Defect Risk Assessment Using a Hybrid Machine Learning Method
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 9
Abstract
Defects pose considerable risks to construction projects in terms of both cost and quality, and identifying defects thus is crucial to effective construction quality management. In this study, data for 45 cases were obtained from the Public Construction Management Information System (PCMIS) of Taiwan. A combined machine learning method comprising association rule mining and a Bayesian network was employed to identify the relationships between defects as well as their occurrence probabilities. A total of 33 association rules and 11 high-risk defects were detected. The Swiss cheese model (SCM) was used to formulate four defensive layers and analyze the high-probability, strong-correlation, and multipath characteristics of high-risk defects. Specifically, the defect quality control inspection not implemented had the highest risk values. In the correlation analysis, more high-risk defects meant reduced inspection scores and construction quality; risk values and inspection scores had a strong negative correlation (). This study proposes innovative hybrid machine learning to evaluate the risks of defects, and the SCM was implemented to establish the risk factors and hierarchical relationships of the defects to determine their priority order in management. Future studies should analyze the time series of defects and employ sequential data to forecast their order of occurrence and relationships at different times, thereby increasing the understanding of dynamic construction projects.
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Data Availability Statement
Data generated or analyzed in this study are available from the corresponding author upon request.
Acknowledgments
The author is most grateful to the three anonymous referees and the editor, Professor Tricia Kershaw, for their helpful constructive comments which helped improve this manuscript. The author also express sincere gratitude to Associate Professor Shih-Hsu Wang in the Department of Civil Engineering, Military Academy (R.O.C.) for his useful suggestions and knowledge contribution to this paper.
References
Abdul-Rahman, H., C. Wang, L. C. Wood, and Y. M. Khoo. 2014. “Defects in affordable housing projects in Klang Valley, Malaysia.” J. Perform. Constr. Facil. 28 (2): 272–285. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000413.
Agrawal, R., and R. Srikant. 1994. “Fast algorithms for mining association rules.” In Proc., 20th Int. Conf. on Very Large Data Bases, 487–499. Burlington, MA: Morgan Kaufmann Publishers.
Aguinis, H., L. E. Forcum, and H. Joo. 2013. “Using market basket analysis in management research.” J. Manage. 39 (7): 1799–1824. https://doi.org/10.1177/0149206312466147.
Ahzahar, N., N. A. Karim, S. H. Hassan, and J. Eman. 2011. “A study of contribution factors to building failures and defects in construction industry.” Procedia Eng. 20 (2011): 249–255. https://doi.org/10.1016/j.proeng.2011.11.162.
Aljassmi, H., and S. Han. 2013. “Analysis of causes of construction defects using fault trees and risk importance measures.” J. Constr. Eng. Manage. 139 (7): 870–880. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000653.
Aljassmi, H., S. Han, and S. Davis. 2014. “Project pathogens network: New approach to analyzing construction-defects-generation mechanisms.” J. Constr. Eng. Manage. 140 (1): 04013028. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000774.
Almahmoud, E. S., H. K. Doloi, and K. Panuwatwanich. 2012. “Linking project health to project performance indicators: Multiple case studies of construction projects in Saudi Arabia.” Int. J. Project Manage. 30 (3): 296–307. https://doi.org/10.1016/j.ijproman.2011.07.001.
Aurélien, G. 2017. Hands-on machine learning with Scikit-Learn and TensorFlow. Sebastopol, CA: O’Reilly Media.
Baralis, E., L. Cagliero, T. Cerquitelli, P. Garza, and M. Marchetti. 2011. “CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules.” Knowl. Inf. Syst. 28 (2): 283–310. https://doi.org/10.1007/s10115-010-0359-z.
Brunsson, N. 1985. The irrational organization: Irrationality as a basis for organizational action and change. Chichester, UK: Wiley.
Burati, J. L., J. J. Farrington, and W. B. Ledbetter. 1992. “Causes of quality deviations in design and construction.” J. Constr. Eng. Manage. 118 (1): 34–49. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:1(34).
Cheng, C. W., C. C. Lin, and S. S. Leu. 2010. “Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry.” Saf. Sci. 48 (4): 436–444. https://doi.org/10.1016/j.ssci.2009.12.005.
