Estimativa dos parâmetros da resistência do solo ao cisalhamento através de pedotransferência

Abstract

The growing world population requires a higher demand for food and one of the techniques to meet this demand is irrigation. One of the best alternatives for the storage of water for use in irrigation are earth dams. The accurate determination of cohesion and angle of internal friction is an essential and of great concern in the drafting of earth dams process criteria, but their determination is expensive and also time consuming process. The objective of this study is to evaluate a model that allows an estimation of soil shear strength using two different techniques (multivariate analysis and artificial neural networks) to obtain the strength parameters (cohesion and angle of internal friction) as a function of textural composition , density, Atterberg limits (plasticity, liquidity and plasticity index) and the degree of soil moisture. Different database were searched in the literature with the dependent and independent variables needed to conduct the study. 6 dataset were totaled. PTFs were generated through multiple linear regression (MLR), stepwise, and artificial neural networks (ANN) with each data set. Through MLR were estimated friction angle and cohesion separately since the RNAs were estimated jointly and separately maintaining these two parameters form the architecture (one hidden layer) and varying the topology of the networks (10, 20, 30, 40 , 50 and 70 neurons in the hidden layer). After the performance index (Id) and subsequent classification of each FPT was calculated. The results demonstrated the inefficiency in MLRs to estimate parameters and the superiority of ANN to predict the cohesion and friction angle. Estimation of the parameters together shows different results than when estimated separately. Thus, the estimated shear the soil parameters RNAs can be effective for a given set of data, in this case belonging to RNAs 3, 5 and 6.