Deformation Prediction of deep excavation Using Support Vector Machine

Support Vector Machine (SVM) is a new pattern recognition method developed in recent years on the foundation of statistical learning theory. It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. Due to the complexity of the deep excavation, deformation prediction problem has not been a good solution. In the paper the support vector machine model was proposed to predict the deep excavation deformation. On the basis of deep excavation displacement data measured with real time series, the model of deep excavation displacement with time was built by SVM. Typical deformation data of deep excavation is used as learning and test samples. Comparison analysis is made between calculated values generated by SVM method and observed values. The result shows this method is feasible and effective.