Grouting technology is an important means for controlling strata disasters and has been widely employed in mining engineering. Grouting materials are the core of grouting technology, and numerous types of grouting materials have been developed globally. Among these, the sulfoaluminate cement-based grouting material has achieved excellent results in fractured rocks reinforcement, filling, and water blocking projects due to its high stone strength and rapid growth, adjustable setting time, no sedimentation or segregation of the slurry, simplified operational requirements, and cost-effectiveness. To further promote in-depth research on the grouting technology of sulfoaluminate cement-based materials and its application in strata disaster control fields, this study reviews the research on sulfoaluminate cement-based materials and the grouting process, points out the shortcomings and prospects of current research, and presents our typical engineering application cases of sulfoaluminate cement-based grouting materials.

In this study, the impact of rock mass anisotropy—arising from the presence and orientation of discontinuities—on tunnel behavior under combined static and dynamic loading was investigated using numerical modeling. The tunnel lining internal force (TLIF) and displacement responses were analyzed by simulating different joint dip angles and in-situ stress ratios through the jointed rock model (JRM). The dynamic loading is applied as a horizontally propagating shear-wave acceleration time history with a peak ground acceleration of 0.3g and dominant frequencies representative of moderate seismic excitation. Results reveal that anisotropy significantly affects stress redistribution and deformation patterns, especially under low stress ratios (K = 0.5) and joint dips around 60°, where bending moments and shear forces can increase by up to 250% relative to the isotropic reference model without discontinuities. In contrast, axial forces and displacements may reduce depending on the joint configuration. The findings provide insight into seismic-induced instability mechanisms in jointed rock masses and help improve understanding of tunnel performance under geohazard-related dynamic loading.

Large deformation disasters in deep-buried hard rock tunnels under high stress represent a major challenge for the safety and efficiency of coal mining. Traditional pressure relief methods often fail to reliably stabilize the surrounding rock. To overcome the limitations of conventional pressure relief techniques for deeply buried hard rock tunnel drilling, this study introduces a new pressure relief method for roof drilling and validates it through theoretical analysis, numerical simulation, similarity modeling, and on-site monitoring. The research examines how drilling in the upper tunnel affects stress release and redistribution in the surrounding rock of the lower tunnel, providing a scientific foundation for measuring stress transfer and the effectiveness of pressure relief. A multi-criteria evaluation system was developed to assess the efficiency of the new drilling approach, focusing on pressure relief performance, rock stability, and construction practicality. This system addresses the challenge of quantifying pressure relief effects in traditional methods and offers theoretical guidance for parameter optimization. The optimal drilling parameters identified are a hole length of 17 m and a spacing of 3.2 m, which effectively controlled the deformation of the surrounding rock when implemented in the field. This innovative technique successfully overcomes the shortcomings of traditional methods in deeply buried hard rock tunnels, greatly broadening the scope of drilling-based pressure relief and providing valuable insights for similar complex tunnel stabilization efforts.

Urban centers along the Alboran-Rif margin face recurring seismic losses, yet city-scale vulnerability mapping often lacks event-informed labels and operational detail. We develop a reproducible framework for Al Hoceima that couples supervised machine learning with a curated inventory of 664 georeferenced buildings damaged during the 2004 and 2016 earthquakes and entries from the Risk-UE program. Seventeen predictors capture the structural, geotechnical, physical, social, and emergency-access conditions. After correlation screening to limit redundancy, four classifiers—Random Forest, XGBoost, Support Vector Machine, and Artificial Neural Network—were trained and validated, and their outputs were aggregated into five vulnerability classes using Jenks natural breaks. All models recover a coherent geography of risk, with very-high and high classes concentrated in the central, eastern, and south-western sectors, and low or safe classes dominant across northern and peripheral belts. Random Forest delivered the strongest performance with accuracy of 0.94, F1-score of 0.943, Kappa of 0.925, and area under the ROC curve of 0.98, while XGBoost performed closely and the remaining models were moderate. Feature-importance analysis identifies population density, distance to the epicenter, and peak ground acceleration as primary drivers, followed by access to fire stations, lithological site effects, and building age. The maps provide decision-grade guidance for retrofit targeting, land-use control, and emergency access planning. Remaining limitations include incomplete building-stock attributes in informal districts, scale and temporal inconsistencies among predictors, label scarcity, and class imbalance. The framework is immediately transferable to data-constrained Mediterranean and North African cities and can be strengthened by harmonized inventories, event-derived labels from UAV and SAR, and multi-hazard integration.