Cheng, Y., W. D. Yu, and Q. Li. 2015. “GA-based multi-level association rule mining approach for defect analysis in the construction industry.” Autom. Constr. 51 (Mar): 78–91. https://doi.org/10.1016/j.autcon.2014.12.016.
Chou, J. S., C. F. Tsai, and Y. H. Lu. 2013. “Project dispute prediction by hybrid machine learning techniques.” J. Civ. Eng. Manage. 19 (4): 505–517. https://doi.org/10.3846/13923730.2013.768544.
Cohen, E., M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J. D. Ullman, and C. Yang. 2001. “Finding interesting associations without support pruning.” IEEE T. Knowl. Data Eng. 13 (1): 64–78. https://doi.org/10.1109/69.908981.
Das, S., and M. Y. L. Chew. 2011. “Generic method of grading building defects using FMECA to improve maintainability decisions.” J. Perform. Constr. Facil. 25 (6): 522–533. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000206.
Dong, S., Z. Zhong, P. Hao, W. Zhang, J. Chen, Y. Lei, and A. Schneider. 2018. “Mining multiple association rules in LTPP database: An analysis of asphalt pavement thermal cracking distress.” Constr. Build. Mater. 191 (10): 837–852. https://doi.org/10.1016/j.conbuildmat.2018.09.162.
Forcada, N., M. Macarulla, A. Fuertes, and M. Casals. 2012. “Influence of building type on post-handover defects in housing.” J. Perform. Constr. Facil. 26 (4): 433–440. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000225.
Forcada, N., M. Macarulla, and P. E. D. Love. 2013. “Assessment of residential defects at post-handover.” J. Constr. Eng. Manage. 139 (4): 372–378. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000603.
Georgiou, J. 2010. “Verification of a building defect classification system for housing.” Struct. Surv. 28 (5): 370–383. https://doi.org/10.1108/02630801011089164.
Georgiou, J., P. E. D. Love, and J. Smith. 1999. “A comparison of defects in houses constructed by owners and registered builders in the Australian State of Victoria.” Struct. Surv. 17 (3): 160–169. https://doi.org/10.1108/02630809910291343.
Gerassis, S., J. E. Martin, J. T. Garcia, A. Saavedra, and J. Taboada. 2017. “Bayesian decision tool for the analysis of occupational accidents in the construction of embankments.” J. Constr. Eng. Manage. 143 (2): 04016093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001225.
Giudici, P., and G. Passerone. 2002. “Data mining of association structures to model consumer behavior.” Comput. Stat. Data Anal. 38 (4): 533–541. https://doi.org/10.1016/S0167-9473(01)00077-9.
Guo, S. Y., C. H. Xiong, and P. S. Gong. 2018. “A real-time control approach based on intelligent video surveillance for violations by construction workers.” J. Civ. Eng. Manage. 24 (1): 67–78. https://doi.org/10.3846/jcem.2018.301.
Han, J., and M. Kamber. 2006. Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann.
Heckerman, D., A. Mamdani, and M. P. Wellman. 1995. “Real-world applications of Bayesian networks.” Commun. ACM 38 (3): 24–26. https://doi.org/10.1145/203330.203334.
Ilozor, B. D., M. I. Okoroh, and C. E. Egbu. 2004. “Understanding residential house defects in Australia from the State of Victoria.” Build. Environ. 39 (3): 327–337. https://doi.org/10.1016/j.buildenv.2003.07.002.
Jiang, J. J., L. Zhang, Y. Q. Wang, K. Zhang, D. X. Yang, and W. He. 2011. “Association rules analysis of human factor events based on statistics method in digital nuclear power plant.” Saf. Sci. 49 (6): 946–950. https://doi.org/10.1016/j.ssci.2011.02.010.
Josephson, P. E., and Y. Hammarlund. 1999. “The causes and costs of defects in construction: A study of seven building projects.” Autom. Constr. 8 (6): 681–687. https://doi.org/10.1016/S0926-5805(98)00114-9.
Kabir, G., S. Tesfamariam, A. Francisque, and R. Sadiq. 2015. “Evaluating risk of water mains failure using a Bayesian belief network model.” Eur. J. Oper. Res. 240 (1): 220–234. https://doi.org/10.1016/j.ejor.2014.06.033.
