Technical Papers
Sep 25, 2023

The Relative Influence of Environmental Factors Compared to Age on Building Element Degradation

Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 6

Abstract

Age has often been the only factor considered in predictive models of degradation. This study, however, assesses the influence on degradation of coastal exposure for concrete beams, level of utilization for rendered cement floors, and rainfall for timber windows. First, the difference between random data and data categorized on the basis of high or low levels of environmental factors was explored to establish whether they had a perceptible influence on degradation. Next, five types of models were explored for fitting the data and making predictions: namely Markov chain, multiple linear regression, simple neural network, deep neural network, and random forest. Among the environmental factors, coastal exposure on concrete beams had the greatest influence, while rainfall on timber windows the least. Random forest modeling was the most accurate and was also explored using the local interpretable model-agnostic explanation (LIME) technique, which revealed that the environmental factor effects were more evident during the mid-life of elements rather than at the early or late stages. Including environmental factors in degradation models in addition to element age will increase their accuracy and portability.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

AS (Standards Australia). 2018. Concrete structures. AS 3600. Sydney, Australia: AS.
Bargiela, A., W. Pedrycz, and T. Nakashima. 2007. “Multiple regression with fuzzy data.” Fuzzy Sets Syst. 158 (19): 2169–2188. https://doi.org/10.1016/j.fss.2007.04.011.
Ben Seghier, M. E., J. A. Corriea, J. Jafari-Asl, A. Malekjafarian, V. Plevris, and N. T. Trung. 2021a. “On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm.” Neural Comput. Appl. 33 (23): 15969–15985. https://doi.org/10.1007/s00521-021-06199-w.
Ben Seghier, M. E., H. Ouaer, M. A. Ghriga, N. A. Menad, and D. K. Thai. 2021b. “Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete.” Neural Comput. Appl. 33 (12): 6905–6920. https://doi.org/10.1007/s00521-020-05466-6.
Biau, G., and E. Scornet. 2016. “A random forest guided tour.” Test 25 (2): 197–227. https://doi.org/10.1007/s11749-016-0481-7.
Blocken, B., and J. Carmeliet. 2004. “A review of wind-driven rain research in building science.” J. Wind Eng. Ind. Aerodyn. 92 (13): 1079–1130. https://doi.org/10.1016/j.jweia.2004.06.003.
Caelen, O. 2017. “A Bayesian interpretation of the confusion matrix.” Ann. Math. Artif. Intell. 81 (3): 429–450. https://doi.org/10.1007/s10472-017-9564-8.
Castro, P. 1999. “The atmospheric corrosion performance of reinforced concrete.” Corros. Rev. 17 (5–6): 333–382. https://doi.org/10.1515/CORRREV.1999.17.5-6.333.
Caterini, A. L., and D. E. Chang. 2018. Deep neural networks in a mathematical framework. Cham: Springer.
Ching, F. D., and S. R. Winkel. 2016. Building codes illustrated: A guide to understanding the 2015 international building code. Hoboken, NJ: Wiley.
de Brito, J., and A. Silva. 2020. “Life cycle prediction and maintenance of buildings.” Buildings 10 (6): 112. https://doi.org/10.3390/buildings10060112.
Department of Agriculture Sri Lanka. 2021. “Agro climatic zones.” Accessed September 25, 2021. https://doa.gov.lk/index.php/en/weather-climate.
De Silva, D. T., S. Setunge, and H. Tran. 2021. “Effect of runtime on the deterioration of HVAC components in building services.” ASCE J. Infrastruct. Syst. 28 (1): 04021049. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000656.
Dias, W. P. S. 2013. “Factors influencing the service life of buildings.” Eng. J. Inst. Eng. Sri Lanka 46 (4): 1–7. https://doi.org/10.4038/engineer.v46i4.6801.
Dong, B., Z. Li, and G. Mcfadden. 2015. “An investigation on energy-related occupancy behavior for low-income residential buildings.” Sci. Technol. Built Environ. 21 (6): 892–901. https://doi.org/10.1080/23744731.2015.1040321.
Edirisinghe, R., S. Setunge, and G. Zhang. 2015. “Markov model-based building deterioration prediction and ISO factor analysis for building management.” J. Manage. Eng. 31 (6): 4015009. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000359.
Ekanayake, I. U., D. P. P. Meddage, and U. Rathnayake. 2022. “A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP).” Case Stud. Constr. Mater. 16 (Jun): e01059. https://doi.org/10.1016/j.cscm.2022.e01059.
Feng, X., D. Yan, and T. Hong. 2015. “Simulation of occupancy in buildings.” Energy Build. 87 (Jan): 348–359. https://doi.org/10.1016/j.enbuild.2014.11.067.
ISO (International Standards Organization). 2011. Buildings and constructed assets—Service life planning—Part 1: General principles and framework. ISO 15686-1:2011. Geneva: ISO.
Jiefan, G., X. Peng, P. Zhihong, C. Yongbao, J. Ying, and C. Zhe. 2018. “Extracting typical occupancy data of different buildings from mobile positioning data.” Energy Build. 180 (Dec): 135–145. https://doi.org/10.1016/j.enbuild.2018.09.002.
