Genetic programming-based backbone curve model of reinforced concrete walls

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{MA:2023:engstruct,

• author = "Gao Ma and Yao Wang and Hyeon-Jong Hwang",
• title = "Genetic programming-based backbone curve model of reinforced concrete walls",
• journal = "Engineering Structures",
• volume = "283",
• pages = "115824",
• year = "2023",
• ISSN = "0141-0296",
• DOI = "doi:10.1016/j.engstruct.2023.115824",
• URL = "https://www.sciencedirect.com/science/article/pii/S0141029623002389",
• keywords = "genetic algorithms, genetic programming, Reinforced concrete wall, Machine learning, SHAP, Symbolic regression, Backbone curve",
• abstract = "Backbone curve, as a nonlinear response analysis method, can be used for performance assessment of residual resistance and performance prediction during the preliminary design of structures. In this study, a backbone curve model of reinforced concrete (RC) walls based on Genetic programming-based symbolic regression (GP-SR) was proposed, which can help to quickly evaluate the bearing capacity and seismic performance of RC walls. Unlike the black-box characteristic of traditional machine learning models, the GP-SR method can give explicit computational equations, which are more interpretable and easier to be used by researchers and engineers. Experimental data of 388 existing RC walls were used for feature selection, model training, and comparison with the modeling method of ASCE 41-17 to verify its effectiveness for modeling the backbone curves of RC walls with four failure modes (i.e., flexure, flexure-shear, shear, and shear-sliding). The results showed that the accuracy of the GP-SR model was better than that of the prediction of ASCE 41-17. Overall, the GP-SR model described well the backbone curves of RC walls with various design conditions",

}

Genetic Programming entries for Gao Ma Yao Wang Hyeon-Jong Hwang

Citations