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Ballesteros-Pérez, P, Sanz-Ablanedo, E, Soetanto, R, González-Cruz, M C, Larsen, G D and Cerezo-Narváez, A (2020) Duration and Cost Variability of Construction Activities: An Empirical Study. Journal of Construction Engineering and Management, 146(01).
Davila Delgado, J M, Oyedele, L, Bilal, M, Ajayi, A, Akanbi, L and Akinade, O (2020) Big Data Analytics System for Costing Power Transmission Projects. Journal of Construction Engineering and Management, 146(01).
Deng, H, Hong, H, Luo, D, Deng, Y and Su, C (2020) Automatic Indoor Construction Process Monitoring for Tiles Based on BIM and Computer Vision. Journal of Construction Engineering and Management, 146(01).
El-adaway, I H, Ali, G G, Abotaleb, I S and Barber, H M (2020) Studying the Relationship between Stock Prices of Publicly Traded US Construction Companies and Gross Domestic Product: Preliminary Step toward Construction–Economy Nexus. Journal of Construction Engineering and Management, 146(01).
Elmousalami, H H (2020) Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review. Journal of Construction Engineering and Management, 146(01).
Gondia, A, Siam, A, El-Dakhakhni, W and Nassar, A H (2020) Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management, 146(01).
- Type: Journal Article
- Keywords: Classification; Complex systems; Confusion matrices; Construction projects; Cross validation; Delay risk analysis; Machine learning; Predictive data analytics; Risk identification; Time delay;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001736
- Abstract:
Projects delays are among the most pressing challenges faced by the construction sector attributed to the sector’s complexity and its inherent delay risk sources’ interdependence. Machine learning offers an ideal set of techniques capable of tackling such complex systems; however, adopting such techniques within the construction sector remains at an early stage. The goal of this study was to identify and develop machine learning models in order to facilitate accurate project delay risk analysis and prediction using objective data sources. As such, relevant delay risk sources and factors were first identified, and a multivariate data set of previous projects’ time performance and delay-inducing risk sources was then compiled. Subsequently, the complexity and interdependence of the system was uncovered through an exploratory data analysis. Accordingly, two suitable machine learning models, utilizing decision tree and naïve Bayesian classification algorithms, were identified and trained using the data set for predicting project delay extents. Finally, the predictive performances of both models were evaluated through cross validation tests, and the models were further compared using machine-learning-relevant performance indices. The evaluation results indicated that the naïve Bayesian model provides a better predictive performance for the data set examined. Ultimately, the work presented herein harnesses the power of machine learning to facilitate evidence-based decision making, while inherent risk factors are active, interdependent, and dynamic, thus empowering proactive project risk management strategies.
Halabya, A and El-Rayes, K (2020) Optimizing the Planning of Pedestrian Facilities Upgrade Projects to Maximize Accessibility for People with Disabilities. Journal of Construction Engineering and Management, 146(01).
He, C, McCabe, B, Jia, G and Sun, J (2020) Effects of Safety Climate and Safety Behavior on Safety Outcomes between Supervisors and Construction Workers. Journal of Construction Engineering and Management, 146(01).
Li, Y, Cao, L, Han, Y and Wei, J (2020) Development of a Conceptual Benchmarking Framework for Healthcare Facilities Management: Case Study of Shanghai Municipal Hospitals. Journal of Construction Engineering and Management, 146(01).
Maqsoom, A, Wazir, S J, Choudhry, R M, Thaheem, M J and Zahoor, H (2020) Influence of Perceived Fairness on Contractors’ Potential to Dispute: Moderating Effect of Engineering Ethics. Journal of Construction Engineering and Management, 146(01).
Newaz, M T, Davis, P, Jefferies, M and Pillay, M (2020) Examining the Psychological Contract as Mediator between the Safety Behavior of Supervisors and Workers on Construction Sites. Journal of Construction Engineering and Management, 146(01).
Pereira, E, Ali, M, Wu, L and Abourizk, S (2020) Distributed Simulation–Based Analytics Approach for Enhancing Safety Management Systems in Industrial Construction. Journal of Construction Engineering and Management, 146(01).
Signor, R, Love, P E D, Belarmino, A T N and Alfred Olatunji, O (2020) Detection of Collusive Tenders in Infrastructure Projects: Learning from Operation Car Wash. Journal of Construction Engineering and Management, 146(01).
Tawalare, A, Laishram, B and Thottathil, F (2020) Relational Partnership in Public Construction Organizations: Front-Line Employee Perspective. Journal of Construction Engineering and Management, 146(01).
Yuan, H and Yang, Y (2020) BIM Adoption under Government Subsidy: Technology Diffusion Perspective. Journal of Construction Engineering and Management, 146(01).