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采用数值研究和机器学习技术对椭圆形钢管混凝土短柱的抗压性能进行研究

Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques.

作者信息

Mohamed Hazem Samih, Qiong Tang, Isleem Haytham F, Tipu Rupesh Kumar, Shahin Ramy I, Yehia Saad A, Jangir Pradeep, Khishe Mohammad

机构信息

College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, 350002, Fujian, China.

School of Applied Technologies, Qujing Normal University, Qujing, Yunnan, 655011, China.

出版信息

Sci Rep. 2024 Nov 6;14(1):27007. doi: 10.1038/s41598-024-77396-5.

DOI:10.1038/s41598-024-77396-5
PMID:39505978
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11541875/
Abstract

This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns. These techniques include Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), MLP Regressor (MLPR), K-nearest Neighbours Regressor (KNNR), and Naive Bayes Regressor (NBR). ML models accuracy is assessed by comparing their predictions with FE results. Among the models, GBR and XGBR exhibited outstanding results with high test R scores of 0.9888 and 0.9885, respectively. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP indicate that the eccentric loading ratio (e/2a) has the most significant effect on the load-carrying capacity of elliptical CFST short columns, followed by the yield strength of the outer steel tube ([Formula: see text]) and the inner width of the inner steel tube ([Formula: see text]). Additionally, a user interface platform has been developed to streamline the practical application of the proposed ML.

摘要

本文提出了一种非线性有限元模型（FEM），用于预测三种不同配置的椭圆形钢管混凝土（CFST）短柱的承载能力：带夹层混凝土的双钢管（CFDST）、带夹层混凝土且内钢管内有混凝土的双钢管，以及带夹层混凝土的单外钢管。然后，进行了参数和分析研究，以探讨几何和材料参数对椭圆形CFST短柱承载能力的影响。此外，本研究还调查了机器学习（ML）技术在预测椭圆形CFST短柱承载能力方面的有效性。这些技术包括支持向量回归器（SVR）、随机森林回归器（RFR）、梯度提升回归器（GBR）、XGBoost回归器（XGBR）、多层感知器回归器（MLPR）、K近邻回归器（KNNR）和朴素贝叶斯回归器（NBR）。通过将ML模型的预测结果与有限元结果进行比较来评估其准确性。在这些模型中，GBR和XGBR表现出色，测试R分数分别高达0.9888和0.9885。该研究使用SHapley加法解释（SHAP）方法深入了解了各个特征对预测的贡献。SHAP的结果表明，偏心加载比（e/2a）对椭圆形CFST短柱的承载能力影响最大，其次是外钢管的屈服强度（[公式：见原文]）和内钢管的内宽度（[公式：见原文]）。此外，还开发了一个用户界面平台，以简化所提出的ML的实际应用。

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