Assessing model of highway slope stability based on optimized SVM |
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| Institution: | 1. School of Prospecting Technology and Engineering, Hebei GEO University, Shijiazhuang 050031, China;2. Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang 050031, China;3. Shanxi Institute of Geological Survey, Taiyuan 030006, China |
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| Abstract: | Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment. |
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| Keywords: | Highway slope Principal component analysis Genetic algorithm Support vector machine Stability Geological hazard engineering |
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