000018863 001__ 18863
000018863 005__ 20170118182244.0
000018863 04107 $$aeng
000018863 046__ $$k2017-01-09
000018863 100__ $$aZarfam, Panam
000018863 24500 $$aDevelopment of Seismic Fragility Curves of Horizontal Curved Bridge Using Neural Network Prediction.

000018863 24630 $$n16.$$pProceedings of the 16th World Conference on Earthquake Engineering
000018863 260__ $$b
000018863 506__ $$arestricted
000018863 520__ $$2eng$$aIn this study a Soft Computing approach for the seismic fragility assessment of horizontal curved bridge is developed. In recent years, seismic fragility curves are often determined by using analytical method in structures. In order to build neural network structure, Nonlinear time history analysis is performed by 129 natural records in OpenSees. This records have been chosen from the PEER strong motion database and scaled on 0.1g to 1.3g. The structure of the neural network is based on input ground motions and output of nonlinear dynamic analyses of bridge. Arias Intensity, cumulative absolute velocity, characteristic intensity and specific energy density reflect the amplitude, the duration of a strong ground motion, the frequency content and energy respectively, and they correlate well with structural damage. Kolmogorov-Smirnov and Shapiro-Wilk tests was performed to normalize the data. Approach to reducing the computational effort in the evaluation of fragility, a neural network was considered in this study, which can provide accurate predictions of the structural response. The proposed approach is applied for bridge and a reduction of magnitude is achieved in the computational effort.

000018863 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000018863 653__ $$anerual network; statistical distribution; fragility curve; bridge

000018863 7112_ $$a16th World Conference on Earthquake Engineering$$cSantiago (CL)$$d2017-01-09 / 2017-01-13$$gWCEE16
000018863 720__ $$aZarfam, Panam$$iMoridani, Komeyl Karimi
000018863 8560_ $$ffischerc@itam.cas.cz
000018863 8564_ $$s759232$$uhttps://invenio.itam.cas.cz/record/18863/files/2372.pdf$$yOriginal version of the author's contribution as presented on USB, paper 2372.
000018863 962__ $$r16048
000018863 980__ $$aPAPER