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Explainable machine learning-based prediction of pullout-based bond strength between steel rebar and steel fiber reinforced concrete

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成果类型:
期刊论文
作者:
Zhang, Xuhui;Chen, Junzhuo;Yuan, Ping;Wang, Lei
通讯作者:
Wang, L
作者机构:
[Zhang, Xuhui; Chen, Junzhuo] Xiangtan Univ, Coll Civil Engn, Xiangtan 411105, Hunan, Peoples R China.
[Yuan, Ping; Wang, Lei] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Hunan, Peoples R China.
通讯机构:
[Wang, L ] C
Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Hunan, Peoples R China.
语种:
英文
关键词:
Bond strength;Steel fiber reinforce concrete;Machine learning;Genetic algorithm;Gaussian noise;SHAP
期刊:
Construction and Building Materials
ISSN:
0950-0618
年:
2025
卷:
493
页码:
143134
基金类别:
CRediT authorship contribution statement Xuhui Zhang: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, acquisition. Ping Yuan: Writing – review & editing, Writing – original draft, Software, Investigation, Formal analysis. Lei Wang: Writing – review & editing, Supervision, acquisition. Junzhuo Chen: Writing – original draft, Validation, Software, Methodology, Investigation, Data curation.
机构署名:
本校为通讯机构
院系归属:
土木工程学院
摘要:
The bond behavior between steel fiber reinforced concrete (SFRC) and steel rebars significantly influences both serviceability and seismic performance of structures. Accurate prediction of bond strength remains challenging due to the various influencing factors and limitations inherent in experimental data acquisition. To address this challenge, an explainable machine learning-based approach is proposed in the present study to predict the bond strength. A comprehensive dataset is developed incorporating seven key parameters: fiber slenderness r...

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