Multi-Model Intelligent Prediction of Rock Integrity in Tunnels Based on Geological Differences of Ground-Penetrating Radar Exploration Workfaces

Intelligent prediction of rock integrity is essential for tunneling construction. Ground-Penetrating Radar (GPR), a high-resolution detection technique, is usually used for rock integrity prediction. However, the geological conditions of the detection workface are rarely considered when utilizing the GPR to forecast rock integrity. In this paper, a multi-model intelligent prediction method for tunnel rock integrity based on geological differences of GPR exploration workfaces is proposed. Firstly, the structural features are extracted from the GPR detection data through matrix calculations. A statistic is proposed to judge the abnormal data, and filtering rules are formulated to eliminate abnormal data. Then, considering the difference of geological conditions of the GPR exploration workface, multi-models are established with different degrees of fragmentation of the exploration workface. Finally, the validity of the multi-model prediction method is proved by practical engineering verification.