Perm, Perm, Russian Federation
UDC 691
UDC 625
The study is based on a methodology for calculating transitional pavements for logging roads using geosynthetic interlayers, based on the use of artificial neural networks (ANN). Numerical modeling of the stress-strain state, performed in the PLAXIS 2D environment (finite element method), allowed us to obtain a dataset of 414 calculation series. This dataset, including 13 input and 2 output variables, served as the basis for training and validation of the developed intelligent neural network. Using the neural network, we identified weighting coefficients for the influence of input parameters on the value of output parameters. The results showed that such factors as the elastic modulus of the subgrade soil, the thickness of the pavement surface, and the linear stiffness of the geosynthetic material have the greatest influence on the magnitude of vertical deformation. For the tensile force in the geosynthetic interlayer, the following factors have the greatest influence: the elastic modulus of the subgrade soil, the linear stiffness of the geosynthetic material, and the thickness of the pavement base. The predictive calculation results demonstrated high accuracy in predicting output parameters using the developed neural network, with an average error of 9.24%. Thus, the developed neural network allows for a fairly accurate prediction of the magnitude of vertical displacement on the road surface, i.e., the elastic modulus of the road surface and the tensile force in the reinforcing layer.
Highways, logging roads; geosynthetic reinforcing materials; transitional road surfaces, finite element method, intelligent neural network, load, linear elastic model
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