Identification Method of Dynamic Propagation Process of Rock Fracture Based on Ground Penetrating Radar - CHEN Jun; ZHANG Bo; ZHUANG Xingyue; SONG Zhishu; ZHENG Jun (工程科学与技术)
ObjectiveThe stability of rock engineering structures, crucial for national major construction projects, strategic security, and environmental protection, is significantly influenced by the dynamic propagation of rock mass fractures. These fractures, acting as weak planes and seepage channels, can lead to catastrophic events like landslides and collapses when they evolve progressively. While the existing monitoring techniques such as borehole imaging, CT scanning, and acoustic emission offer valuable insights, they fall short in dynamic tracking, spatial resolution, and engineering applicability, especially for complex fracture networks. This study addresses this gap by proposing a ground penetrating radar (GPR)-based method for identifying the dynamic propagation processes of rock fractures, aiming to provide a non-intrusive, real-time, and quantitative solution for rock mass stability assessment and disaster early warning.MethodsThis research adopts a comprehensive approach, integrating numerical simulations and physical experiments to systematically investigate the intricate relationship between rock fracture propagation and GPR signal response. The study utilizes GPRMax software, a powerful electromagnetic wave simulation tool, to construct numerical models of orthogonal fracture systems. These models simulate the dynamic evolution of electromagnetic wavefields under three distinct propagation scenarios: horizontal left-to-right extension, vertical bottom-to-top extension, and vertical top-to-bottom extension. To ensure the reliability of these simulations, physical experiments are conducted on rock-like specimens, meticulously prepared using concrete and wood to replicate realistic rock mass conditions. These specimens, featuring prefabricated fractures, undergo GPR detection, and the resulting data are compared with the numerical simulations to validate the accuracy of the models.Results and Discussions　The analysis of the GPR data reveals fascinating insights into the dynamic behavior of rock fractures. During horizontal propagation, the GPR signals exhibit a distinct three-stage characteristic. The initial stage, termed "independent expansion," is marked by the presence of two separate reflection curves, corresponding to the individual fractures. As the fractures continue to propagate, they enter the "collaborative coupling" stage, where the wavefields from each fracture interact, leading to interference patterns and amplitude modulation. This stage is characterized by the emergence of wavefield interference stripes and spectral overlapping. Finally, the fractures reach the "through-composite" stage, where the reflection signals merge into a complex waveform, forming a stable composite reflection structure. This stage signifies the complete connection of the fractures and the establishment of a unified wave propagation path. An inversion model, developed based on the analysis of phase mutation points and interference extrema, demonstrates remarkable accuracy in locating the spatial coordinates of fracture endpoints. The localization errors for the endpoints of the horizontally propagating fractures are consistently below 2%, proving the reliability of this method for real-time monitoring and early warning of fracture propagation.In contrast, the vertical propagation of fractures presents a more complex scenario. When the fracture propagates upwards, the "shielding effect" caused by the overlying fracture significantly hinders the GPR signal's ability to detect the lower fracture tip. This effect results in only two identifiable stages: the "independent expansion" stage, where the GPR signal is primarily influenced by the upper fracture, and the "penetration zone" stage, where the signal changes dramatically upon the two fractures connecting. Similarly, when the fracture propagates downwards, the weak reflection energy from the lower fracture tip, coupled with signal attenuation, limits the GPR's ability to capture the detailed propagation process. Consequently, only the "independent expansion" stage and the "penetration zone" stage can be identified, and the accuracy of the vertical trajectory inversion is compromised.To overcome these limitations, the study proposes a time-sequenced multi-azimuth joint detection technique. By integrating data from multiple GPR antenna positions and incident angles, this approach effectively mitigates the shielding effect and enhances the resolution of three-dimensional dynamic fracture evolution. This technique allows for a more comprehensive understanding of the fracture propagation process, capturing details that would be missed with a single detection perspective.Furthermore, the study explores the potential of combining GPR with other monitoring techniques and artificial intelligence algorithms. The integration of GPR with borehole imaging and acoustic emission data can provide a multi-scale monitoring system, offering a more comprehensive view of the fracture network from micro-crack initiation to macro-scale propagation. Additionally, the application of deep learning algorithms, such as convolutional neural networks and Transformers, can automate the process of GPR image analysis, reducing human bias and improving the efficiency of data interpretation.ConclusionsThe findings of this study demonstrate the significant potential of GPR technology for dynamic monitoring of rock mass fractures. The proposed method effectively captures the dynamic evolution of fractures and provides reliable spatial positioning of fracture endpoints, offering a valuable tool for real-time monitoring and early warning of potential rock mass instabilities. However, challenges remain in addressing the heterogeneity of fracture fillings, the dynamic coupling effects in multi-fracture systems, and the rapid interpretation of large-scale monitoring data. Future research should focus on developing advanced algorithms to suppress interference from non-uniform fillings, resolve dynamic coupling mechanisms, and improve the accuracy of vertical trajectory inversion. The integration of GPR with other monitoring techniques and artificial intelligence algorithms will further enhance the capabilities of rock mass stability assessment and disaster early warning, paving the way for safer and more sustainable rock engineering practices. By addressing these challenges, GPR technology can transition from laboratory research to complex engineering applications, contributing to the safety and stability of rock engineering structures worldwide.