The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.

The structure of rocks plays a crucial role in their failure process. However, it is ignored that the interactions between rock internal structure and the effect of its own evolution on the rock fracture process. To investigate the effect between the evolution law of rock regionalized structures and their interaction relationships during failure. We conducted an experiment using visual acoustic imaging monitoring to study rock failure, introducing a new concept of characteristics of rock structure—regionalized structures. The findings reveal three main types of regionalized structures in rocks: skeleton regions, variable regions, and damage regions. These structures combine to form four categories of complex rock structures: block-type support skeletons, point column-type support skeletons, suspension-type weak support skeletons, and no skeletons. During the failure process, we found that these regionalized structures worked together synergistically to control rock failure. Although the evolutionary relationships among the structures show some similarities, the final fracture states vary significantly. Stress and strain distribution patterns clearly demonstrate that variations in the force capacities and roles of the regionalized structures influence the synergistic evolutionary relationships, ultimately impacting the mode of rock failure. This work provides new insights for further research on rock failure mechanisms and can significantly contribute to preventing rock engineering disasters related to regionalized structures.

Accurate classification of rock fracture modes is essential for understanding rock mass instability mechanisms. To address the limitation of traditional acoustic emission (AE) classification methods that treat a single AE signal as a single fracture event, overlooking its composite nature from multiple fracture events and leading to misclassification, this study proposes a novel rock fracture mode classification method based on AE spectral analysis. This study details the development framework, theoretical model, classification criteria, application process, and experimental validation of the new rock fracture mode classification method. Uniaxial compression tests on granite, marble, and limestone, along with rockburst simulation tests on granite, were conducted to validate the classification of fracture modes. In rockburst simulations, shear fracture signals accounted for 48% on average, composite signals 40%, and tensile signals 12%. The method effectively distinguishes multiple fracture events within a single AE signal, accurately classifies fracture modes, and elucidates the dynamic evolution of fracture modes during the rockburst precursor stage, offering significant advantages for rock fracture mode classification and mechanistic insight.

Extracting nodal features is crucial for analyzing rock structure stability and plays a significant role in designing engineering projects. This study presents an enhanced version of the FraSegNet algorithm, focusing on improving its ability to identify nodal features in images. The updated FraSegNet incorporates the ResNet101 backbone and integrates the Squeeze-and-Excitation (SE) attention mechanism, enabling better concentration on key nodal characteristics. The primary improvements are as follows: (1) Multi-scale feature extraction: Leveraging the ResNet101 architecture for the effective extraction of detailed information from nodal images. (2) Better attention mechanisms: The SE module focuses on nodal regions, resulting in clearer and more refined feature representations. (3) Dynamic learning strategies: I incorporation of cosine annealing and warm-up techniques to optimize training efficiency. The algorithm was validated with the Barton–Bandis model and Hoek–Brown criterion. The experimental results demonstrate its superior performance, achieving 97.1% accuracy in nodal feature detection with an average error of only 1.5% compared to the rock mass parameter. This small error proves the model works well. FraSegNet offers accurate segmentation and precise geometric parameter extraction, making it a valuable tool for advancing rock stability analysis and practical applications in rock mechanics.

To characterize the three-dimensional evolution of creep damage in deep roadway surrounding rock, this study establishes a COMSOL-based (Multiphysics 6.3) integrated analysis framework coupling creep damage, seismic velocity response and three-dimensional imaging. The framework adopts the Norton–Bailey creep constitutive model, Weibull-distributed material heterogeneity and an elastic damage model to simulate the 210-day creep damage evolution of surrounding rock. The three-dimensional P-wave velocity field is then reconstructed via a regional correlation approach using active-source P-wave first-arrival times. The results show that with progressive damage accumulation, low-velocity zones gradually expand from the intersection of the fault and roadway roof towards both the fault zone and the surrounding roadway. From 90 d to 210 d, these anomalies develop into a large-scale regional low-velocity belt with significantly enhanced spatial connectivity. The fault weak plane exerts a controlling effect on damage localization and the formation of low-velocity zones. Numerical results demonstrate a distinct spatiotemporal correspondence between P-wave low-velocity zones and damage concentration areas. Furthermore, the predicted damage distribution agrees well with the field failure morphology of surrounding rock, verifying that the proposed method can effectively track creep damage evolution in rock masses and provide an analytical basis for dynamic stability evaluation and early warning of deep roadway surrounding rock.