Development of Hybrid Machine Learning Models for Predicting Permanent Transverse Displacement of Circular Hollow Section Steel Members under Impact Loads

Abstract

The impact effect is a crucial issue in civil engineering and has received considerable attention for decades. For the first time, this study develops hybrid machine learning models that integrate the novel Extreme Gradient Boosting (XGB) model with Particle Swam Optimization (PSO), Grey Wolf Optimizer (GWO), Moth Flame Optimizer (MFO), Jaya (JA), and Multi-Verse Optimizer (MVO) algorithms for predicting the permanent transverse displacement of circular hollow section (CHS) steel members under impact loads. The hybrid machine learning models are developed using data collected from 357 impact tests of CHS steel members. The efficacy of hybrid machine learning models is evaluated using three performance metrics. The results show that the GWO-XGB model achieves high accuracy and outperforms the other models. The values of R2, RMSE, and MAE obtained from the GWO-XGB model for the test set are 0.981, 2.835 mm, and 1.906 mm, respectively. The SHAP-based model explanation shows that the initial impact velocity of the indenter, the impact mass, and the ratio of impact position to the member length are the most sensitive parameters, followed by the yield strength of the steel member and the member length; meanwhile, member diameter and member thickness are the parameters least sensitive to the permanent transverse displacement of CHS steel members. Finally, this study develops a web application tool to help rapidly estimate the permanent transverse displacement of CHS steel members under impact loads.

Keywords

• circular hollow section steel members
• Extreme Gradient Boosting
• Grey Wolf Optimizer
• hybrid machine learning models
• impact effect
• transverse displacement