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<p>Variation orders (VOs) are largely accountable for construction project dispute, schedule extension, and cost overrun, mostly in the Jordanian construction climate. This work introduces a combined predictive framework that combines Building Information Modeling (BIM), machine learning (ML), and metaheuristic optimization to anticipate and mitigate VO risks. Descriptive statistics investigation of nearby project data sets identified that VOs had caused an average cost increment of 11.4% and schedule extension of 14.1%. Gradient Boosting and LightGBM stood up with higher precision than other tested predictive versions, most effectively with optimization using Particle Swarm Optimization (PSO) and Black Widow Optimization (BWO). VO discriminators like BIM clashes, variation requests during design, and contractor experience were determined with feature importance investigation. The work stipulates a pragmatic, scalable framework for early VO risk detection enhancement and proactive choices. Methodological as well as pragmatic applications are found for its usability with contractors, consultants, and policymakers to mitigate VO effects and improve construction project execution returns. The framework introduced is favorable to digital construction evolution in construction risk management. Moreover, the framework also offers a transferable developmental profile for construction risk management in developing nations with comparable VO issues.</p>

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Integrating machine learning and metaheuristic optimization into BIM frameworks for mitigating variation orders: evidence from the Jordanian construction sector

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Aya Bassam,
  • Mohammed A. KA. Al-Btoush

摘要

Variation orders (VOs) are largely accountable for construction project dispute, schedule extension, and cost overrun, mostly in the Jordanian construction climate. This work introduces a combined predictive framework that combines Building Information Modeling (BIM), machine learning (ML), and metaheuristic optimization to anticipate and mitigate VO risks. Descriptive statistics investigation of nearby project data sets identified that VOs had caused an average cost increment of 11.4% and schedule extension of 14.1%. Gradient Boosting and LightGBM stood up with higher precision than other tested predictive versions, most effectively with optimization using Particle Swarm Optimization (PSO) and Black Widow Optimization (BWO). VO discriminators like BIM clashes, variation requests during design, and contractor experience were determined with feature importance investigation. The work stipulates a pragmatic, scalable framework for early VO risk detection enhancement and proactive choices. Methodological as well as pragmatic applications are found for its usability with contractors, consultants, and policymakers to mitigate VO effects and improve construction project execution returns. The framework introduced is favorable to digital construction evolution in construction risk management. Moreover, the framework also offers a transferable developmental profile for construction risk management in developing nations with comparable VO issues.