| Autor: |
Esfandiari, Mohammad Javad, Haghighi, Homa, Urgessa, Girum |
| Předmět: |
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| Zdroj: |
Electronic Journal of Structural Engineering; 2023, Vol. 23 Issue 2, p1-8, 8p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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