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Novel procedure for reliability‐based cost optimization of seismically isolated structures for the protection of critical equipment: A case study using single curved surface sliders
Author(s) -
Moeindarbari Hesamaldin,
Taghikhany Touraj
Publication year - 2018
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2054
Subject(s) - reliability (semiconductor) , simulated annealing , monte carlo method , range (aeronautics) , reliability engineering , ground motion , sensitivity (control systems) , artificial neural network , structural engineering , optimal design , random variable , engineering , computer science , algorithm , mathematics , artificial intelligence , statistics , power (physics) , physics , quantum mechanics , electronic engineering , machine learning , aerospace engineering
Summary The optimized reliability‐based design of seismic isolated structures requires a consideration of the failure probability of the system, in addition to the optimization objectives. The stochastic nature of a given system (e.g., the probable input ground motion) must therefore be properly included in the analysis model. To address this challenge, a novel procedure is developed and examined on a 3‐story isolated concrete building model, to minimize the total construction cost of the system with regards to protecting sensitive equipment located within the structure. In this procedure, the structure performance and reliability were first evaluated using time‐consuming Monte Carlo simulations. We then employed artificial neural networks as a response surface to facilitate the prediction of the failure probability for the supposed structure. To simulate seismic excitations, we generated artificial ground motion combining random high‐frequency and long‐period components. Also, a newly developed sensitivity analysis method was used to identify the critical uncertain parameters of the system. Finally, by using a simulated annealing algorithm, we determined the optimal design variables of the structure and isolation system for a range of desired probabilities of failure. The optimal results indicate that, for different target failure probability ranges, some design variables are more significant than others.