Karim, K., M. Marosszeky, and S. Davis. 2006. “Managing subcontractor supply chain for quality in construction.” Eng. Constr. Archit. Manage. 13 (1): 27–42. https://doi.org/10.1108/09699980610646485.
Kim, B. C., and K. F. Reinschmidt. 2009. “Probabilistic forecasting of project duration using Bayesian inference and the beta distribution.” J. Constr. Eng. Manage. 135 (3): 178–186. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:3(178).
Kolodner, J. L. 1993. Case-based reasoning. San Francisco: Morgan Kaufmann.
Larose, D. T. 2005. Discovering knowledge in data. Hoboken, NJ: Wiley.
Lee, S., Y. Cha, S. Han, and C. Hyun. 2019. “Application of association rule mining and social network analysis for understanding causality of construction defects.” Sustainability 11 (3): 618. https://doi.org/10.3390/su11030618.
Lee, S., S. Han, and C. Hyun. 2016. “Analysis of causality between defect causes using association rule mining.” Int. J. Civ. Environ. Struct. Constr. Archit. Eng. 10 (5): 654–657. https://doi.org/10.5281/zenodo.1125579.
Liao, P. C., H. N. Chen, and X. W. Luo. 2019. “Fusion model for hazard association network development: A case in elevator installation and maintenance.” KSCE J. Civ. Eng. 23 (4): 1451–1465. https://doi.org/10.1007/s12205-019-0646-5.
Lin, C. L., and C. L. Fan. 2018. “Examining association between construction inspection grades and critical defects using data mining and fuzzy logic.” J. Civ. Eng. Manage. 24 (4): 301–317. https://doi.org/10.3846/jcem.2018.3072.
Love, P. E. D. 2002. “Influence of project type and procurement method on rework costs in building construction projects.” J. Constr. Eng. Manage. 128 (1): 18–29. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:1(18).
Luo, X., H. Li, F. Dai, and D. Cao. 2017. “Hierarchical Bayesian model of worker response to proximity warnings of construction safety hazards: Toward constant review of safety risk control measures.” J. Constr. Eng. Manage. 143 (6): 04017006. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001277.
Ma, Y. F., L. Wang, J. R. Zhang, Y. B. Xiang, and Y. M. Liu. 2014. “Bridge remaining strength prediction integrated with Bayesian network and in situ load testing.” J. Bridge Eng. 19 (10): 04014037. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000611.
Macarulla, M., N. Forcada, M. Casals, M. Gangolells, A. Fuertes, and X. Roca. 2013. “Standardizing housing defects: Classification, validation, and benefits.” J. Constr. Eng. Manage. 139 (8): 968–976. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000669.
Maeda, K., S. Takahashi, T. Ogawa, and M. Haseyama. 2017. “Distress classification of road structures via adaptive Bayesian network model selection.” J. Comput. Civ. Eng. 31 (5): 04017044. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000686.
Mansingh, G., K. Osei-Bryson, and H. Reichgelt. 2011. “Using ontologies to facilitate post-processing of association rules by domain experts.” Inf. Sci. 181 (3): 419–434. https://doi.org/10.1016/j.ins.2010.09.027.
McCabe, B., S. M. AbouRizk, and R. Goebel. 1998. “Belief networks for construction performance diagnostics.” J. Comput. Civ. Eng. 12 (2): 93–100. https://doi.org/10.1061/(ASCE)0887-3801(1998)12:2(93).
Mills, A., P. E. D. Love, and P. Williams. 2009. “Defects cost in residential construction.” J. Constr. Eng. Manage. 135 (1): 12–16. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:1(12).
Mirabadi, A., and S. Sharifian. 2010. “Application of association rules in Iranian Railways (RAI) accident data analysis.” Saf. Sci. 48 (10): 1427–1435. https://doi.org/10.1016/j.ssci.2010.06.006.
Murphy, K. 2001. “The Bayes net toolbox for Matlab.” Comp. Sci. Stat. 33 (2): 1024–1034.
