Comprehensive Root Cause Analysis of Construction Defects Using Semisupervised Graph Representation Learning
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 9
Abstract
Quality is a substantial pillar of construction success, as its failure poses a significant threat to the construction budget and schedule. Effective root cause (RC) analysis allows for the early identification of issues leading to quality failure and proactive defect-prevention measures. This study puts forward a flexible RC analysis method that extracts useful information from construction nonconformance reports (NCRs) to identify the future trend RCs of construction defects by employing a novel graph representation learning (GRL) approach called node2vec. Node2vec was used to connect high-cost impact RC information based on shared construction defects to determine the RCs of the construction defects. Compared with the conventional RC analysis in the literature (i.e., association rule mining), the proposed node2vec offers three advantages: (1) responsiveness to large itemset, allowing its application across multiple projects with different data collection systems. (2) It receives richer semantic information (defect-related features, RC connectivity, and different cost impacts), enabling a more comprehensive understanding of underlying defects. (3) Prediction ability of future connectivity RCs, resulting in more efficient defect-prevention actions. In contrast to unsupervised RC analysis approaches, the incorporated word2vec prediction model allows the measurement of the prediction performance of related RCs (73% accuracy and 2.31% loss), providing a noticeably more accountable RC analysis and holistic defect prevention. This in turn facilitates the integration of the proposed approach with decisions regarding quality improvement in construction projects, thereby accelerating targeted decisions and interventions within related defect-prevention policies.
Practical Applications
The proposed RC analysis approach works as a construction quality management (CQM) tool to improve the quality of construction activities and reduce delays, cost overruns, and client satisfaction by incorporating three elements: (1) a comprehensive RC network that factors in the direct and indirect relationships between RCs of defects. (2) A node2vec algorithm that can dynamically find the relationship between different RCs, which facilitates the development of more efficient defect-prevention strategies to prevent similar occurrences. (3) A cosine similarity that allows practitioners to prioritize RCs for specific construction activities, enabling more efficient utilization of resources in the quality delivery of construction activities. This improves the efficiency of the related strategies and data-oriented decisions. Overall, the developed RC analysis method can aid construction managers in improving the quality of construction activities. Although the proposed node2vec approach for improving CQM is not a universal quality solution, the continuous RC analysis of the collected NCRs facilitates error prevention in the long term. Constant identification, documentation, and prioritization of the RCs of construction defects allow construction managers to devise more effective CQM plans that can gradually address and eliminate underlying issues, thereby directing their actions toward achieving the zero-defect goal for each activity.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abdelsalam, H. M. E., and M. M. Gad. 2009. “Cost of quality in Dubai: An analytical case study of residential construction projects.” Int. J. Project Manage. 27 (5): 501–511. https://doi.org/10.1016/j.ijproman.2008.07.006.
Abdul-Rahman, H. 1995. “The cost of non-conformance during a highway project: A case study.” Construct. Manage. Econ. 13 (1): 23–32. https://doi.org/10.1080/01446199500000004.
Ayhan, B. U., N. B. Doğan, and O. B. Tokdemir. 2020. “An association rule mining model for the assessment of the correlations between the attributes of severe accidents.” J. Civ. Eng. Manage. 26 (4): 315–330. https://doi.org/10.3846/jcem.2020.12316.
Balouchi, M., M. Gholhaki, and A. Niousha. 2019. “Reworks causes and related costs in construction: Case of Parand mass housing project in Iran.” Int. J. Q. Reliab. Manage. 36 (8): 1392–1408. https://doi.org/10.1108/IJQRM-06-2018-0155.
Barber, P., A. Graves, M. Hall, D. Sheath, and C. Tomkins. 2000. “Quality failure costs in civil engineering projects.” Int. J. Q. Reliab. Manage. 17 (4): 479–492. https://doi.org/10.1108/02656710010298544.
Battikha, M. G. 2008. “Reasoning mechanism for construction nonconformance root-cause analysis.” J. Constr. Eng. Manage. 134 (4): 280–288. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:4(280).
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, Y., W. Yu, and Q. Li. 2015. “GA-based multi-level association rule mining approach for defect analysis in the construction industry.” Autom. Constr. 51 (3): 78–91. https://doi.org/10.1016/j.autcon.2014.12.016.
