An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine

Abstract

The prediction of the grouting volume is a very important task in the grouting quality control. Because of the concealment and complexity of the karst curtain grouting engineering, there is little research on the prediction of the karst curtain grouting volume (KCGV), and the prediction is hindered by the practical problems of small samples, high dimensions, and nonlinearity. In the study, based on the basic idea of support vector machine (SVM), a multiparameter comprehensive intelligent prediction method of the KCGV is proposed, which overcomes the limitation of few sample data in practical engineering. This method takes the grouting construction conditions and the slurry conditions which control the slurry diffusion as the input parameters, which are the basic data which can be easily obtained in the field grouting process. This feature greatly improves the prediction accuracy and generalization performance of the method. The intelligent prediction method of the KCGV based on SVM is applied to a typical karst curtain grouting project. The mean absolute error of the prediction results is 3.47 L/m, and the mean absolute percentage error of the prediction results is 5.97%. The results show that the proposed prediction method has an excellent prediction effect on the KCGV and can provide practical and beneficial help for the field karst curtain grouting project.