Numerical analysis and prediction of lateral-torsional buckling resistance of cellular steel beams using FEM and least square support vector machine optimized by metaheuristic algorithms

Abstract

This study presents an advanced framework for modeling the lateral-torsional buckling behavior of cellular steel beams, which combines hybrid intelligent models with numerical simulation. The proposed hybrid intelligent models employ a large dataset-based finite element method (FEM) for training and validation the framework, as well as metaheuristic algorithms for optimal auto-hyper-parameters selection. A total of 1535 numerical models are examined in order to evaluate the lateral-torsional buckling behavior. Following that, the least square support vector machine (LSSVM) optimized using four metaheuristic algorithms (ME): particle swarm optimization (PSO), ant lion optimization (ALO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO) algorithms, is utilized to estimate accurately the lateral-torsional buckling resistance. According to the findings of a comprehensive performance evaluation utilizing statistical and graphical comparing criteria, the suggested LSSVM-ME predicts the lateral-torsional buckling behavior with excellent accuracy. LSSVM-HHO, in particular, outperforms the other hybrid intelligence models, with an RMSE of 41.72 kN.m and an NSE of 0.99. Overall, the results indicate that the proposed framework has a great potential for use as a practical tool for estimating the lateral-torsional buckling behavior of cellular steel beams.

Keywords

• Lateral–torsional buckling resistance
• Cellular steel beams
• Finite element method
• Machine learning
• Least-square support vector machine
• Harris hawks optimization