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<p>The construction projects are characterized by intricate relations of cost, schedule, resources, and structural aspects that frequently give rise to uncertainty and project risk. In order to overcome these difficulties, this research suggests a Hybrid Construction Intelligence Framework Network (HCIF-Net) of intelligent construction risk management through combining the BIM data with predictive analytics and hybrid optimization methods. Several heterogeneous datasets, such as records of construction project management, project reports, and IFC-based BIM structural data, are incorporated to form a unified dataset of 1300 project instances. SMOTE balancing is used after the preprocessing and feature engineering to deal with class imbalance in risk categories. The HCIF-Net framework integrates TabNet to learn tables, Bayesian Network to model probabilistic risks dependencies, Temporal Attention LSTM to predict sequential progress, and Monte Carlo simulation to analyse uncertainty. A hybrid MILP-NSGA-II optimization model is employed to reduce the project cost, the time taken to complete, and the total risk to aid decision-making. The results of the experiment show a high level of predictive performance with an accuracy of 98.88% and close-to-perfect ROC-AUC metrics, and BIM-based visualization dashboards offer intuitive insights to monitor and manage construction risk efficiently.</p>

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Optimizing construction risk management through BIM integration and strategic planning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Ruixin Li,
  • Reem A. Almenweer,
  • Haytham F. Isleem,
  • Ahmed Sayed M. Metwally

摘要

The construction projects are characterized by intricate relations of cost, schedule, resources, and structural aspects that frequently give rise to uncertainty and project risk. In order to overcome these difficulties, this research suggests a Hybrid Construction Intelligence Framework Network (HCIF-Net) of intelligent construction risk management through combining the BIM data with predictive analytics and hybrid optimization methods. Several heterogeneous datasets, such as records of construction project management, project reports, and IFC-based BIM structural data, are incorporated to form a unified dataset of 1300 project instances. SMOTE balancing is used after the preprocessing and feature engineering to deal with class imbalance in risk categories. The HCIF-Net framework integrates TabNet to learn tables, Bayesian Network to model probabilistic risks dependencies, Temporal Attention LSTM to predict sequential progress, and Monte Carlo simulation to analyse uncertainty. A hybrid MILP-NSGA-II optimization model is employed to reduce the project cost, the time taken to complete, and the total risk to aid decision-making. The results of the experiment show a high level of predictive performance with an accuracy of 98.88% and close-to-perfect ROC-AUC metrics, and BIM-based visualization dashboards offer intuitive insights to monitor and manage construction risk efficiently.