Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings

Abstract

The rapid growth of using the short links in steel buildings due to their high shear strength and rotational capacity attracts the attention of structural engineers to investigate the performance of short links. However, insignificant attention has been oriented to efficiently developing a comprehensive model to forecast the shear strength of short links, which is expected to enhance the steel structures’ constructability. As machine learning algorithms was successfully used in various fields of structural engineering, the current study fills the gap in estimating the shear strength of short links using sophisticated machine learning algorithms. The deriving factors such as web and flange slenderness ratios, the flange-to-web area ratio, the forces in web and flange, and the link length ratio were investigated in this study, which is imperative to formulate an integrated prediction model. Consequently, the aim of this study utilizes advanced machine learning (ML) models (i.e., Extreme Gradient Boosting (XGBOOST), Light Gradient Boosting Machine (LightGBM), and Artificial Neural Network (ANN) to produce accurate forecasting for the shear strength. In this study, publicly available datasets were used for the training, testing, and validation. Different evaluation metrics were employed to evaluate the prediction’s performance of the used models, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). The prediction result displays that the XGBOOST and LightGBM provided better, and more reliable results compared to ANN and the AISC code. The XGBOOST and LightGBM models yielded higher values of R2, lower (RMSE), (MAE), and (MAPE) values and have shown to perform more accurate. Therefore, the overall outcomes showed that the LightGBM outperformed the XGBOOST model. Moreover, the overstrength ratio predicted by the LightGBM showed an excellent performance compared to the Gene Expression and Finite Element-based models. The developed models are vital for practitioners to predict the shear strength accurately, which pave the road towards wider application for automation in the steel buildings.