The probabilistic approach, based on the Monte Carlo method has been recently introduced to simulate cracking of concrete in the framework of a finite element analysis. When a procedure based on this approach is used, N samples of the vector of random variables (tensile strength, Young modulus, etc.) are generated from a specific probability density function. If the uncertainties of these material parameters are assumed to vary spatially following a normal distribution, the N samples corresponding to a simulation are function of the mean and the standard deviation that defines the Gauss density function. The problem is that these statistical moments are not known, a priori, for the characteristic volume of the finite elements for which the problem has been discretized. In this paper neural networks are used to evaluate the parameters characterizing the statistical distribution (e.g., for a normal distribution: the mean and the standard deviation) for a given response of the structure (for instance, an average load-displacement curve) following an inverse analysis procedure. It is shown that the presently presented procedure improves a recently proposed algorithm, which is able to solve the problem, but is very hard to operate.

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