Examining association between construction inspection grades and critical defects using data mining and fuzzy logic
This paper explores the relations between defect types and quality inspection grades of public construction projects in Taiwan. Altogether, 499 defect types (classified from 17,648 defects) were found after analyzing 990 construction projects from the Public Construction Management Information System of the public construction commission which is a government unit that administers all the public construction. The core of this research includes the following steps. (1) Data mining (DM) was used to derive 57 association rules which altogether contain 30 of the 499 defect types. (2) K-means clustering was used to regroup the 990 projects of two attributes (defect frequency and original grading score of each project) into four new quality classes, so the 990 projects can be more evenly distributed in the four new classes and the correctness and reliability of the following analyses can be ensured. (3) Finally analysis of variance (ANOVA), fuzzy logic, and correlation analysis were used to verify that the aforementioned 30 defect types are the important ones determining inspection grades. Results of this research can help stakeholders of construction projects paying more attention on the root causes of the critical defect types so to dramatically raise their management effectiveness.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Aguinis, H.; Forcum, L. E.; Joo, H. 2013. Using market basket analysis in management research, Journal of Management 39(7): 1799–1824. https://doi.org/10.1177/0149206312466147
Ahzahar, N.; Karim, N. A.; Hassan, S. H.; Eman, J. 2011. A study of contribution factors to building failures and defects in construction industry, in The 2nd International Building Control Conference, 11–12 July 2011, Penang, Malaysia.
Aljassmi, H.; Han, S. 2013. Analysis of causes of construction defects using fault trees and risk importance measures, Journal of Construction Engineering and Management 139(7): 870–880. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000653
Aljassmi, H.; Han, S.; Davis, S. 2014. Project pathogens network: new approach to analyzing construction-defects-generation Mechanisms, Journal of Construction Engineering and Management 140(1): 04013028. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000774
Aljassmi, H.; Han, S.; Davis, S. 2016. Analysis of the complex mechanisms of defect generation in construction projects, Journal of Construction Engineering and Management 142(2): 04015063. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001042
Amadore, A.; Bosurgi, G.; Pellegrino, O. 2014. Classification of measures from deflection tests by means of fuzzy clustering techniques, Construction and Building Materials 53: 173–181. https://doi.org/10.1016/j.conbuildmat.2013.11.094
Amiri, M.; Ardeshir, A.; Zarandi, M. H. F.; Soltanaghaei, E. 2016. Pattern extraction for high-risk accidents in the construction industry: a data-mining approach, International Journal of Injury Control and Safety Promotion 23(3): 264–276. https://doi.org/10.1080/17457300.2015.1032979
Atkinson, A. R. 1999. The role of human error in construction defects, Structural Survey 17(4): 231–236. https://doi.org/10.1108/02630809910303006
Baralis, E.; Cagliero, L.; Cerquitelli, T.; Garza, P.; Marchetti, M. 2011. CAS-MINE: providing personalized services in context-aware applications by means of generalized rules, Knowledge and Information Systems 28(2): 283–310. https://doi.org/10.1007/s10115-010-0359-z
Berry, M. J. A.; Linoff, G. S. 1997. Data mining techniques: for marketing, sales, and customer support. New York: John Wiley & Sons.
Chae, M. J.; Abraham, D. M. 2001. Neuro-fuzzy approaches for sanitary sewer pipeline condition assessment, Journal of Computing in Civil Engineering 15(1): 4–14. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:1(4)
Cheng, C. W.; Lin, C. C.; Leu, S. S. 2010. Use of association rules to explore cause–effect relationships in occupational accidents in the Taiwan construction industry, Safety Science 48(4): 436–444. https://doi.org/10.1016/j.ssci.2009.12.005
Cheng, H. D. 1996. Automated real-time pavement distress detection using fuzzy logic and neural networks, SPIE
Proceedings on Nondestructive Evaluation of Bridges and Highways, 140–151. http://dx.doi.org/10.1117/12.259131
Cheng, Y. M.; Leu, S. S. 2011. Integrating data mining with KJ method to classify bridge construction defects, Expert Systems with Applications 38: 7143–7150. https://doi.org/10.1016/j.eswa.2010.12.047
Cheng, Y.; Yu, W. D.; Li, Q. 2015. GA‐based multi-level association rule mining approach for defect analysis in the construction industry, Automation in Construction 51: 78–91. https://doi.org/10.1016/j.autcon.2014.12.016
Chew, M. Y. L. 2005. Defect analysis in wet areas of buildings, Construction and Building Materials 19(3): 165–173. https://doi.org/10.1016/j.conbuildmat.2004.07.005
Chou, J. S.; Cheng, M. Y.; Wu, Y. W. 2013. Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models, Expert Systems with Applications 40(6): 2263–2274. https://doi.org/10.1016/j.eswa.2012.10.036
Chou, J. S.; Hsu, S. C.; Lin, C. W.; Chang, Y. C. 2016. Classifying influential for project information to discover rule sets for project disputes and possible resolutions, International Journal of Project Management 34(8): 1706–1716. https://doi.org/10.1016/j.ijproman.2016.10.001
Coenen, F.; Goulbourne, G.; Leng, P. 2004. Tree structures for mining association rules, Data Mining and Knowledge Discovery 8: 25–51. https://doi.org/10.1023/B:DAMI.0000005257.93780.3b
Cohen, E.; Datar, M.; Fujiwara, S.; Gionis, A.; Indyk, P.; Motwani, R.; Ullman, J. D.; Yang, C. 2001. Finding interesting associations without support pruning, IEEE Transactions on Knowledge and Data Engineering 13(1): 64–78. https://doi.org/10.1109/69.908981
Cohen, J. 1988. Statistical power analysis for the behavioral sciences. 2nd ed. New Jersey: Lawrence Earlbaum Associates.