Doğan, N. B. 2021. “Predicting the cost impacts of construction nonconformities using CBR-AHP and CBR-GA models.” Master of Science, Dept. of Civil Engineering, Middle East Technical Univ.
Doğan, N. B., B. U. Ayhan, G. Kazar, M. Saygili, Y. E. Ayözen, and O. B. Tokdemir. 2022. “Predicting the cost outcome of construction quality problems using case-based reasoning (CBR).” Buildings 12 (11): 1946. https://doi.org/10.3390/buildings12111946.
Dong, S., J. 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 (Jun): 837–852. https://doi.org/10.1016/j.conbuildmat.2018.09.162.
Fan, C.-L. 2020. “Defect risk assessment using a hybrid machine learning method.” J. Constr. Eng. Manage. 146 (9): 04020102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001897.
Forcada, N., M. Gangolells, M. Casals, and M. Macarulla. 2017. “Factors affecting rework costs in construction.” J. Constr. Eng. Manage. 143 (8): 04017032. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001324.
Grover, A., and J. Leskovec. 2016. “node2vec: Scalable feature learning for networks.” In Proc., ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 855–864. New York: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939754.
Gupta, V., P. Acharya, and M. Patwardhan. 2012. “Monitoring quality goals through lean Six-Sigma insures competitiveness.” Int. J. Prod. Perform. Manage. 61 (2): 194–203. https://doi.org/10.1108/17410401211194680.
Heravi, G., and A. Jafari. 2014. “Cost of quality evaluation in mass-housing projects in developing countries.” J. Constr. Eng. Manage. 140 (5): 04014004. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000837.
Hwang, B.-G., S. R. Thomas, C. T. Haas, C. H. Caldas, M. Asce, T. Haas, C. H. Caldas, C. T. Haas, and C. H. Caldas. 2009. “Measuring the impact of rework on construction cost performance.” J. Constr. Eng. Manage. 135 (3): 187–198. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:3(187).
Josephson, P.-E., and Y. Hammarlund. 1999. “The causes and costs of defects in construction.” Autom. Constr. 8 (6): 681–687. https://doi.org/10.1016/S0926-5805(98)00114-9.
Kazar, G., N. B. Doğan, B. U. Ayhan, and O. B. Tokdemir. 2022. “Quality failures—Based critical cost impact factors: Logistic regression analysis.” J. Constr. Eng. Manage. 148 (12): 04022138. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002412.
Larsen, J. K., G. Q. Shen, S. M. Lindhard, and T. D. Brunoe. 2016. “Factors affecting schedule delay, cost overrun, and quality level in public construction projects.” J. Manage. Eng. 32 (1): 04015032. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000391.
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.
Li, Y., and T. R. B. Taylor. 2014. “Modeling the impact of design rework on transportation infrastructure construction project performance.” J. Constr. Eng. Manage. 140 (9): 04014044. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000878.
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.
Liu, J., D. Shi, G. Li, Y. Xie, K. Li, B. Liu, and Z. Ru. 2020a. “Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers.” Energy Build. 216 (Jun): 109957. https://doi.org/10.1016/j.enbuild.2020.109957.
Liu, Q., G. Ye, Y. Feng, C. Wang, and Y. Peng. 2020b. “Case-based insights into rework costs of residential building projects in China.” Int. J. Construct. Manage. 20 (4): 347–355. https://doi.org/10.1080/15623599.2018.1484856.
Loushine, T. W., P. L. T. Hoonakker, P. Carayon, and M. J. Smith. 2006. “Quality and safety management in construction.” Total Q. Manage. Bus. Excellence 17 (9): 1171–1212. https://doi.org/10.1080/14783360600750469.
Love, P., L. Ika, J. Matthews, W. Fang, and B. Carey. 2022a. “The duality and paradoxical tensions of quality and safety: Managing error in construction projects.” In IEEE transaction engineering management, 1–8. New York: IEEE.
Love, P., and J. Smith. 2018. “Unpacking the ambiguity of rework in construction: Making sense of the literature.” Civ. Eng. Environ. Syst. 35 (1–4): 180–203. https://doi.org/10.1080/10286608.2019.1577396.
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).
