A robust method for safety evaluation of steel trusses using Gradient Tree Boosting algorithm

Abstract

In this study, an efficient method is proposed for the safety evaluation of steel trusses using the gradient tree boosting (GTB) algorithm, one of the most powerful techniques in machine learning (ML). Datasets are first generated using the advanced analysis to consider both geometric and material nonlinearities of the structure. Four GTB models are then proposed to predict the ultimate load-carrying capacity and displacement of the structure for safety evaluation of strength and serviceability. Both continuous and discrete input variables are considered. To demonstrate the efficiency of the proposed method, four popular ML methods including support vector machines (SVM), decision tree (DT),..