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Ayhan, M, Dikmen, I and Talat Birgonul, M (2021) Predicting the Occurrence of Construction Disputes Using Machine Learning Techniques. Journal of Construction Engineering and Management, 147(04).
- Type: Journal Article
- Keywords: Dispute prediction; Machine learning (ML); Data classification; Construction disputes; Dispute management; Project management;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002027
- Abstract:
The construction industry is overwhelmed by an increasing number and severity of disputes. The primary objective of this research is to predict the occurrence of disputes by utilizing machine learning (ML) techniques on empirical data. For this reason, variables affecting dispute occurrence were identified from the literature, and a conceptual model was developed to depict the common factors. Based on the conceptual model, a questionnaire was designed to collect empirical data from experts. Chi-square tests were conducted to reveal the associations between input variables and dispute occurrence. Alternative classification techniques were tested, and support vector machine (SVM) classifiers achieved the best average accuracy (90.46%). Ensemble classifiers combining the tested classification techniques were developed for enhanced prediction performance. Experimental results showed that the best ensemble classifier, obtained from the majority voting technique, can achieve 91.11% average accuracy. Based on Chi-square tests, the most influential factors on dispute occurrence were found as variations and unexpected events in projects. Other important predictors were all related to the skills of the parties involved. This study contributes to the construction dispute domain in three ways: (1) by proposing a conceptual model that combined the diverse efforts in the literature for identifying variables affecting dispute occurrence; (2) by highlighting the influential factors, such as response rate and communication skills, as indicators for potential disputes; and (3) by providing an empirical ML-based model with enhanced prediction capabilities that can function as an early-warning mechanism for decision-makers.
Bhandari, S, Hallowell, M R, Alruqi, W and Salas, R (2021) Modeling the Relationship between Personal Risk Tolerance, Work-Related Risk Tolerance, and Risk-Taking Behavior of Construction Workers. Journal of Construction Engineering and Management, 147(04).
Elsayegh, A and El-adaway, I H (2021) Holistic Study and Analysis of Factors Affecting Collaborative Planning in Construction. Journal of Construction Engineering and Management, 147(04).
Garcia, G, Golparvar-Fard, M, de la Garza, J M and Fischer, M (2021) Measuring Progress and Productivity in Model-Driven Engineering for Capital Project Delivery. Journal of Construction Engineering and Management, 147(04).
Hasan, M and Lu, M (2021) Error Propagation Model for Analyzing Project Labor Cost Budget Risks in Industrial Construction. Journal of Construction Engineering and Management, 147(04).
Herrera, R F, Mourgues, C, Alarcón, L F and Pellicer, E (2021) Analyzing the Association between Lean Design Management Practices and BIM Uses in the Design of Construction Projects. Journal of Construction Engineering and Management, 147(04).
Jia, B, Yang, Y, Xie, B and Yu, X (2021) Novel Geometric Control Technology for Precast Segmental Bridges. Journal of Construction Engineering and Management, 147(04).
Kar, S, Kothari, C and Jha, K N (2021) Developing an Optimum Material Procurement Schedule by Integrating Construction Program and Budget Using NSGA-II. Journal of Construction Engineering and Management, 147(04).
Kineber, A F, Othman, I, Oke, A E, Chileshe, N and Buniya, M K (2021) Impact of Value Management on Building Projects Success: Structural Equation Modeling Approach. Journal of Construction Engineering and Management, 147(04).
Kula, B and Ergen, E (2021) Implementation of a BIM-FM Platform at an International Airport Project: Case Study. Journal of Construction Engineering and Management, 147(04).
Love, P E D, Matthews, J and Fang, W (2021) Reflections on the Risk and Uncertainty of Rework in Construction. Journal of Construction Engineering and Management, 147(04).
Makul, N (2021) Modified Cost-Benefit Analysis of the Production of Ready-Mixed Self-Consolidating Concrete Prepared with a Recycled Concrete Aggregate. Journal of Construction Engineering and Management, 147(04).
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Murguia, D, Demian, P and Soetanto, R (2021) Systemic BIM Adoption: A Multilevel Perspective. Journal of Construction Engineering and Management, 147(04).
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Rastegar, H, Arbab Shirani, B, Mirmohammadi, S H and Akhondi Bajegani, E (2021) Stochastic Programming Model for Bidding Price Decision in Construction Projects. Journal of Construction Engineering and Management, 147(04).
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Ugural, M and Giritli, H (2021) Three-Way Interaction Model for Turnover Intention of Construction Professionals: Some Evidence from Turkey. Journal of Construction Engineering and Management, 147(04).
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