Nasir, D., B. McCabe, and L. Hartono. 2003. “Evaluating risk in construction schedule model (ERIC-S): Construction schedule risk model.” J. Constr. Eng. Manage. 129 (5): 518–527. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:5(518).
Nguyen, L. D., D. Q. Tran, and M. P. Chandrawinata. 2016. “Predicting safety risk of working at heights using Bayesian networks.” J. Constr. Eng. Manage. 142 (9): 04016041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001154.
Olafsson, S., X. Li, and S. Wu. 2008. “Operations research and data mining.” Eur. J. Oper. Res. 187 (3): 1429–1448. https://doi.org/10.1016/j.ejor.2006.09.023.
Pan, W., and R. Thomas. 2015. “Defects and their influencing factors of posthandover new-build homes.” J. Perform. Constr. Facil. 29 (4): 04014119. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000618.
Pearl, J. 2014. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco: Morgan Kaufmann.
Reason, J. 1990. Human error. Cambridge, UK: Cambridge University Press.
Reason, J. 1997. Managing the risks of organizational accidents. Aldershot, UK: Ashgate.
Rish, I. 2001. “An empirical study of the naïve Bayes classifier.” In Proc. Int. Conf. of the IJCAI-01Workshop on Empirical Methods in Artificial Intelligence, 41–46. Ossining, NY: Thomas J Watson Research Center.
Shin, D. P., Y. J. Park, and J. Seo. 2018. “Association rules mined from construction accident data.” KSCE J. Civ. Eng. 22 (4): 1027–1039. https://doi.org/10.1007/s12205-017-0537-6.
Straub, D., and A. D. Kiureghian. 2010. “Bayesian network enhanced with structural reliability methods: Methodology.” J. Constr. Eng. Manage. 136 (10): 1248–1258. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000173.
Tesfamariam, S., and B. Martín-Pérez. 2008. “Bayesian belief network to assess carbonation-induced corrosion in reinforced concrete.” J. Constr. Eng. Manage. 20 (11): 707–717. https://doi.org/10.1061/(ASCE)0899-1561(2008)20:11(707.
Tsang, S. L., R. T. Aoieong, and S. M. Ahmed. 2004. “The use of process cost model (PCM) for measuring quality costs of construction projects: Model testing.” Constr. Econ. Manage. 22 (3): 263–275. https://doi.org/10.1080/0144619032000064091.
Varlamis, I., I. Apostolakis, D. Sifaki-Pistolla, N. Dey, V. Georgoulias, and C. Lionis. 2017. “Application of data mining techniques and data analysis methods to measure cancer morbidity and mortality data in a regional cancer registry: The case of the island of Crete, Greece.” Comput. Methods Programs Biomed. 145 (Jul): 73–83. https://doi.org/10.1016/j.cmpb.2017.04.011.
Verma, A., S. D. Khan, J. Maiti, and O. B. Krishna. 2014. “Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports.” Saf. Sci. 70 (Dec): 89–98. https://doi.org/10.1016/j.ssci.2014.05.007.
Wang, X. H., X. F. Huang, Y. Luo, J. J. Pei, and M. Xu. 2018. “Improving workplace hazard identification performance using data mining.” J. Constr. Eng. Manage. 144 (8): 04018068. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001505.
Wilson, P. F., D. Dell, and G. F. Anderson. 1993. Root cause analysis: A tool for total quality management. Milwaukee: ASQC Quality Press.
Xiao, F., and C. Fan. 2014. “Data mining in building automation system for improving building operational performance.” Energy Build. 75 (Jun): 109–118. https://doi.org/10.1016/j.enbuild.2014.02.005.
Zhang, C., and S. Zhang. 2002. Association rule mining: Models and algorithms. New York: Springer.
Zhang, S., C. Du, W. Sa, and C. Wang. 2014. “Bayesian-based hybrid simulation approach to project completion forecasting for underground construction.” J. Constr. Eng. Manage. 140 (1): 04013031. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000764.
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Journal of Construction Engineering and Management
Volume 146 • Issue 9 • September 2020
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©2020 American Society of Civil Engineers.
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Received: Aug 16, 2019
Accepted: Apr 16, 2020
Published online: Jun 22, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 22, 2020
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