/10.1111/mice.12452.
Madanat, S., R. Mishalani, and W. H. W. Ibrahim. 1995. “Estimation of infrastructure transition probabilities from condition rating data.” J. Infrastruct. Syst. 1 (2): 120–125. https://doi.org/10.1061/(ASCE)1076-0342(1995)1:2(120).
Micevski, T., G. Kuczera, and P. Coombes. 2002. “Markov model for storm water pipe deterioration.” Int. J. Infrastruct. Syst. 8 (2): 49–56. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:2(49).
Mithraratne, N., and B. Vale. 2004. “Life cycle analysis model for New Zealand houses.” Build. Environ. 39 (4): 483–492. https://doi.org/10.1016/j.buildenv.2003.09.008.
Mohseni, H. 2012. “Deterioration prediction of community buildings in Australia.” Ph.D. thesis, School of Civil, Environmental and Chemical Engineering, RMIT Univ.
Mohseni, H., S. Setunge, G. Zhang, and R. Edirisinghe. 2012. “Deterioration prediction for community buildings in Australia.” Int. J. Constr. Environ. 1 (4): 175–196. https://doi.org/10.18848/2154-8587/CGP/v01i04/37498.
Mohseni, H., S. Setunge, G. Zhang, and R. Wakefield. 2017. “Markov process for deterioration modeling and asset management of community buildings.” J. Constr. Eng. Manage. 143 (6): 04017003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001272.
Perez, H., J. M. H. Tah, and A. Mosavi. 2019. “Deep learning for detecting building defects using convolutional neural networks.” Sensors 19 (16): 3556. https://doi.org/10.3390/s19163556.
Prakash, R., and K. K. Shukla. 2013. “Life cycle energy analysis of a multifamily residential house: A case study in Indian context.” Open J. Energy Effic. 2 (1): 34–41. https://doi.org/10.4236/ojee.2013.21006.
Rashedi, R., and T. Hegazy. 2016. “Holistic analysis of infrastructure deterioration and rehabilitation using system dynamics.” J. Infrastruct. Syst. 22 (1): 04015016. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000273.
RICS (Royal Institute of Chartered Surveyors). 2013. Cost Analysis and Benchmarking, RICS guidance note, global. 1st ed. London: RICS.
Salvado, F., N. M. de Almeida, and A. V. e Azevedo. 2019. “Historical analysis of the economic life-cycle performance of public school buildings.” Build. Res. Inf. 47 (7): 813–832. https://doi.org/10.1080/09613218.2019.1612730.
Salvado, F., N. M. de Almeida, and A. V. e Azevedo. 2020a. “Aligning financial and functional equivalent depreciations rates of building assets.” Eng. Constr. Archit. Manage. 27 (2): 441–457. https://doi.org/10.1108/ECAM-03-2019-0152.
Salvado, F., N. M. de Almeida, and A. V. e Azevedo. 2020b. “Future-proofing and monitoring capital investments needs throughout the life cycle of building projects.” Sustainable Cities Soc. 59 (Aug): 102159. https://doi.org/10.1016/j.scs.2020.102159.
Schober, P., and C. Boer. 2018. “Correlation coefficients: Appropriate use and interpretation.” Int. J. Anesthesia Analg. 126 (5): 1763–1768. https://doi.org/10.1213/ANE.0000000000002864.
Shahraki, A. F., O. P. Yadav, and H. Liao. 2017. “A review on degradation modelling and its engineering applications.” Int. J. Performability Eng. 13 (3): 299–314. https://doi.org/10.23940/ijpe.17.03.p6.299314.
Tavares, J., A. Silva, and J. de Brito. 2020. “Computational models applied to the service life prediction of external thermal insulation composite systems (ETICS).” J. Build. Eng. 27 (Jan): 100944. https://doi.org/10.1016/j.jobe.2019.100944.
Tran, H. D. 2016. “Markov-based reliability assessment for hydraulic design of concrete stormwater pipes.” J. Hydraul. Eng. 142 (7): 06016005. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001130.
van der Boomen, M., R. Schoenmeker, and A. Wolfert. 2018. “A life cycle costing approach for discounting in age and interval replacement optimisation models for civil infrastructure assets.” Struct. Infrastruct. Eng. 14 (1): 1–13. https://doi.org/10.1080/15732479.2017.1329843.
Vieira, J., M. Cabral, N. Almeida, J. G. Silva, and D. Covas. 2020. “Novel methodology for efficiency-based long-term investment planning in water infrastructures.” Struct. Infrastruct. Eng. 16 (12): 1654–1668. https://doi.org/10.1080/15732479.2020.1722715.
Zhang, G., P. Kalutara, S. Setunge, and R. Wakefield. 2010. “Development of a monetary and engineering combined metric for community building valuation.” In Proc., 2010 Int. Conf. on Construction and Real Estate Management, edited by Y. Wang, J. Yang, G. Shen, and J. Wong, 1–6. Beijing: China Architecture and Building Press.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 35 • Issue 6 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Dec 1, 2020
Accepted: Jun 21, 2021
Published online: Aug 19, 2021
Published in print: Dec 1, 2021
Discussion open until: Jan 19, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Vajira Wickramasinghe, W. P. S. Dias, Dilan Robert, Sujeeva Setunge, The Relative Influence of Environmental Factors Compared to Age on Building Element Degradation, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4521, 37, 6, (2023).
- Vajira Wickramasinghe, W. P. S. Dias, Huu Tran, Sujeeva Setunge, Using Snapshot Data of Deficiency and Generic Deterioration to Predict the Degradation of Building Elements, Journal of Performance of Constructed Facilities, 10.1061/(ASCE)CF.1943-5509.0001743, 36, 5, (2022).