Stochastic response of steel columns subjected to lateral blast based on modified single degree of freedom (MSDOF) method

Mohammad Momeni , Chiara Bedon , Mohammad Ali Hadianfard , Sina Malekpour
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Abstract

This paper aims to evaluate the stochastic response of steel columns subjected to blast loads using the modified single degree of freedom (MSDOF) method, which assessed towards the conventional single degree of freedom (SDOF) and the experimentally validated Finite Element (FE) methods (LSDYNA). For this purpose, special attention is given to calculating the response of H-shaped steel columns under blast. The damage amount is determined based on the support rotation criterion, which is expressed as a function of their maximum lateral mid-span displacement. To account for uncertainties in input parameters and obtain the failure probability, the Monte Carlo simulation (MCS) method is employed, complemented by the Latin Hypercube Sampling (LHS) method to reduce the number of simulations. A parametric analysis is hence performed to examine the effect of several input parameters (including both deterministic and probabilistic parameters) on the probability of column damage as a function of support rotation. First, the MSDOF method confirms its higher accuracy in estimating the probability of column damage due to blast, compared to the conventional SDOF. The collected results also show that uncertainties of several input parameters have significant effects on the column behavior. In particular, geometric parameters (including cross-sectional characteristics, boundary conditions and column length) have major effect on the corresponding column response, in the same way of input blast load parameters and material properties.
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An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework Stochastic response of steel columns subjected to lateral blast based on modified single degree of freedom (MSDOF) method Table of Contents The MDOF equivalent linear system and its applications in seismic analysis and design of framed structures Automated knowledge graphs for complex systems (AutoGraCS): Applications to management of bridge networks
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