SEISMIC VULNERABILITY ASSESSMENT OF LARGE-SCALE GEOSTRUCTURES
Seismic vulnerability analysis of structural and infrastructural systems is commonly performed by means of fragility curves. There are two approaches for developing fragility curves, either based on the assumption that the structural response follows the lognormal distribution or using reliability analysis techniques for calculating the probability of exceedance for various damage states and seismic hazard intensity levels. The Monte Carlo Simulation (MCS) technique is considered as the most consistent reliability analysis method having no limitations regarding its applicability range. Nevertheless, the only limitation imposed is the required computational effort,which increases substantially when implemented for calculating lower probabilities. Incorporating artificial neural networks (ANN) into the vulnerability analysis framework enhances the computational efficiency of MCS, since ANN require a fraction of time compared to the conventional procedure. Thus, ANN offer a precise and efficient way to determine a geostructures seismic vulnerability for multiple hazard levels and multiple limit states.
slope stability fragility curves neural networks Monte Carlo simulation
Y. Tsompanakis N.D. Lagaros P. N. Psarropoulos E.C. Georgopoulos
Dept. of Applied Mechanics, Technical University of Crete, Chania, Greece Dept. of Civil Engineering, National Technical University of Athens, Athens, Greece
国际会议
14th World Conference on Earthquake Engineering(第十四届国际地震工程会议)
北京
英文
2008-10-12（万方平台首次上网日期，不代表论文的发表时间）