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Ahn, S, Crouch, L, Kim, T W and Rameezdeen, R (2020) Comparison of Worker Safety Risks between Onsite and Offsite Construction Methods: A Site Management Perspective. Journal of Construction Engineering and Management, 146(09).

Al-Bayati, A J and Panzer, L (2020) Reducing Damages to Underground Utilities: Importance of Stakeholders’ Behaviors. Journal of Construction Engineering and Management, 146(09).

Ariyachandra, M R M F and Brilakis, I (2020) Detection of Railway Masts in Airborne LiDAR Data. Journal of Construction Engineering and Management, 146(09).

Ballesteros-Pérez, P, Sanz-Ablanedo, E, Cerezo-Narváez, A, Lucko, G, Pastor-Fernández, A, Otero-Mateo, M and Contreras-Samper, J P (2020) Forecasting Accuracy of In-Progress Activity Duration and Cost Estimates. Journal of Construction Engineering and Management, 146(09).

Chen, T, Hu, H and Zhu, F (2020) Developing a Hierarchical Road Layout Method for Large-Scale Construction Site. Journal of Construction Engineering and Management, 146(09).

Dou, Y, Xue, X, Wu, C, Luo, X and Wang, Y (2020) Interorganizational Diffusion of Prefabricated Construction Technology: Two-Stage Evolution Framework. Journal of Construction Engineering and Management, 146(09).

Fan, C (2020) Defect Risk Assessment Using a Hybrid Machine Learning Method. Journal of Construction Engineering and Management, 146(09).

  • Type: Journal Article
  • Keywords:
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001897
  • 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 (r=−0.85). 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.

Franz, B, Molenaar, K R and Roberts, B A M (2020) Revisiting Project Delivery System Performance from 1998 to 2018. Journal of Construction Engineering and Management, 146(09).

Gurmu, A T and Ongkowijoyo, C S (2020) Stochastic-Based Model for Setting Formwork-Productivity Baseline. Journal of Construction Engineering and Management, 146(09).

Jazayeri, E and Dadi, G B (2020) Hazard Recognition and Risk Perception Skills among Union Electricians. Journal of Construction Engineering and Management, 146(09).

Kar, S and Jha, K N (2020) Examining the Effect of Material Management Issues on the Schedule and Cost Performance of Construction Projects Based on a Structural Equation Model: Survey of Indian Experiences. Journal of Construction Engineering and Management, 146(09).

Kim, T, Lee, D, Cha, M, Lim, H, Lee, M, Cho, H and Kang, K (2020) Simulation-Based Lift Planning Model for the Lift Transfer Operation System. Journal of Construction Engineering and Management, 146(09).

Love, P E D, Matthews, J and Fang, W (2020) Rework in Construction: A Focus on Error and Violation. Journal of Construction Engineering and Management, 146(09).

Ma, H, Zhang, H and Chang, P (2020) 4D-Based Workspace Conflict Detection in Prefabricated Building Constructions. Journal of Construction Engineering and Management, 146(09).

Moon, H, Kim, K, Lee, H, Park, M, Williams, T P, Son, B and Chun, J (2020) Cost Performance Comparison of Design-Build and Design-Bid-Build for Building and Civil Projects Using Mediation Analysis. Journal of Construction Engineering and Management, 146(09).

Neve, H H, Wandahl, S, Lindhard, S, Teizer, J and Lerche, J (2020) Determining the Relationship between Direct Work and Construction Labor Productivity in North America: Four Decades of Insights. Journal of Construction Engineering and Management, 146(09).

Ogunrinde, O, Amirkhanian, A, Corley, M and Nnaji, C (2020) Effect of Nighttime Construction on Quality of Asphalt Paving. Journal of Construction Engineering and Management, 146(09).

Simmons, D R, McCall, C and Clegorne, N A (2020) Leadership Competencies for Construction Professionals as Identified by Construction Industry Executives. Journal of Construction Engineering and Management, 146(09).

Townsend, R and Gershon, M (2020) Attaining Successful Construction Project Execution Through Personnel and Communication. Journal of Construction Engineering and Management, 146(09).

Xue, J, Shen, G Q, Yang, R J, Zafar, I and Ekanayake, E M A C (2020) Dynamic Network Analysis of Stakeholder Conflicts in Megaprojects: Sixteen-Year Case of Hong Kong-Zhuhai-Macao Bridge. Journal of Construction Engineering and Management, 146(09).

Yap, J B H, Rou Chong, J, Skitmore, M and Lee, W P (2020) Rework Causation that Undermines Safety Performance during Production in Construction. Journal of Construction Engineering and Management, 146(09).

Zhang, Y, Lei, Z, Han, S, Bouferguene, A and Al-Hussein, M (2020) Process-Oriented Framework to Improve Modular and Offsite Construction Manufacturing Performance. Journal of Construction Engineering and Management, 146(09).