A Bayesian framework for modeling and testing the temperature-dependent residual compressive strength of concrete

Autor/innen

Md Maidul Islam
Ghent University, Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium
https://orcid.org/0009-0001-4900-7524
Andrea Franchini
Ghent University, Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium
https://orcid.org/0000-0001-9732-2484
Balsa Jovanovic
Ghent University, Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium
https://orcid.org/0000-0001-5200-5848
Ruben Van Coile
Ghent University, Technologiepark-Zwijnaarde 60, Ghent 9052, Belgium
https://orcid.org/0000-0002-9715-6786
DOI: https://doi.org/10.17877/tudobooks-11.156

Über dieses Buch

This study presents a novel Bayesian model for the temperature-dependent residual compressive strength of a concrete mix. The model consists of a lognormal distribution for the concrete strength retention factor kfc, i.e., the ratio of the compressive strength after exposure to elevated temperature to the initial compressive strength at room temperature. The Bayesian conceptualization of the model enables straightforward updating of the kfc model as new experimental data becomes available. Furthermore, a framework for pre-posterior analysis is presented to guide decision-making before actual data collection. Specifically, the concept of expected information gain (EIG) is adopted to optimize experimental design. This approach allows the identification of optimal measurement temperatures to reduce uncertainty in estimating kfc. The analysis reveals that measuring at 600–900 °C yields a higher information gain than measuring at any other temperature. Comparative analyses illustrate the effectiveness of the EIG concept in guiding experimental planning.

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

641-649

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

A Bayesian framework for modeling and testing the temperature-dependent residual compressive strength of concrete. (2026). In REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World (pp. 641-649). TUDObooks. https://doi.org/10.17877/tudobooks-11.156