Heterogeneity identification method for surrounding rock of large-section rock tunnel faces based on support vector machine

Abstract

PurposeThe purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approachRelying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.FindingsThe results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.Originality/valueBy applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

Dynamic Numerical Simulation and Transfer Learning-Based Rapid Rock Identification during Measurement While Drilling (MWD)

“…In recent industrial applications, many geological surveying equipment companies have developed MWD systems to address geotechnical survey issues. The main systems include: the MWD module of the RPD series drilling rig by Japan's KOKEN Company [10], used for predicting geological conditions ahead, such as the hardness of strata, cavities, spatial distances, and water inflow; this system is extensively employed in tunnel engineering; the DEFI geological prediction system by France's JEAN LUTZ Company [11], which can be integrated into various multifunctional drilling rigs for predicting rock layer geological conditions; and the EXPLOFOR system by France's Apageo Company [12], which collects drilling parameters to analyze geotechnical profile information. However, despite their advantages, MWD systems also present notable disadvantages.…”