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Machine Learning-based Optimum Reinforced Concrete Design for Progressive Collapse

Author(s):
ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Electronic Journal of Structural Engineering, , n. 2, v. 23
Page(s): 1-8
DOI: 10.56748/ejse.233642
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 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.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.56748/ejse.233642.
  • About this
    data sheet
  • Reference-ID
    10778668
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
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