# Research on Grouting Dynamic Monitoring Based on Borehole-Tunnel Joint Resistivity Method
[16] established a hybrid AHP-CRITIC weighting model integrated with TOPSIS-RSR methods, creating a comprehensive grouting effect evaluation system proven effective in engineering practice. [PERSON] et al. [17] developed a \"grout-rock\" seepage instability model for mining-induced environments using fiber-optic and microseismic monitoring, while [PERSON] et al. [5] formulated a unidirectional coupling model linking excavation-induced stress to plastic failure and grout diffusion. [PERSON] et al. [18] independently validated resistivity monitoring for detecting grout penetration and evaluating long-term rock-grout composite stability. Optical fiber monitoring technology also demonstrates potential. [PERSON] et al. [19] constructed a \"grout-rock\" interface slip early-warning model based on distributed optical fiber strain monitoring. [PERSON] et al. [20] successfully monitored the penetration of cement milk grout using direct current and controlled-source audio-frequency magnetotelluric measurements. Australian scholars [PERSON] et al. [21] studied grouting effects through combined resistivity-microseismic inversion imaging, achieving dynamic updates of the permeability coefficient of grouting curtains. In the context of multi-parameter sensor fusion monitoring methods, [PERSON] et al. [22] proposed a multi-sensor data fusion (MDF) approach combined with radial basis function neural network (RBFN) to monitor the settlement of the Kerrada rockfill dam in Algeria. Although geophysical techniques offer non-destructive advantages, existing single-tunnel or borehole monitoring systems (such as traditional electrical resistivity tomography (ERT) and borehole televiewer (BT)) still have significant limitations: 2D survey lines cannot capture the 3D diffusion characteristics of grout, insufficient inter-borehole resolution leads to path inversion distortion, and most systems rely on manual intervention with a lack of real-time capability, see Table 1.  
In the modern research of mining technology digitization, intelligent controllers have made remarkable progress in drill string dynamic monitoring. For example, [PERSON] et al. [23] developed a drill string vibration monitoring system based on a Smart 4 controller, which was first verified in an industrial environment for its real-time evaluation capability of downhole vibration loads. Similarly, [PERSON] et al. [24] used machine learning models to analyze surface drilling rig data, achieving high-precision prediction of three vibration types in horizontal drilling. The electrical resistivity method can detect the diffusion range of grout around boreholes, but due to technological limitations, it only collects data before and after grouting, qualitatively analyzing grouting effects. It lacks real-time monitoring of the entire grouting process and thus cannot obtain the electrical property data of the strata throughout the grouting process, making it difficult to accurately evaluate grouting effects. This study for the first time realizes the 3D dynamic reconstruction of grout diffusion paths and the time-series tracking of resistivity during the consolidation process. Using a roadway-borehole combined electrical resistivity monitoring system, a 3D cross-monitoring network is constructed through the 3D collaborative deployment of roadway and borehole electrodes,