A robust method for safety evaluation of steel trusses using Gradient Tree Boosting algorithm. (September 2020)

• Record Type: Journal Article
• Authors: Truong, Viet-Hung; Vu, Quang-Viet; Thai, Huu-Tai; Ha, Manh-Hung
• Is Part Of: Advances in engineering software. Volume 147(2020)
• Publication Date: 2020-09
• Subjects: Gradient Tree Boosting algorithm -- Machine learning -- Deep learning -- Advanced analysis -- Nonlinear inelastic analysis -- Steel truss; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2020.102825
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A method for safety evaluation of steel trusses using Gradient Tree Boosting algorithm has been developed. Advanced analysis has been used to capture the nonlinear behaviors of trusses. Support vector machines, decision tree, random forest, and deep learning have been used for comparison. GTB method has the best performance in most considered case studies. 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), random forest (RF), and deep learning (DL) are refereed in a comparison study. Three numerical examples of steel truss structures including a planar truss, a spatial truss, and a case study of planar truss bridge are considered in the comparative study. The numerical results show that the developed GTB models provide high accurate (more than 90%) regardless of the number of training data and design variable types and have the best performance in most considered cases.