Prediction of karst for tunnelling using fuzzy assessment combined with geological investigations

Abstract This paper presents a method for predicting karst features before and during tunnel construction. The prediction of karst consists of two components: an initial karst prediction using a fuzzy assessment system to evaluate the underground karst state and an updating karst prediction where appropriate geological investigation methods are selected based on the assessment of underground karst state. The investigation results are then used to update the underground karst state. The initial assessment system is based on a fuzzy comprehensive evaluation method. Nine influence factors are selected as the evaluation indices for the underground karst state, and each index is quantitatively rated to four grades. The membership of the evaluation index is determined by using a membership function, and the weights of these indices are distributed by using a fuzzy Analytical Hierarchy Process. The fuzzy transform principle and maximum membership degree principle are applied to determine the underground karst state level. Based on the assessment result, several techniques for geological investigation, including the seismic reflection method, ground penetrating radar, infrared water detection, transient electromagnetic method, and advance probe boreholes, are recommended to predict the location, size, and distribution of karst features ahead of tunnel faces. These geological investigations have different characteristics and can be combined to improve the accuracy of the geological prediction. The appropriate combination of investigation methods is selected using the assessed underground karst state, and the investigation results are also used as the input to update the underground karst state. The proposed method can improve the prediction of karst in tunnelling. An application of this method was performed in the Doupengshan tunnel project.