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Earthquakes and Structures
  Volume 14, Number 5, May 2018, pages 425-436
DOI: http://dx.doi.org/10.12989/eas.2018.14.5.425
 


Seismic reliability assessment of base-isolated structures using artificial neural network: operation failure of sensitive equipment
Hesamaldin Moeindarbari and Touraj Taghikhany

 
Abstract
    The design of seismically isolated structures considering the stochastic nature of excitations, base isolators\' design parameters, and superstructure properties requires robust reliability analysis methods to calculate the failure probability of the entire system. Here, by applying artificial neural networks, we proposed a robust technique to accelerate the estimation of failure probability of equipped isolated structures. A three-story isolated building with susceptible facilities is considered as the analytical model to evaluate our technique. First, we employed a sensitivity analysis method to identify the critical sources of uncertainty. Next, we calculated the probability of failure for a particular set of random variables, performing Monte Carlo simulations based on the dynamic nonlinear time-history analysis. Finally, using a set of designed neural networks as a surrogate model for the structural analysis, we assessed once again the probability of the failure. Comparing the obtained results demonstrates that the surrogate model can attain precise estimations of the probability of failure. Moreover, our proposed approach significantly increases the computational efficiency corresponding to the dynamic time-history analysis of the structure.
 
Key Words
    seismic reliability; neural network; base isolation; friction pendulum; sensitivity analysis; equipment protection
 
Address
Hesamaldin Moeindarbari and Touraj Taghikhany: Civil and Environmental Engineering Department Amirkabir University of Technology, Tehran, Iran
 

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