Predicting shear strength of CFS channels with slotted webs by machine learning models

Abstract

Staggered rectangular perforations (slots) are provided in the webs of cold-formed steel (CFS) beams and columns to reduce their thermal conductivity and improve the energy efficiency of CFS buildings. The perforations adversely affect the structural characteristics of the members, especially those governed by the web parameters, such as the shear strength and shear buckling. This paper presents machine learning (ML) models to predict the elastic shear buckling load and the ultimate shear strength of CFS channels with slotted webs. Support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN) regressors were trained using a large dataset of numerical results with 3512 samples. An extensive search was conducted to find optimal hyperparameters of the models that result in the best predictions and prevent overfitting. The models’ performances were evaluated employing the ten-fold cross-validation method to make more data available for training and reduce bias and variance. The SVR, DT, and RF models demonstrate good prediction accuracy, which exceeds the accuracy of the existing descriptive equations. Relative feature importance was evaluated using the permutation and SHAP methods for each model. Partial dependence of the buckling load and the shear strength from the channel features was assessed. The predictions of the developed ML models were also compared with the predictions of previously developed artificial neural networks (ANNs). The comparisons demonstrated that ANNs showed higher accuracy in predicting the elastic buckling load, whereas the SVR model provided the most accurate shear strength predictions.