1. 新疆大学数学与系统科学学院
2. 新疆大学建筑工程学院
3. 新疆大学新疆建筑结构与抗震重点实验室
纸质出版:2025
移动端阅览
[1]常旭,马财龙,肖旭峰,等.RC柱受剪承载力神经网络预测模型及其可解释性[J].新疆大学学报(自然科学版中英文),2025,42(01):114-128.
[1]常旭,马财龙,肖旭峰,等.RC柱受剪承载力神经网络预测模型及其可解释性[J].新疆大学学报(自然科学版中英文),2025,42(01):114-128. DOI: 10.13568/j.cnki.651094.651316.2023.11.04.0001.
DOI:10.13568/j.cnki.651094.651316.2023.11.04.0001.
针对钢筋混凝土(Reinforced Concrete
RC)柱的受剪承载力传统预测模型精度提升及已有试验数据的挖掘利用,基于机器学习方法和可解释性SHAP方法,建立人工神经网络模型对RC柱的受剪承载力进行预测.基于受剪理论,确定了纵筋配筋率ρl、纵筋屈服强度fyl、面积配箍率ρsv等9个输入特征并对其相关性进行验证.利用收集整理的441组RC柱受剪试验数据,将所建神经网络模型与5种机器学习模型、5种传统半经验半理论公式进行比较,发现所建神经网络模型具有更好的泛化性和鲁棒性,其预测结果更加精准(在训练集和测试集上的R2分别达到0.99和0.92).然后,采用SHAP方法对神经网络模型进行全局与局部可解释性分析.结果表明:截面宽度b、轴压力N、剪跨比λ、有效截面高度h0、轴心抗拉强度ft等特征对RC柱的受剪性能具有显著影响,SHAP方法对未知样例的预测结果同样给出了具有一定可信度的分析结果.综上,此数据与机理协同驱动的神经网络模型和SHAP可解释性方法可用于RC柱受剪承载力及类似问题的预测.
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 5traditional 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.
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