RISK PREGNANT ENVIRONMENT EARLY-WARNING MODEL FOR UNDERGROUND STRUCTURE BASED ON IMPROVED SUPPORT VECTOR MACHINE

This paper addresses the limited complex and nonlinear characteristics in earthquake-induced risk pregnant environmental sample data. It puts forward a risk early-warning method for such pregnant environment around underground structure on least squares support vector machine. Around risk early-warning object and parameters in after-earthquake risk pregnant to underground engineering, the risk pregnant environment is expressed and designed with Support Vector Machine, and SVM training mechanism was proposed for risk pregnant environment. Such risk early-warning mold and the component is erected for risk pregnant environment around underground structure on least squares support vector machine, which utilizes structural risk minimization principle and nonlinear mapping feature of SVM, and optimizes the penalty function and kernel function parameters with Genetic Algorithm. The model implicitly expresses the non-linear relationship among the risk pregnant environment and factors. Case study shows such model has an effective small sample learning ability, well fitting and forecasting accuracy, which excels the predicting model with BP nerve network.