Machine learning-based optimum reinforced concrete design for progressive collapse

Abstract

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 using Machine Learning. The various load paths resulting from multiple-column removal are incorporated in the optimization automatically. The investigation includes formulating an integrated computational framework that incorporates a self-training machine learning algorithm. The efficiency of the algorithm is tested by using several hundreds of optimized structures. 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. Copyright: