Enhanced Bayesian Network Approach to Delay Risk Analysis in Mass Housing Construction

Document Type : Research Article

Authors

1 Associate Professor, School of Civil Engineering, Iran University of Science and Technology

2 PhD student, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

3 Master's student, School of Civil Engineering, Iran University of Science and Technology

Abstract

Housing mass construction projects are vital for urban development but often face delays that lead to increased costs and missed opportunities. This study addresses delay risks using an integrated approach combining Bayesian Belief Networks (BBNs), Interpretive Structural Modeling (ISM), and the DEMATEL method. BBNs identify and model risk relationships, while the Ranked Node Method optimizes parametric analysis by reducing time and effort. ISM establishes risk hierarchies, and DEMATEL analyzes cause-and-effect relationships. Applied to a Parand mass housing project, the methodology revealed a 68% probability of significant delays, with financial and economic risks having a 60% likelihood. Payment delays by the owner showed a 65% probability, while factors like inexperienced consultants, unsuitable contractors, and mismanagement each had over 50% severity probabilities. Contractor- and consultant-related issues also contributed, each with average probabilities exceeding 40%. These findings highlight the importance of addressing financial inefficiencies and enhancing project management practices. The proposed method enhances delay risk modeling precision while minimizing required information, offering a practical and efficient solution for managing delays in mass housing projects.

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Main Subjects

References

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Amirkabir Journal of Civil Engineering
Volume 57, Issue 3
2025
Pages 529-548

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