Prediction of shear capacity of steel channel sections using machine learning algorithms

Dissanayake, Madhushan; Nguyen, Hoang; Poologanathan, Keerthan; Perampalam, Gatheeshgar; Upasiri, Irindu; Rajanayagam, Heshachanaa; Suntharalingam, Thadshajini

Abstract

This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 238 results of carbon steel LiteSteel sections were gathered to train machine learning models including support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGB). The cross-validation with 10 folds has been conducted in the training process to avoid over-fitting. The optimal hyperparameter combinations for each machine learning model were found during the hyperparameter tuning process and four performance indicators were used to evaluate the performance of the trained models. The comparison results suggest that all four implemented machine learning models reliably predict the shear capacity of both stainless steel lipped channel sections and carbon steel LiteSteel sections while the implemented SVR algorithm is found to be the best performing model. Moreover, it is shown that the implemented machine learning models exceed the prediction accuracy of the available design equations in estimating the shear capacity of steel channel sections.

Citation

Dissanayake, M., Nguyen, H., Poologanathan, K., Perampalam, G., Upasiri, I., Rajanayagam, H., & Suntharalingam, T. (2022). Prediction of shear capacity of steel channel sections using machine learning algorithms. Thin-Walled Structures, 175, Article 109152. https://doi.org/10.1016/j.tws.2022.109152

• Journal Article Type: Article
• Acceptance Date: Mar 5, 2022
• Online Publication Date: Mar 31, 2022
• Publication Date: Jun 30, 2022
• Deposit Date: Jan 22, 2024
• Journal: Thin-Walled Structures
• Print ISSN: 0263-8231
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 175
• Article Number: 109152
• DOI: https://doi.org/10.1016/j.tws.2022.109152
• Public URL: https://uwe-repository.worktribe.com/output/11623950