Love, P. E. D., F. Ackermann, B. Carey, J. Morrison, M. Ward, and A. Park. 2016a. “Praxis of rework mitigation in construction.” J. Manage. Eng. 32 (5): 05016010. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000442.
Love, P. E. D., F. Ackermann, P. Teo, and J. Morrison. 2015. “From individual to collective learning: A conceptual learning framework for enacting rework prevention.” J. Constr. Eng. Manage. 141 (11): 05015009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001013.
Love, P. E. D., P. R. Davis, S. On Cheung, and Z. Irani. 2011. “Causal discovery and inference of project disputes.” IEEE Trans. Eng. Manage. 58 (3): 400–411. https://doi.org/10.1109/TEM.2010.2048907.
Love, P. E. D., D. J. Edwards, and J. Smith. 2016b. “Rework causation: Emergent theoretical insights and implications for research.” J. Constr. Eng. Manage. 142 (6): 04016010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001114.
Love, P. E. D., Z. Irani, and D. J. Edwards. 2004. “A rework reduction model for construction projects.” IEEE Trans. Eng. Manage. 51 (4): 426–440. https://doi.org/10.1109/TEM.2004.835092.
Love, P. E. D., and J. Matthews. 2022. “When ‘less is more’: The rationale for an adaptive toolbox to manage the risk and uncertainty of rework.” Dev. Built. Environ. 12 (Apr): 100084. https://doi.org/10.1016/j.dibe.2022.100084.
Love, P. E. D., J. Matthews, M. C. P. Sing, S. R. Porter, and W. Fang. 2022b. “State of science: Why does rework occur in construction? What are its consequences? And what can be done to mitigate its occurrence?” Engineering 2022 (Jun): 4. https://doi.org/10.1016/j.eng.2022.05.010.
Love, P. E. D., J. Smith, F. Ackermann, Z. Irani, W. Fang, H. Luo, and L. Ding. 2019. “Houston, we have a problem! Understanding the tensions between quality and safety in construction.” Prod. Plann. Control 30 (16): 1354–1365. https://doi.org/10.1080/09537287.2019.1617908.
Love, P. E. D., P. Teo, and J. Morrison. 2018a. “Revisiting quality failure costs in construction.” J. Constr. Eng. Manage. 144 (2): 05017020. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001427.
Love, P. E. D., P. Teo, and J. Morrison. 2018b. “Unearthing the nature and interplay of quality and safety in construction projects: An empirical study.” Saf. Sci. 103 (Apr): 270–279. https://doi.org/10.1016/j.ssci.2017.11.026.
Mostofi, F., V. Toğan, Y. Ayözen, and O. Behzat Tokdemir. 2022. “Predicting the impact of construction rework cost using an ensemble classifier.” Sustainability 14 (22): 14800. https://doi.org/10.3390/su142214800.
Mou, N., H. Wang, H. Zhang, and X. Fu. 2020. “Association rule mining method based on the similarity metric of tuple-relation in indoor environment.” IEEE Access 8 (Jun): 52041–52051. https://doi.org/10.1109/ACCESS.2020.2980952.
Rosenfeld, Y. 2014. “Root-cause analysis of construction-cost overruns.” J. Constr. Eng. Manage. 140 (1): 04013039. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000789.
Senouci, A., A. Alsarraj, M. Gunduz, and N. Eldin. 2017. “Analysis of change orders in Qatari construction projects.” Int. J. Construct. Manage. 17 (4): 280–292. https://doi.org/10.1080/15623599.2016.1211973.
Shingo, S. 1986. Zero quality control, source inspection and the Poka-Yoke system. New York: Productivity Press.
Wang, X., X. Huang, Y. Luo, 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.
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© 2023 American Society of Civil Engineers.
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Received: Dec 9, 2022
Accepted: Apr 12, 2023
Published online: Jun 24, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 24, 2023
ASCE Technical Topics:
- Analysis (by type)
- Concrete
- Construction engineering
- Construction management
- Construction methods
- Data analysis
- Defects and imperfections
- Ecosystems
- Engineering fundamentals
- Engineering materials (by type)
- Environmental engineering
- Failure analysis
- Materials characterization
- Materials engineering
- Methodology (by type)
- Project management
- Reinforced concrete
- Research methods (by type)
- Vegetation
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