Forcada, N.; Macarulla, M.; Gangolells, M.; Casals, M.; Fuertes, A.; Roca, X. 2013a. Post-handover housing defects: sources and origins, Journal of Performance of Constructed Facilities 27(6): 756–762. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000368
Forcada, N.; Macarulla, M.; Love, P. E. D. 2013b. Assessment of residential defects at post-handover, Journal of Construction Engineering and Management 139(4): 372–378. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000603
Georgiou, J.; Love, P. E. D.; Smith, J. 1999. A comparison of defects in houses constructed by owners and registered builders in the Australian State of Victoria, Structural Survey 17(3): 160–169. https://doi.org/10.1108/02630809910291343
Gravetter, F. J.; Wallnau, L. B. 2007. Statistics for the behavioral sciences. 7th ed. Belmont: Wadsworth.
Han, J. W.; Pei, J.; Yin, Y.; Mao, R. 2000. Mining frequent patterns without candidate generation, in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 15–18 May 2000, Dallas, Texas. https://doi.org/10.1145/342009.335372
Han, J.; Kamber, M. 2006. Data mining: concepts and techniques. 2nd ed. Massachusetts: Morgan Kaufmann Publishers.
Han, J.; Wang, J.; Lu, Y. 2002. Mining top-k frequent closed patterns without minimum support, in Proceedings of the 2002 IEEE International Conference on Data Mining, 12–15 December 2002, Maebashi, Japan.
Hastie, T.; Tibshirani, R.; Friedman, J. 2009. The elements of statistical learning: data mining, inference and prediction. 2nd ed. Heidelberg: Springer. https://doi.org/10.1007/978-0-387-84858-7
Hong, T. P.; Lin, C. W.; Wu, Y. L. 2008. Incrementally fast updated frequent pattern trees, Expert Systems with Applications 34(4): 2424–2435. https://doi.org/10.1016/j.eswa.2007.04.009
Ilozor, B. D.; Okoroh, M. I.; Egbu, C. E.; Archicentre. 2004. Understanding residential house defects in Australia from the state of Victoria, Building and Environment 39(3): 327–337. https://doi.org/10.1016/j.buildenv.2003.07.002
Josephson, P. E.; Hammarlund, Y. 1999. The causes and costs of defects in construction: a study of seven building projects, Automation in Construction 8(6): 681–687. https://doi.org/10.1016/S0926-5805(98)00114-9
Karim, K.; Marosszeky, M.; Davis, S. 2006. Managing subcontractor supply chain for quality in construction, Engineering, Construction and Architectural Management 13(1): 27–42. https://doi.org/10.1108/09699980610646485
Koduru, H.; Xiao, F.; Amirkhanian, S.; Juang, C. 2010. Using fuzzy logic and expert system approaches in evaluating flexible pavement distress: case study, Journal of Transportation Engineering 136(2): 149–57. https://doi.org/10.1061/(ASCE)0733-947X(2010)136:2(149)
Kouris, I. N.; Makris, C. H.; Tsakalidis, A. K. 2005. Using information retrieval techniques for supporting data mining, Data & Knowledge Engineering 52: 353–383. https://doi.org/10.1016/j.datak.2004.07.004
La, P. T.; Le, B.; Vo, B. 2014. Incrementally building frequent closed itemset lattice, Expert Systems with Applications 41(6): 2703–2712. https://doi.org/10.1016/j.eswa.2013.11.002
Le, T. P.; Hong, T. P.; Vo, B.; Le, B. 2012. An efficient incremental mining approach based on IT-tree, in 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future, 27 February – 01 March 2012, Ho Chi Minh City, Vietnam.
Lee, S.; Han, S.; Hyun, C. 2016. Analysis of causality between defect causes using association rule mining, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering 10(5): 654–657.
