000019485 001__ 19485
000019485 005__ 20170118182320.0
000019485 04107 $$aeng
000019485 046__ $$k2017-01-09
000019485 100__ $$aMoeindarbari, Hesamaldin
000019485 24500 $$aReliability Based Optimization of a Seismically Isolated Structure Using Artificial Neural Networks as the Response Surface Method

000019485 24630 $$n16.$$pProceedings of the 16th World Conference on Earthquake Engineering
000019485 260__ $$b
000019485 506__ $$arestricted
000019485 520__ $$2eng$$aOne of the efficient methods in seismic resistant design of medium height buildings is applying seismic isolation systems at the base of the structures to mitigate the response of structure. In this study an effective numerical reliability-based optimization technique is presented for the optimum design of isolation system under random time history earthquake loading. Friction Pendulum System (FPS) as one of the popular types of seismic isolation devices is considered to protect delicate equipment installed on the floor of a specific concrete building. So the object is to minimize the probability of failure of the base-isolated building subjected to design performance criteria in terms of the story acceleration of the superstructure. Due to stochastic nature of variables such as input ground motion; a novel method is proposed to predict the reliability of the supposed structure using artificial neural networks (ANN). The reliability of the system in the format of probability of failure (Pf) is calculated using a simulation based method which is an effective tool for an isolated structure subjected to random earthquake excitations. A 2D concrete frame three story building isolated with FPS, representing critical facilities, such as a data center, is considered as the super structure. Random excitations are applied by the means of artificial earthquake ground motions generated through the superposition of the long period and high frequency components of the earthquake. The probability of failure for a particular set of structure and isolation parameters is calculated using Monte Carlo Simulation by time history structural analysis at first. Then a set of neural networks were trained to predict the peak responses of the structure and furthermore to predict the probability of failure. Then using a meta-heuristic optimization algorithm the design parameters of the structure and isolation systems are obtained.

000019485 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000019485 653__ $$aNeural Network, Reliability Based Optimization, Base Isolation, Friction Pendulum

000019485 7112_ $$a16th World Conference on Earthquake Engineering$$cSantiago (CL)$$d2017-01-09 / 2017-01-13$$gWCEE16
000019485 720__ $$aMoeindarbari, Hesamaldin$$iTaghikhany, Touraj
000019485 8560_ $$ffischerc@itam.cas.cz
000019485 8564_ $$s608135$$uhttps://invenio.itam.cas.cz/record/19485/files/3746.pdf$$yOriginal version of the author's contribution as presented on USB, paper 3746.
000019485 962__ $$r16048
000019485 980__ $$aPAPER