Inversion Of Rock And Soil Mechanics Parameters Based On PSO Optimization Wavelet Support Vector Machine

There are two methods of on-site in-situ testing and indoor testing to determine the geotechnical mechanical parameters,but they all have certain limitations.The correctness of parameter selection has a great influence on the validity of numerical analysis results.Therefore,the support vector machine method is used to invert the geotechnical engineering parameters.This method can effectively solve the problems of small samples,nonlinearity,high dimensionality,and local minimum.Compared with the neural network methods used in early back analysis,support vector machines have obvious advantages in both theoretical basis and solving algorithms,and are increasingly valued by geotechnical engineering researchers.This article revolves around some problems with the current anti-analysis methods.Firstly,the numerical modeling method for geotechnical engineering is discussed from the perspective of improving modeling efficiency and modeling accuracy.Then from the aspects of the main factors affecting the generalization performance of SVM,namely support vector machine model parameters,support vector machine type and kernel function form,the systematic research on back analysis method of rock mechanical parameters based on support vector machine is carried out.And applied to engineering practice,the main content is summarized as follows:(1)Because of the influence of the kernel function form on the inversion result,the commonly used radial basis kernel function lacks the translation orthogonality,which makes the approximation ability of the corresponding support vector machine model limited and indirectly affects the accuracy of parameter identification.This wavelet function was introduced as a kernel function of support vector machine for inversion of rock mechanical parameters.(2)Using particle swarm optimization algorithm has the advantages of strong global search capability and high search efficiency,it is combined with support vector machine and applied to the optimization of the parameters of support vector machine model and the intelligent search of pavement modulus parameters.The analysis results show that the support vector machine model with the best prediction performance and the optimal parameter inversion result can be obtained effectively by using the particle swarm algorithm.(3)In combination with theoretical data and actual engineering data,the inversion of rock and soil mechanics parameters based on PSO-optimized wavelet support vector machine is performed.The results show that this method has a high inversion accuracy and is an effective and feasible parameter inversion method.