Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Ali Mansouri, Maryam Mansouri, Sujith Mangalathu
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Abstract

SummaryPrecise estimation of the shear strength of concrete‐filled steel tubes (CFSTs) is a crucial requirement for the design of these members. The existing design codes and empirical equations are inconsistent in predicting the shear strength of these members. This paper provides a data‐driven approach for the shear strength estimation of circular CFSTs. For this purpose, the authors evaluated and compared the performance of nine machine learning (ML) methods, namely linear regression, decision tree (DT), k‐nearest neighbors (KNN), support vector regression (SVR), random forest (RF), bagging regression (BR), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) in estimating the shear strength of CFSTs on an experimental database compiled from the results of 230 shear tests on CFSTs in the literature. For each model, hyperparameter tuning was performed by conducting a grid search in combination with k‐fold cross‐validation (CV). Comparing the nine methods in terms of several performance measures showed that the XGBoost model was the most accurate in predicting the shear strength of CFSTs. This model also showed superior accuracy in predicting the shear strength of CFSTs when compared to the formulas provided in design codes and the existing empirical equations. The Shapley Additive exPlanations (SHAP) technique was also used to interpret the results of the XGBoost model. Using SHAP, the features with the greatest impact on the shear strength of CFSTs were found to be the cross‐sectional area of the steel tube, the axial load ratio, and the shear span ratio, in that order.
用于估算圆形混凝土填充钢管抗剪强度的可解释机器学习模型
摘要精确估算混凝土填充钢管(CFST)的抗剪强度是这些构件设计的关键要求。现有的设计规范和经验公式在预测这些构件的抗剪强度方面不一致。本文提供了一种数据驱动的方法来估算圆形 CFST 的抗剪强度。为此,作者评估并比较了九种机器学习 (ML) 方法的性能,即线性回归、决策树 (DT)、k-近邻 (KNN)、支持向量回归 (SVR)、随机森林 (RF)、袋装回归 (BR)、自适应提升 (AdaBuilder) 和自适应增强 (AdaBuilder) 方法、自适应提升(AdaBoost)、梯度提升回归树(GBRT)和极端梯度提升(XGBoost),在根据文献中对 CFST 进行的 230 次剪切试验结果编制的试验数据库上估算 CFST 的剪切强度。每个模型的超参数调整都是通过网格搜索结合 k 倍交叉验证(CV)进行的。从多个性能指标对九种方法进行比较后发现,XGBoost 模型在预测 CFST 的剪切强度方面最为准确。与设计规范中提供的公式和现有的经验公式相比,该模型在预测 CFST 的剪切强度方面也表现出更高的准确性。Shapley Additive exPlanations(SHAP)技术也用于解释 XGBoost 模型的结果。使用 SHAP,发现对 CFST 抗剪强度影响最大的特征依次是钢管横截面积、轴向载荷比和剪跨比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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