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A multi-strategy fusion identification model for failure mode of reinforced concrete column.
Gai, Tongtong; Yu, Dehu; Zeng, Sen; Lin, Jerry Chun-Wei.
Afiliación
  • Gai T; School of Civil Engineering, Qingdao University of Technology, Qingdao, China. Electronic address: gaitongtong@yeah.net.
  • Yu D; School of Civil Engineering, Qingdao University of Technology, Qingdao, China; School of Civil Engineering, Shandong Jianzhu University, Jinan, China. Electronic address: yudehu@sdjzu.edu.cn.
  • Zeng S; School of Civil Engineering, Qingdao University of Technology, Qingdao, China. Electronic address: zengsen@qut.edu.cn.
  • Lin JC; Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and IT Devices, Silesian University of Technology, Poland. Electronic address: jerry.chun-wei.lin@polsl.pl.
ISA Trans ; 148: 374-386, 2024 May.
Article en En | MEDLINE | ID: mdl-38664117
ABSTRACT
Accurate identification of the failure modes of Reinforced Concrete (RC) columns based on the design parameters of the structural members is critical for earthquake-resistant design and safety evaluation of existing structures. Existing identification methods have some problems, such as high cost, incomplete consideration of influencing factors, and low precision or recall in identifying shear or flexural-shear failure. In this paper, the main factors for the failure modes of RC columns are first analyzed and studied. Then, the problem of class imbalance in data samples is investigated. To identify the failure modes of RC columns, oversampling of data (BSB-FMC), model ensembling (RFB-FMC), cost-sensitive learning (CSB-FMC) and a fusion model of three strategies (BSFCB-FMC) are proposed. And finally, the SHapley Additive exPlanations (SHAP) method is used to provide a better interpretation of the designed model. The results show that the developed strategies can improve the accuracy of identifying the failure modes of RC columns compared to the models using a single Artificial Neural Network (ANN), a Support Vector Machine (SVM), a Random Forest (RF), and Adaptive Boosting (AdaBoost). The overall accuracy of the developed BSFCB-FMC model reaches 97%, and the precision and recall for the three failure modes are both above 90%. The designed model provides a solution for fast, accurate and cost-effective identification of the failure modes of RC columns.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ISA Trans Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ISA Trans Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos