Use of Building Information Modelling (BIM) for structural optimization

Building Information Modelling (BIM) has evolved from a digital modelling tool into a comprehensive platform capable of supporting advanced structural optimization throughout the design, construction, and operational phases of built assets. This review paper examines the chronological development of BIM-based structural optimization from early parametric design foundations to contemporary integration with artificial intelligence, multi-objective optimization, and digital twin technologies. Early research established BIM as a medium for rule-based geometric modelling and performance assessment, laying the groundwork for optimization-driven workflows. Between 2010 and 2015, the emergence of meta-heuristic algorithms, automated decision systems, and environmental simulation paved the way for the fusion of BIM and computational optimization. The period from 2015 to 2020 saw significant advancements in integrating genetic algorithms, evolutionary form-finding, multi-criteria decision-making, and sustainability analysis directly within BIM environments. Recent developments have further expanded BIM’s capabilities through cloud computing, machine learning, deep learning, and real-time data integration, enabling predictive performance modelling and lifecycle optimization. The review highlights how these developments collectively transform BIM into an intelligent decision-support system for achieving structural efficiency, material reduction, cost optimization, and enhanced sustainability. While the field has advanced substantially, challenges related to interoperability, standardization, and computational complexity persist. Nonetheless, the trajectory of research indicates that BIM-enabled structural optimization will continue to play an increasingly central role in shaping the design and performance of future structural systems.