An efficient reliability analysis method under hybrid uncertainty for high-dimensional problems

Autor/innen

Zi-Xin Zhang
Department of Civil Engineering, Beijing University of Technology, Beijing 100124, China
https://orcid.org/0009-0003-1851-0172
Pei-Pei Li
Department of Civil Engineering, Beijing University of Technology, Beijing 100124, China
https://orcid.org/0000-0002-9766-297X
Yan-Gang Zhao
National Key Laboratory of Bridge Safety and Resilience, Beijing University of Technology, Beijing 100124, China
https://orcid.org/0000-0002-7975-4918
DOI: https://doi.org/10.17877/tudobooks-11.170

Über dieses Buch

In engineering reliability assessment, simultaneously considering hybrid uncertainty is crucial for ensuring structural safety and cost efficiency. Accurately characterizing the distribution of the conditional failure probability (CFP) is therefore of great practical importance. However, when the epistemic uncertainty involves high dimensional parameters, obtaining this distribution remains a significant challenge. To address this issue, this study proposes an efficient and accurate approach that integrates simulation-based post-processing with Bayesian inference to construct the CFP distribution. Parameter samples are first generated using a good lattice point method with partially stratified sampling, followed by reliability analysis to obtain CFP realizations. Beta mixture distribution model is then employed to directly fit the CFP, with Bayesian estimation used to determine the shape parameters of the mixture. The efficiency and accuracy of the proposed approach are demonstrated through two numerical examples, covering bimodal distributions and finite-element frame structures.

REC 2026 Reliability Computations in a Data and Model-Driven World

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REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World

Seiten

140-152

Veröffentlicht

21.08.2026
Copyright (c) 2026 Matthias Faes, Michael Beer, Julian Behrend, Marco Broccardo, Guohai Chen, Hanshu Chen, Jieyu Chen, Fang Cong, Henrik Ebel, Peter Eberhard, Andrea Franchini, Zhuojia Fu, Yanzhao Gu, Michael Hanss, Hui Huang, Md Maidul Islam, Jafar Jafari-Asl, Balsa Jovanovic, Tom Könecke, Olga Kosheleva, Vladik Kreinovich, Hao-Ming Li, Pei-Pei Li, Qinghe Li, Ruikun Li, Stefano Marelli, Rafi L. Muhanna, Robert L. Mullen, Chiara Nardin, Lukáš Novák, Shenghao Piao, Mario Rosenfelder, Styfen Schär, Jan Schneider, Wei Shi, Panagiotis Spyridis, Bruno Sudret, Ruben Van Coile, Bo-Yu Wang, Dixiong Yang, Xuan-Yi Zhang, Zi-Xin Zhang, Bo-Qi Zhao, Yan-Gang Zhao, Zhibao Zheng, Baihong Zhong, Zhaowei Zhou, Marcos A. Valdebenito, Chao Dang, Lan-Xin Li, Marius Bittner, Parth Tambat, Marco Behrendt, Zhao Liu, Ping Zhu, Zlatan Dimitrov, Petar O. Hristov, Meng-Ze Lyu, Yang-Yi Liu, Jian-Bing Chen, Qitian Lu, Miroslav Vořechovský, Ekaterina Auer, Lorenz Gillner, Julien Alexandre dit Sandretto, Binod K. Yadav, Ghifari Adam Faza, Hans Hallez, David Moens, Reza Naeimaei, Steffen Schön, Elisabeth Ötsch, Hans Neuner, Zhanhua Liang, Jingwen Song, Michael Desch, Mehdi Modares, Valentin Nikolov, Paulo Lucas Figueiredo, José Torres Farinha, Hugo Raposo, António J. Marques Cardoso, Alice do Carmo Duarte Rodrigues, Paula Gonçalves, Peihan Chen, Xinyu Zhu, Pengfei Wei, Kaiwen Li, Mansoureh Shahabi, Y. F. Cui, Johannes O. Royset, Salvatore Russotto, Mario Di Paola, Antonina Pirrotta, Til Lux, Johannes Sundheim, Tania Feiri, Udo Wiens, Marcus Ricker, F. Niklas Schietzold, Felix Harazin, Yoshi Diepelt, Wolfgang Graf, Michael Kaliske, Rongyao Song, Changcong Zhou, Xuanyi Zhao, Weiping Zhang, Juntao Hu, Yu Chen, Edoardo Patelli, Tingting Sun, Jianbing Chen, Bouwe Verkens, L. Bogaerts, Patrick Van Rymenant, Maximilian Schweizer, Marc Fina, Werner Wagner, Steffen Freitag, George Stefanou, Dimitros Savvas, Panagiotis Gavalas, Luigi Schiano, Razhan S. Ahmed, Oleksandr Al-Shboul, David Ringeloth, Vladislav Gudžulić, Stefanie Schoen, Jelena Bijeljić, Ernst Niederleithinger, Iurie Curoşu, Gerrit E. Neu, Jia-Hui Fu, Yi Luo, De-Cheng Feng, Xiao-Qiu Ai, Luyi Li, Hao Wang, Tairan Wang, Sifeng Bi, Emily Zeller (Kapitelautor/in)

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Creative Commons License

Dieses Werk steht unter der Lizenz Creative Commons Namensnennung 4.0 International.

Zitationsvorschlag

An efficient reliability analysis method under hybrid uncertainty for high-dimensional problems. (2026). In REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World (pp. 140-152). TUDObooks. https://doi.org/10.17877/tudobooks-11.170