Surface Subsidence Prediction Of Deep Foundation Pit Based On Support Vector Regression

In recent years,with the large-scale construction of urban rail transit in China,the construction of subway station deep foundation pit is increasing day by day.Therefore,the safety problem in the construction of subway deep foundation pit engineering attracts more and more attention.The monitoring and predicting of ground surface subsidence during the excavation of deep foundation pit is one of the most fundamental element.Due to the hysteresis in the monitoring and feedback of deep foundation pit in subway,it is fairly important to adjust the construction plan by predicting the surface subsidence of deep foundation pit in subway in advance.In the process of prediction,the monitoring data has some characteristics like “inadequate sample” and “strict requirement of forecast accuracy”.Whereas,machine learning algorithms can better handle those data.Based on machine learning algorithms,subway deep foundation pit of theoretical analysis and actual monitoring cumulative settlement data,this paper regards the subway deep foundation pit as well as the surrounding surface of the earth as a whole.On the basis of the investigation of the factors,the ground settlement of the subway deep foundation pit,this paper intends to choose multi-core SVR model as the prediction model of ground settlement.First of all,this paper reviews the related theories about the factors influencing the surface settlement of deep foundation pits both at home and abroad,and gives out the evaluation index system for the surface settlement of deep foundation pits in subway.A quantitative standard table for the factors affecting the surface settlement of deep foundation pits in subway is established,too.Then,according to The subway deep foundation pit surface subsidence influencing factors of relative importance questionnaire survey,the study of questionnaire about the construction of the subway engineers,the on-site consulting,as well as ahp,it is concluded that the subway deep foundation pit of surface subsidence various factors in the evaluation index system of influencing factors of initial weights.Finally,a subway deep foundation pit in Wuhan city was selected.Based on the general situation of the project,the corresponding values of each influencing factor were assigned.Four monitoring points in the eighth section of the excavation of the foundation pit were selected to correspond to the accumulated settlement monitoring data.Through linear support vector regression to predict the surface settlement,the subway deep foundation pit and the weight of each factors in the subway deep foundation pit surface subsidence,as the initialization of the linear support vector regression model to predict the surface subsidence,and based on the nonlinear support vector regression subway deep foundation pit under the surface subsidence prediction results comparison,the results showed that predicting precision of the nonlinear of multi-core support vector regression for subway deep foundation pit surface subsidence is higher.Based on the monitoring data of surface subsidence of deep foundation pit in subway,the SVR model of surface subsidence of deep foundation pit in subway is established in this paper.In the construction of deep foundation pit,it is beneficial for the construction unit to timely adjust the construction plan according to the predicted results,so as to reduce the safety accidents in the excavation of deep foundation pit.This prediction model has great engineering application significance for the construction of deep foundation pit of subway and has reference value for similar projects in the future.