Machine Learning-based Optimum Reinforced Concrete Design for Progressive Collapse




Machine Learning, Structural Optimization, Progressive Collapse, Reinforced Concrete, Artificial Intelligence


This paper investigated progressive collapse analysis of three-dimensional (3D) reinforced concrete (RC) frames that are optimized for carrying structural loads by introducing a unique simultaneous multi-column removal load path using Machine Learning. The investigation includes formulating an integrated computational framework that incorporates a self-training machine learning algorithm. This algorithm is used to train the largest machine learning models of 3D RC frames containing more than 600 optimized structures to predict the posterior based on the trained priors. The efficiency of the computational framework was shown by conducting a comprehensive study on the optimization and behavior of structures considering seismic loading, alternative load path due to progressive collapse, and second order (P–delta) effects. The results show that the proposed framework ensures that system solutions meet both structural integrity and constructability requirements of the ACI and the Unified Facilities Criteria.


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How to Cite

Esfandiari, M. ., Haghighi, H. and Urgessa, G. . (2023) “Machine Learning-based Optimum Reinforced Concrete Design for Progressive Collapse”, Electronic Journal of Structural Engineering, 23(2), pp. 1–8. doi: 10.56748/ejse.233642.