Simple models for the estimation of shearing resistance angle of uniform sands
Angle of shearing resistance is the key for the strength analysis of soils, since this parameter is commonly used for the description of shear strength of a soil. Many factors including soil mineralogy, particle shape, grain size distribution, void ratio, organic content, as well as water existence are effective on this parameter. Use of shear box tests for the determination of angle of shearing resistance is prevalent in geotechnical engineering practice, since it is rather successful in identification of shear strength of granular media. However, for cases in which shear box tests exhibit unreliable outcomes, alternative methods for the determination of this parameter could be beneficial. In this investigation, a number of nonlinear multiple regression, adaptive neuro-fuzzy inference systems, and artificial neural network (ANN) models are employed for the estimation of estimating the angle of shearing resistance of uniform sands by means of several grain size distribution, particle shape, and density parameters. Data including results of 132 shear box tests, particle shape identifiers, and grain size distribution parameters on uniform sands are used in the models. In training sessions, results of 104 tests are selected randomly and the results of remaining 28 tests are considered for testing sessions. The results revealed that the performance of a simple ANN architecture is sufficient for pre-evaluation of shearing resistance angle of uniform sands with the help of selected parameters. Since generalization of these models necessitates vast amount of experiments, great care should be dedicated on the assessment of similarity of training as well as testing data.
angle of shearing resistance particle shape nonlinear multiple regression artificial neural networks anfis