Autor/innen
Pei-Pei Li, Department of Civil Engineering, Beijing University of Technology, Beijing 100124, China; Marcos A. Valdebenito, Chair for Reliability Engineering, TU Dortmund University, Leonhard-Euler-Str. 5, Dortmund 44227, Germany; Chao Dang, Chair for Reliability Engineering, TU Dortmund University, Leonhard-Euler-Str. 5, Dortmund 44227, Germany; Michael Beer, Institute for Risk and Reliability, Leibniz Universität Hannover, Hannover, Germany / Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK / Shanghai Institute of Disaster Prevention and Relief, Tongji University, Shanghai, China; Matthias Faes, Chair for Reliability Engineering, TU Dortmund University, Leonhard-Euler-Straße 5, Dortmund 44227, Germany
Über dieses Buch
In engineering applications, aleatory and epistemic uncertainties commonly coexist and interact, making their proper characterization essential for reliability analysis and informed decision-making under uncertainty. This challenge becomes particularly evident when failure probabilities of engineering structures are evaluated using incomplete, insufficient, imperfect, or imprecise information. Under such circumstances, failure probability can no longer be expressed as a single deterministic value; instead, set-theoretic or Bayesian representations are required to explicitly account for epistemic uncertainty. Although the theoretical foundations for handling these uncertainties are well established, a noticeable gap persists between academic developments and practical engineering implementation. In practice, aleatory and epistemic uncertainties, despite being conceptually distinct, are still frequently mixed, either implicitly or even explicitly in engineering analyses. To bridge this gap, this paper offers a pragmatic guide for selecting suitable uncertainty modeling frameworks, such as probability boxes, fuzzy probability models, or hierarchical probabilistic approaches, when dealing with problems influenced by both uncertainty types. By assessing the nature and quality of available information as well as the objectives of the analysis, we provide clear recommendations for appropriate modeling choices and present a comprehensive framework for evaluating failure probability. Furthermore, this work highlights the critical role of sensitivity analysis in identifying influential parameters, enabling targeted data acquisition efforts that reduce epistemic uncertainty and ultimately enhance the credibility of reliability assessments.
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)
Zitationsvorschlag
A practical framework for failure probability assessment under aleatory and epistemic uncertainty. (2026). In
REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World (pp. 179-192). TUDObooks.
https://doi.org/10.17877/tudobooks-11.175
