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Detection and In-Depth Analysis of Causes of Delay in Construction Projects: Synergy between Machine Learning and Expert Knowledge

Author

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  • Marija Z. Ivanović

    (Department of Construction Project Management, Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Đorđe Nedeljković

    (Department of Construction Project Management, Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Zoran Stojadinović

    (Department of Construction Project Management, Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Dejan Marinković

    (Department of Construction Project Management, Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Nenad Ivanišević

    (Department of Construction Project Management, Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Nevena Simić

    (Department of Construction Project Management, Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia)

Abstract

Due to numerous reasons, construction projects often fail to achieve the planned duration. Detecting causes of delays (CoD) is the first step in eliminating or mitigating potential delays in future projects. The goal of research is unbiased CoD detection at a single project level, with the ultimate goal to discover the root causes of delay. The existing approach is based on expert knowledge which is used to create CoD lists for projects in general or groups of similar projects. When applied to a single project, it is burdened with bias, as shown on a case project returning low Spearman Rank correlation values. This research introduces a Delay Root causes Extraction and Analysis Model—DREAM. The proposed model combines expert knowledge, machine learning techniques, and Minutes of Meetings (MoM) as an unutilized extensive source of information. In the first phase, DREAM outputs a CoD list based on occurring frequency in MoM with satisfactory recall values, significantly reducing expert-induced subjectivism. In the second phase, enabled by MoM dates, DREAM adds another dimension to delay analysis—temporal CoD distribution. By analyzing corresponding informative charts, experts can understand the nature of delays and discover the root CoD, allowing intelligent decision making on future projects.

Suggested Citation

  • Marija Z. Ivanović & Đorđe Nedeljković & Zoran Stojadinović & Dejan Marinković & Nenad Ivanišević & Nevena Simić, 2022. "Detection and In-Depth Analysis of Causes of Delay in Construction Projects: Synergy between Machine Learning and Expert Knowledge," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14927-:d:969890
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    References listed on IDEAS

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    Cited by:

    1. Al-Bataineh Fuad & Khatatbeh Ahmed Ali & Alzubi Yazan, 2024. "Unsupervised machine learning for identifying key risk factors contributing to construction delays," Organization, Technology and Management in Construction, Sciendo, vol. 16(1), pages 170-185.

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