Abstract
The ground external columns of buildings are vulnerable to the extreme actions such as a vehicle collision. This event is a common scenario of buildings' damages. In this study, a nonlinear model of 2-story steel moment-resisting frame (SMRF) is made in OpenSees software. This paper aims investigating the reliability analysis of aforementioned structure under heavy vehicle impact loadings by Monte Carlo Simulation (MCS) in MATLAB software. To reduce computational costs, meta-model techniques such as Kriging, Polynomial Response Surface Methodology (PRSM) and Artificial Neural Network (ANN) are applied and their efficiency is assessed. At first, the random variables are defined. Then, the sensitivity analyses are performed using MCS and Sobol's methods. Finally, the failure probabilities and reliability indices of studied frame are presented under impact loadings with various collision velocities at different performance levels and thus, the behavior of selected SMRF is compared by using fragility curves. The results showed that the random variables such as mass and velocity of vehicle and yield strength of used materials were the most effective parameters in the failure probability computation. Among the meta-models, Kriging can estimate the failure probability with the least error, sample number with minimum computer processing time, in comparison with MCS.
Highlights
I n the last decade, due to the increasing terrorist threats in different parts of the world, special attention has been paid to the design and analysis of resistant structures to abnormal loadings
As a significance and novelty, a probabilistic versatile framework is proposed based on the reliability analyses under heavy vehicle impact loadings with different collision velocities using Monte Carlo Simulation (MCS), meta-models including Kriging, Polynomial Response Surface Methodology (PRSM) and Artificial Neural Network (ANN) are considered for reducing computational costs, while having high accuracy and least error rate
The performance of three meta-models such as Kriging, PRSM and ANN is investigated in predicting the beam rotation
Summary
I n the last decade, due to the increasing terrorist threats in different parts of the world, special attention has been paid to the design and analysis of resistant structures to abnormal loadings. As a significance and novelty, a probabilistic versatile framework is proposed based on the reliability analyses under heavy vehicle impact loadings with different collision velocities using MCS, meta-models including Kriging, Polynomial Response Surface Methodology (PRSM) and Artificial Neural Network (ANN) are considered for reducing computational costs, while having high accuracy and least error rate. It may provide a throughout framework based on probabilities under vehicle impact loadings, which has gained great interest in practical researches in this field. The comparison of different sensitivity tests results will show the important random variables and at last, the best meta-model will be specified by using reliability and fragility analyses which is useful and practical for future researchers in this aspect
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