Abstract
The prediction model of shear strength parameters of unsaturated soil based on indoor test data is established by using BP neural network. Five kinds of network models with different number of hidden layer nodes are trained and studied, and the best network model is selected to conduct the prediction. The results show that the optimal BP network model is a single hidden layer structure of 8-16-2. Using this model to predict, the correlation coefficient and regression coefficient between the predicted value and the measured value are high, and the predicted result is reliable, so the method has certain practicability.
Highlights
Unsaturated soil exists widely in nature, and its strength is often involved in engineering
The commonly used shear strength theories of unsaturated soil mainly include the effective stress shear strength theory proposed by Bishop [1] and the double stress state variable shear strength theory proposed by Fredlund [2]
Artificial neural network has a strong ability of nonlinear mapping and adaptive learning, which has a wide range of applications in the prediction of nonlinear systems
Summary
Unsaturated soil exists widely in nature, and its strength is often involved in engineering. The parameters in the shear strength expression are generally determined by the triaxial test method of unsaturated soil, which can control the drainage conditions and measure the pore water pressure. In this paper, the shear strength parameters of unsaturated silty sand are predicted by establishing BP neural network model and using some simple and easy to measure physical indexes as influencing factors. This method saves time and labor, avoids tedious theoretical calculation, and provides a method for the application of artificial neural network in unsaturated soil research
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