Li, H.; Li, X. Y.; Luo, X. C.; Siebert, J. 2017. Investigation of the causality patterns of non-helmet use behavior of construction workers, Automation in Construction 80: 95–103. https://doi.org/10.1016/j.autcon.2017.02.006
Liao, C. W.; Perng, Y. H. 2008. Data mining for occupational injuries in the Taiwan construction industry, Safety Science 46(7): 1091–1102. https://doi.org/10.1016/j.ssci.2007.04.007
Love, P. E. D. 2002. Auditing the indirect consequences of rework in construction: a case based approach, Managerial Auditing Journal 17(3): 138–146. https://doi.org/10.1108/02686900210419921
Love, P. E. D.; Edwards, D. J. 2004. Forensic project management: the underlying causes of rework in construction projects, Civil Engineering and Environmental Systems 21(3): 207–228. https://doi.org/10.1080/10286600412331295955
Love, P. E. D.; Irani, Z. 2003. A project management quality cost information system for the construction industry, Information & Management 40(7): 649–661. https://doi.org/10.1016/S0378-7206(02)00094-0
Macarulla, M.; Forcada, N.; Casals, M.; Gangolells, M.; Fuertes, A.; Roca, X. 2013. Standardizing housing defects: classification, validation, and benefits, Journal of Construction Engineering and Management 139(8): 968–976. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000669
Maimon, O.; Rokach, L. 2010. Data mining and knowledge discovery handbook. 2nd ed. Heidelberg: Springer. https://doi.org/10.1007/978-0-387-09823-4
Mansingh, G.; Osei-Bryson, K.; Reichgelt, H. 2011. Using ontologies to facilitate post-processing of association rules by domain experts, Information Sciences 181(3): 419–434. https://doi.org/10.1016/j.ins.2010.09.027
Mills, A.; Love, P. E. D.; Williams, P. 2009. Defects cost in residential construction, Journal of Construction Engineering and Management 135(1): 12–16. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:1(12)
Olafsson, S.; Li, X.; Wu, S. 2008. Operations research and data mining, European Journal of Operational Research 187: 1429–1448. https://doi.org/10.1016/j.ejor.2006.09.023
Olson, D. L; Delen, D. 2008. Advanced data mining techniques. Heidelberg: Springer.
Pallant, J. 2013. SPSS survival manual: a step by step guide to data analysis using SPSS. 5th ed. New York: McGraw-Hill.
Shirkavand, I.; Lohne, J.; Lædre, O. 2016. Defects at handover in Norwegian construction projects, Procedia Social and Behavioral Sciences 226: 3–11.
Sinha, S. K.; Fieguth, P. W. 2006. Neuro-fuzzy network for the classification of buried pipe defects, Automation in Construction 15(1): 73–83. https://doi.org/10.1016/j.autcon.2005.02.005
Sommerville, J. 2007. Defects and rework in new build: an analysis of the phenomenon and drivers, Structural Survey 25(5): 391–407. https://doi.org/10.1108/02630800710838437
SS-ISO Svensk Standard, SS 02010: 1987. Kvalitet-Terminologi, SIS-Standardiserings-kommissionen i Sverige [Quality Terminology, SIS Standardization Commission in Sweden]. Sweden Standard, 1987.
Tang, S. L.; Aoieong, R. T.; Ahmed, S. M. 2004. The use of process cost model (PCM) for measuring quality costs of construction projects: Model testing, Construction Economics and Management 22(3): 263–275. https://doi.org/10.1080/0144619032000064091
Van, T. T.; Vo, B.; Le, B. 2014. IMSR_PreTree: an improved algorithm for mining sequential rules based on the prefix-tree, Vietnam Journal of Computer Science 1(2): 97–105. https://doi.org/10.1007/s40595-013-0012-3
Vieira, S. M.; Silva, A.; Sousa, J. M. C.; Brito, J.; Gaspar, P. L. 2015. Modelling the service life of rendered facades using fuzzy systems, Automation in Construction 51: 1–7. https://doi.org/10.1016/j.autcon.2014.12.011
Watt, D. S. 1999. Building pathology: principles & practice. Oxford: Blackwell Science.
Webster, N. 1828. Merriam-Webster: Definition of defect [online], [cited 12 December 2017]. Available from Internet: https://www.merriam-webster.com/dictionary/defect
Xiao, F.; Fan, C. 2014. Data mining in building automation system for improving building operational performance, Energy and Buildings 75: 109–118. https://doi.org/10.1016/j.enbuild.2014.02.005
Zadeh, L. A. 1965. Fuzzy sets, Information and Control 8(3): 338–535. https://doi.org/10.1016/S0019-9958(65)90241-X
Zaki, M. J. 2000. Scalable algorithms for association mining, IEEE Transactions on Knowledge and Data Engineering 12(3): 372–390. https://doi.org/10.1109/69.846291
Zaki, M. J.; Hsiao, C. J. 2005. Efficient algorithms for mining closed itemsets and their lattice structure, IEEE Transactions on Knowledge and Data Engineering 17(4): 462–478. https://doi.org/10.1109/TKDE.2005.60
Zhang, C.; Zhang, S. 2002. Association rule mining: models and algorithms. Heidelberg: Springer. https://doi.org/10.1007/3-540-46027-6
Zimmermann, H. J. 2001. Fuzzy set theory and its application. 4th ed. Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-94-010-0646-0