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Neural Network Prediction Model for Shear Bearing Capacity of RC Columns and Its Interpretability

Xu CHANG1, Cailong MA1,2,3(), Xufeng XIAO1, Chengfeng LU2
School of Mathematics and System Sciences, Xinjiang University, Urumqi Xinjiang 830017, China
School of Civil Engineering and Architecture, Xinjiang University, Urumqi Xinjiang 830017, China
Xinjiang Key Laboratory of Building Structure and Seismic Resistance, Xinjiang University, Urumqi Xinjiang 830017, China
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Abstract

The accuracy of traditional prediction models for the shear bearing capacity of reinforced concrete(RC) columns is improved and existing experimental data is mined and utilized. Based on machine learning methods and the interpretable SHAP method, an artificial neural network model is established to predict the shear bearing capacity of reinforced concrete columns. Firstly, based on shear theory, 9 input features including longitudinal reinforcement ratio ρl, longitudinal reinforcement yield strength fyl, and area shear reinforcement ratio ρsv are determined and their correlations are verified. With 441 sets of collected and organized experimental data on shear tests of reinforced concrete columns, the neural network model is compared with 5 machine learning models and 5 traditional semi-empirical and semi-theoretical formulas. The prediction results show that the neural network model established in this paper has better generalization and robustness, and its prediction results are more accurate(with R2 reaching 0.99 and 0.92 on the training set and test set, respectively). In addition, the SHAP method is used to analyze the interpretability of the neural network model. The analysis results show that features such as section width b, axial compression force N, shear span ratio λ, effective section height h0, and axial tensile strength ft have significant influences on the shear performance of reinforced concrete columns. Moreover, the SHAP method also provides reasonably reliable analysis results for unknown samples. The study demonstrates that the data-driven and mechanism-driven neural network model and the SHAP interpretability method proposed in this paper can be applied to similar prediction problems of shear bearing capacity of reinforced concrete columns.

Keywords

structural engineeringreinforced concrete columnsshear bearing capacityneural networkinterpretabilitySHAP
CLC number: TP181;TU375 Document code: A Article ID: 2096-7675(2025)01-0114-015

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Journal of Xinjiang University(Natural Science Edition in Chinese and English)
Volume 42 Issue 1,
January 2025
Pages 114-128
DOI: 10.13568/j.cnki.651094.651316.2023.11.04.0001
Cite this article:
CHANG X, MA C, XIAO X, et al. Neural Network Prediction Model for Shear Bearing Capacity of RC Columns and Its Interpretability. Journal of Xinjiang University(Natural Science Edition in Chinese and English), 2025, 42(1): 114-128. https://doi.org/10.13568/j.cnki.651094.651316.2023.11.04.0001
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