Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis-Reference-Cited by-同舟云学术

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Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis

Published:2022-01 Issue: Volume:388 Page:114218
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Luo Changqi,Keshtegar Behrooz^ORCID,Zhu Shun Peng,Taylan Osman,Niu Xiao-Peng

Funder

National Natural Science Foundation of China

University of Zabol

Guangzhou Science and Technology Program key projects

King Abdulaziz University

Deanship of Scientific Research, King Saud University

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference72 articles.

1. Support vector regression based metamodeling for seismic reliability analysis of structures;Ghosh;Appl. Math. Model.,2018

2. Filtered importance sampling with support vector margin: A powerful method for structural reliability analysis;Hurtado;Struct. Saf.,2007

3. Directional importance sampling for ill-proportioned spaces;Moarefzadeh;Struct. Saf.,1999

4. First order and second reliability methods;Du;Probab. Eng. Des.,2005

5. Novel probabilistic model for searching most probable point in structural reliability analysis;Zhu;Comput. Methods Appl. Mech. Engrg.,2020

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