Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks

M. PAPADRAKAKIS; PAPADOPOULUS V.; LAGAROS N.D.; J. OLIVER,; A E. HUESPE; P. J. SANCHEZ

STRUCTURAL SAFETY

ELSEVIER SCIENCE BV

Año: 2008 vol. 30 p. 217 - 235

0167-4730

Probabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importancelike dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Simulation (MCS) procedureis proposed for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite elementanalysis for the prediction of the bearing capacity of the Dam using the Continuum Strong Discontinuity Approach.The use of NN was motivated by the approximate concepts inherent in vulnerability analysis and the time consumingrepeated analyses required for MCS. The Rprop algorithm is implemented for training the NN utilizing available informationgenerated from selected non-linear analyses. The trained NN is then used in the context of a MCS procedure tocompute the peak load of the structure due to different sets of basic random variables leading to close prediction of theprobability of failure. This way it is made possible to obtain rigorous estimates of the probability of failure and the fragilitycurves for the Scalere (Italy) dam for various predefined damage levels and various flood scenarios. The uncertain properties(modeled as random variables) considered, for both test examples, are the Young?s modulus, the Poisson?s ratio, thetensile strength and the specific fracture energy of the concrete.