Probabilistic Evaluation of Steel Column Damage under Blast Loading via Simulation Reliability Methods and Gene Expression Programming

Abstract

This paper introduces a probabilistic assessment of steel column damage caused by blast loads, utilizing simulation reliability methods and gene expression programming. The research focuses on an H-section steel column and incorporates uncertainties associated with input loads (axial and blast loads) and geometric factors (i.e., maximum slenderness) under various boundary conditions (pinned and fixed supports). The reliability analysis employs three different methods: the point estimate method (PEM), the Monte Carlo simulation (MCS) method, and the Monte Carlo simulation with Latin hypercube sampling method (MCS-LHS). To perform the reliability analysis, formulas derived from a previous study conducted by the authors using gene expression programming (GEP) were employed. Damage assessment was carried out based on a damage index criterion, considering the post-blast residual axial load-bearing capacity of the steel column. The research results are presented in terms of damage probability, considering the different reliability analysis methods and boundary conditions. The findings demonstrate that the PEM effectively estimates the probabilistic response of the steel column with acceptable accuracy and less effort compared with the MCS and MCS-LHS. Furthermore, the MCS-LHS demonstrates higher accuracy in estimating the probability distribution function by utilizing the Latin hypercube sampling (LHS) method, as compared to the MCS. These findings emphasize the importance of considering uncertainties in calculating the column response under extreme dynamic blast loading.

Keywords

• probabilistic evaluation
• steel column
• Monte Carlo simulation
• Latin hypercube sampling
• point estimate method
• blast load