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

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), Monte Carlo simulation method (MCS), and Monte Carlo simulation method with Latin Hypercube sampling (MCS-LHS). To establish the reliability analysis, formulas derived from a previous study conducted by the authors using gene expression programming are employed. Damage assessment is determined based on a damage index criterion, considering the post-blast residual axial load-bearing capacity of the steel column. The research presents the results in terms of damage probability, considering the different reliability analysis methods and boundary conditions. The findings demonstrate that the point estimate method effectively estimates the probabilistic response of the steel column with acceptable accuracy and less effort compared to the MCS and MCS-LHS methods. Furthermore, the MCS-LHS method demonstrates higher accuracy in estimating the probability distribution function utilizing the Latin Hypercube sampling method as compared to the MCS method. These findings emphasize the importance of considering uncertainties in calculating the column response under extreme dynamic blast loading.