Parameterized fragility analysis of steel frame structure subjected to blast loads using Bayesian logistic regression method

Abstract

Civil structures might be subjected to blast loads during their lifetime. Since explosion is a low probability high consequence event, the damage assessment of structural system against blast loads needs a probability evaluation approach. This study presented a reliability analysis of steel frame structure against blast loads using Bayesian logistic regression method. Fragility functions parameterized on material properties and blast wave characteristics are developed and further utilized to assess the collapse risk of the structure. The imbalanced data set used in developing logistic regression model and the prediction uncertainty due to the limited data size are particularly stressed. The imbalanced data set is treated by generating synthetic data points for samples that are most likely to be misclassified. The prediction uncertainty is considered by constructing confidence intervals based on the posterior samples generated from Markov Chain Monte Carlo simulations. The developed risk assessment approach is applied to a 10-story steel frame as a numerical example. The probability of structural collapse is obtained and compared with the results from subset simulation method in conjunction with finite element analysis. Results show that the developed parameterized fragility function is accurate in predicting the probability of structural collapse against blast loads compared with the FE-based reliability method. The closed-form fragility function can be implemented to study the effect of parameter variations on the safety of the structure without additional FE simulations.