Labeodan, T., W. Zeiler, G. Boxem, and Y. Zhao. 2015. “Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation.” Energy Build. 93 (Apr): 303–314. https://doi.org/10.1016/j.enbuild.2015.02.028.
Lee, E., D. Braines, M. Stiffler, A. Hudler, and D. Harborne. 2019. “Developing the sensitivity of LIME for better machine learning explanation.” In Vol. 11006 of Proc., Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100610. Bellingham, WA: International Society for Optics and Photonics. https://doi.org/10.1117/12.2520149.
Maia, M., R. Morais, and A. Silva. 2020. “Application of the factor method to the service life prediction of window frames.” Eng. Fail. Anal. 109 (Jan): 104245. https://doi.org/10.1016/j.engfailanal.2019.104245.
Miikkulainen, R., J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, and N. Duffy. 2019. “Evolving deep neural networks.” In Artificial intelligence in the age of neural networks and brain computing, 293–312. London: Academic Press.
Mydin, M. 2014. “Key performance indicator of building maintenance and its effect on the building life cycle.” Analele Universitatii’Eftimie Murgu′ 21 (1): 193–202.
Nabian, M. A., and H. Meidani. 2018. “Physics-driven regularisation of deep neural networks for enhanced engineering design and analysis.” J. Comput. Inf. Sci. Eng. 20 (1): 011006. https://doi.org/10.1115/1.4044507.
Nusrat, I., and S.-B. Jang. 2018. “A comparison of regularisation techniques in deep neural networks.” Symmetry 10 (11): 648. https://doi.org/10.3390/sym10110648.
Nwankpa, C. E. 2020. “Advances in optimisation algorithms and techniques for deep learning.” Adv. Sci. Technol. Eng. Syst. J. 5 (5): 563–577. https://doi.org/10.25046/aj050570.
Perez, H., J. 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.
Rogachev, A., and E. Melikhova.2020. “Automation of the process of selecting hyperparameters for artificial neural networks for processing retrospective text information.” IOP Conf. Ser.: Earth Environ. Sci. 577 (1): 012012. https://doi.org/10.1088/1755-1315/577/1/012012.
Samad, M., M. M. M. Aheeyar, J. R. Olid, and I. Arulingam. 2017. The political and institutional context of the water sector in Sri Lanka: An overview. Luxembourg: Publications Office of the European Union.
Seghier, M. E., V. Plevris, and G. Solorzano. 2022. “Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes.” Structures 44 (Oct): 261–273. https://doi.org/10.1016/j.istruc.2022.08.007.
Sousa, V., T. D. Pereira, and I. Meireles. 2015. “Modeling the degradation rate of the wood frame doors and windows of the National Palace of Sintra, Portugal.” J. Perform. Constr. Facil. 30 (2): 04015010. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000747.
Srikanth, I., and M. Arockiasamy. 2020. “Deterioration models for prediction of remaining useful life of timber and concrete bridges: A review.” J. Traffic Transp. Eng. 7 (2): 152–173. https://doi.org/10.1016/j.jtte.2019.09.005.
Tavares, J., A. Silva, and D. J. 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.
Wickramasinghe, V., W. P. S. Dias, and S. Setunge. 2021. “Deterioration rates of building component groups using nominal replacement costs of components.” J. Perform. Constr. Facil. 35 (6): 04021074. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001648.
Wickramasinghe, V., W. P. S. Dias, H. D. Tran, and S. Setunge. 2022. “Using snapshot data of deficiency and generic deterioration for predicting the degradation of building elements.” J. Perform. Constr. Facil. 36 (5): 04022042. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001743.
Zhang, H., and D. W. R. Marsh. 2018. “Generic Bayesian network models for making maintenance decisions from available data and expert knowledge.” J. Risk Reliab. 232 (5): 505–523.

Information & Authors

Information

Published In

Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 37Issue 6December 2023

History

Received: Mar 14, 2023
Accepted: Jul 31, 2023
Published online: Sep 25, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 25, 2024

Permissions

Request permissions for this article.

ASCE Technical Topics:

Authors

Affiliations

Vajira Wickramasinghe [email protected]
Research Student, Dept. of Civil Engineering, Univ. of Moratuwa, Moratuwa 10400, Sri Lanka. Email: [email protected]
W. P. S. Dias [email protected]
Emeritus Professor, Dept. of Civil Engineering, Univ. of Moratuwa, Moratuwa 10400, Sri Lanka (corresponding author). Email: [email protected]
Dilan Robert [email protected]
Associate Professor, School of Engineering, RMIT Univ., Melbourne, VIC 3001, Australia. Email: [email protected]
Sujeeva Setunge [email protected]
Professor, School of Engineering, RMIT Univ., Melbourne, VIC 3001, Australia. Email: [email protected]

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.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share