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Seismic vulnerabity assessment of large -scale geostructures

Y. Tsompanakis, N.D. Lagaros, P. N. Psarropoulos, E.C. Georgopoulos

Πλήρης Εγγραφή

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Περίληψη

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 geostructure’s seismic vulnerability for multiple hazard levels and multiple limit states.

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