Predicting the load-bearing capacity of lipped channel section cold formed steel profiles under combined effects using machine learning

Abstract

The application areas of cold formed steel (CFS) profiles are progressively expanding. Due to the thin cross-sections of these profiles, buckling problem occurs. Experimental and numerical studies to investigate this behavior are very difficult and time consuming. Therefore, this study aims to predict the load bearing capacity of CFS profiles with different slenderness under combined effects using advanced machine learning (ML) models. In this scope, a detailed finite element model including material nonlinearity and geometrical imperfections was created and 2240 finite element analyses (FEAs) were performed using the model validated by experimental studies. A comprehensive data set consisting of 2240 data points and 9 different features was created from the FEAs. Then, five ML models such as support vector regressor (SVR), random forest (RF), multilayer perceptron (MLP), gradient boosting regressor (GBR) and extreme gradient boosting (XGB) were applied to the dataset to predict the load bearing capacity of CFS profiles. In order to obtain effective results during the implementation phase, cross-validation and hyperparameter fine-tuning were performed to obtain optimum parameters. At the conclusion of the study, all five ML models were considered to be highly effective in predicting the load bearing capacity of CFS profiles. MLP model made better predictions compared to the other four models. In the load bearing capacity prediction, the accuracy of the MLP model is ± 4.26 kN, which is less than 4 % of the average load bearing capacity. It was also found that flange length and section thickness have more effect on the load bearing capacity compared to other properties.