Abstract
The safety problem of the slope has always been an important subject in engineering geology, which has a wide range of application background and practical significance in reality. How to correctly evaluate the stability of the slope and obtain the parameters of the slope has always been the focus of research and production personnel at home and abroad. In recent years, various artificial intelligence calculation methods have been applied to the field of rock engineering and engineering geology, providing some new ideas for the solution of slope stability analysis and parameter back analysis. Support vector machine (SVM) algorithm has unique advantages and generalization in dealing with finite samples and highly complex and nonlinear problems. At present, it has become a research hotspot of intelligent methods and has been widely paid attention to in various application fields of slope engineering. In this paper, a cuckoo search algorithm-improved support vector machine (CS-SVM) method is applied to slope stability analysis and parameter inversion. Aiming at the problem of selecting kernel function parameters and penalty number of SVM, a method of using cuckoo search algorithm to improve support vector machine was proposed, and the global optimization ability of cuckoo search algorithm was used to improve the algorithm. Aiming at the slope samples collected, the classification algorithm of support vector machine (SVM) was used to identify the stable state of the test samples, and the improved SVM algorithm was used to analyze the safety factor of the test samples. The results show that the proposed method is reasonable and reliable. Based on the inversion of the permeability coefficient of the test samples by the improved support vector machine, the comparison between the inversion value and the theoretical value shows that it is basically feasible to invert the permeability coefficient of the dam slope by the improved support vector machine.
Highlights
Slope is formed by natural condition or artificial reason
Support vector machine (SVM), as a new machine learning method developed in recent years, has a good theoretical basis and unique advantages in dealing with nonlinear and highly complex finite sample application problems. e cuckoo search algorithm, as a new meta-heuristic optimization algorithm, has the advantages of searching path segment and finding optimization ability and has better generalization
The application of improved cuckoo search algorithm in slope stability analysis and parameter inversion is studied. e main achievements are as follows: (1) cuckoo search algorithm code is compiled from MATLAB platform, and it is applied to the inversion of conductivity coefficient and storage coefficient of one-dimensional groundwater system; (2) using the global optimization ability of cuckoo search algorithm, the kernel
Summary
Slope is formed by natural condition or artificial reason. Slope engineering is an important subject in engineering geology, which has a wide range of application background and practical significance in reality [1,2,3]. Qualitative analysis method is through the engineering geological investigation, based on slope stability influence factors (such as the engineering geological and hydrogeological conditions, tectonic movement, topography, climate, and human engineering activities) of the comprehensive consideration, macroscopic analysis of the causes of slope deformation and the development and evolution of geological structure and landform can study the potential forms of deformation and failure. The actual slope problems relating to the engineering geological conditions and mechanical parameters are highly nonlinear and complex and are difficult to determine with the analytical model to describe the engineering practice of state, at the same time, the nonlinear conditions can greatly increase the computing workload, and numerical method selection method was hard to get the global optimal solution. Similar to slope stability analysis, the application of various heuristic artificial intelligence methods has provided
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