The number of urban road collapse accidents is sharply increasing. In particular, the leakage of underground pipelines often leads to collapse accidents, seriously threatening the operation of a city and the safety of people and property. To evaluate soil instability caused by road collapse induced by underground pipeline leakage, we adopted a systematic research approach of experiment-theory-simulation-verification to elucidate the evolution law of collapse and provide the technical support for prevention and control. A physical model test platform integrated with a synchronous light-pressure-magnetic-electric-mass monitoring system was established to simulate the entire process of road collapse induced by pipeline leakage using key test data, such as soil density, settlement deformation, particle migration, and pore pressure. Based on the test phenomena, a soil mechanical model considering particle skeleton settlement was developed, and the quantitative relationships between the soil density and parameters, including the settlement amount, seepage force, and stiffness coefficient, were derived. The criterion for identifying soil instability was the point where the density of the clay drops to zero under the condition of pipeline leakage causing road collapse. Based on this criterion, the critical deformation amount and critical strain condition of the road were determined. This criterion was integrated to establish a fluid-solid coupling numerical model to enable coordinated computation using fast Lagrangian analysis of continua in 3D, particle flow code in 3D, and a computational fluid dynamics flow field module, thereby achieving a full-process simulation from mesoscopic particle migration to macroscopic collapse failure. Comparative verification in the use of test data, theoretical calculations, and numerical simulation results showed that the maximum error between the theoretical and measured values of soil density during the erosion and infiltration stages was only 5.4%. The numerical model showed high consistency with the physical experiment in terms of macro- and mesoscopic characteristics, such as the formation of cavity-like run-off channels, cavity development, collapse sliding, and particle migration laws. This study clarified the internal mechanism of road collapse induced by pipeline leakage, providing scientific and theoretical methods and technical support for early warning, prediction, prevention, and control projects of urban road collapse, thereby demonstrating significant academic value and engineering application prospects.

The monitoring and prevention of geological hazards constitute a critical component in safeguarding public safety, property protection, and socio-economic stability. Short-term prediction methodologies for rock failure serve as the fundamental prerequisite for precise geological disaster mitigation. The rock deformation and failure process inherently involve energy accumulation, dissipation, and release, with the released signals useful for rupture prediction (including deformation, acoustic, thermal, and optical variations) exhibiting varying energy release ratios. This study proposes a multi-source fusion monitoring approach based on existing technologies: the inflection point method of rock volumetric strain, the multi-parameter trend and threshold method for acoustic emission waveforms, and the infrared radiation thermography time-space statistical analysis with standard deviation integration. Subsequently, an analytic hierarchy process-entropy weight method is established to comprehensively predict rock failure timing. The rock fracture energy is calculated through mechanical parameters derived from elastic solid mechanics theory and integrated prediction time. Acoustic emission localization coupled with infrared thermography enables comprehensive characterization of rock fracturing processes, providing technical support for final rupture pattern prediction. Experimental results demonstrate a 9.91% prediction error for failure timing and 0.902 energy prediction accuracy. The combined application of acoustic emission localization and infrared thermography effectively predicts principal rupture surfaces: granite exhibits typical X-shaped conjugate shear failure, while sandstone displays conventional shear failure. Different lithology has different precursory characteristics. Compared with the conventional crack development model of sandstone, the random micro crack propagation of granite brings greater prediction challenges. These findings validate that the proposed multi-source data fusion methodology offers novel insights into early warning systems in rock engineering, significantly enhancing geological hazard prevention capabilities.