Papers by Author: Sbartai Badreddine

Abstract: Artificial Neural Networks (ANN) has seen an explosion of interest over the last few years. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. Hence, the main objective of this paper is to develop a model to predict the response of the soil-structure interaction system without using the calculate code based on sophisticate numerical methods by the employment of a statistical approach based on an Artificial Neural Network model (ANN). In this study, a data base which relates the impedance functions to the geometrics characteristic of the foundation and the dynamic properties of the soil is implemented. This leads to develop a neural network model to predict impedances functions (all modes) of a rectangular surface foundation. Then the results are compared with unused data to check the ANN model’s validity.

Abstract: This study consists in estimating the risk (potential) of liquefaction of a soil deposit saturated when it is submitted to a horizontal seismic excitement to its basis while using the approach in total constraints. This approach requires the determination of the shear constraint τa developed by the seismic solicitation, For the determination of τa, an equivalent linear dynamic analysis with concentrated mass has been used to adapt (iterable proceeding) the module of shear and the factor of damping according to the level of the resulting shear deformations because, the constraint-distortion diagram of the layer is nonlinear with dissipation of energy by hysteresis for each cycle. For this reason, in this survey, the behavior of soil has been represented by a hyperbolic relation of Hardin and Drenevich. For the determination of τl, the graph of Seed and al based on the results of the SPT. The report of the two constraints permits the definition of safety factor against the risk of liquefaction (CSL).