Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks. This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference. By integrating the Bayesian online change-point detection (BOCD) method with a typical dimensionless trend (TDT) model, the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration (OA). Subsequently, a Bayesian approach is employed to estimate the parameters of the inverse velocity (INV) method, enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages. Building on this, the influence of starting point (SP) selection, trend update (TU), and multi-data fusion on prediction reliability is evaluated, and a novel decision criterion for impending slope failure is proposed. The feasibility of the proposed methods is then validated using 73 rock slope failure cases. Results show that using INV data, the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA. In time-to-failure analysis, the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model, along with TU detection and multi-data fusion. The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds.

The time-dependent failure of surrounding rock in deep engineering is essentially controlled by the evolution of microcracks, with the pre-existing fracturing state induced by excavation playing a crucial role in the subsequent time-dependent fracturing process. From the perspective of microcrack development, it is a continuous, dynamic process. Therefore, taking the microcrack propagation process as the fundamental principle, this paper proposes a novel three-dimensional (3D) time-dependent model for hard rock that can depict the entire fracturing process within a unified theoretical framework. This developed model discards the traditional tri-modal partition method based on deformation, and instead adopts an analysis approach centred on time-dependent tensile and shear fracturing. The results show that the time-dependent deformation of hard rock is the macroscopic manifestation of the progressive evolution of microcracks over time. Under true triaxial stress, the growth tendency of cracks in hard rock is orientation-dependent throughout the entire loading process. This developed model provides a mechanical explanation for key time-dependent fracture characteristics observed in true triaxial creep tests, including the anisotropy of time-dependent deformation and the preferred orientation of macroscopic failure plane, and provides a novel framework for elucidating the time-dependent failure process of hard rock.

The ISRM-suggested Brazilian disc (BD) test using split Hopkinson pressure bar (SHPB) for dynamic rock tensile strength requires central crack initiation and stress equilibrium. This study aims to re-evaluate the critical strain rate, ensuring a valid dynamic Brazilian disc test, and to analyse the reliable dynamic tensile behaviour of granite using high-speed digital image correlation (DIC). The comparison between the measured strain obtained through high-speed DIC analysis and the strain gauge allowed for determining the optimal subset parameters used to obtain the real-time deformation field and the stress-strain curve from DIC data. Crack initiation, crack velocity, and failure process are studied to reveal the rate dependence of granites. A unified dynamic increase factor (DIF) model is proposed for the tensile strength of rocks, and the reason for the sudden drop in DIF for high strain rates is discussed. The results reveal that the upper limit of the valid strain rate, which ensures the validity of the ISRM-suggested dynamic BD test, is co-determined by the conditions of stress equilibrium and crack initiation from the centre of the disc. At higher strain rates (75 s^-1), BD test results fail to capture the actual tensile behaviour of rocks, and the potential factors influencing the critical valid strain rate (CVSr), such as sample radius and boundary crack length, should also be considered.

This research introduces a powerful tool, the automatic parametrization of hardening soil (HS) model (APHS), designed to make the HS model parameterization process easier and faster than conventional methods while maintaining high accuracy. Traditional parameterizations rely on oedometer tests, unloading-reloading data, or domain-specific assumptions. Existing optimization-based models often assume uniform parameter weighting, potentially overlooking the distinct sensitivity of each parameter. APHS addresses these limitations as a standalone tool that relies exclusively on conventional triaxial loading test data. To achieve this goal and address the scarcity of labeled datasets, this study integrates numerical modeling with deep learning. The study focuses on a typical shallow Hong Kong soil with parameter ranges derived from field data and relevant literature. Latin hypercube sampling generated diverse parameter values within theoretical bounds for reliable input, while a two-dimensional (2D) axisymmetric finite element model (SIGMA/W) simulated laboratory tests to create a comprehensive, labeled dataset. Seven novel multi-parallel deep long short-term memory (LSTM) networks were trained and validated, achieving an accuracy of 99.4 %. Validation against a conventionally parameterized reference case confirmed 99.6 % accuracy, while an experimental laboratory case study demonstrated strong agreement between simulated and measured results. APHS accelerates HS model parameterization, delivering accurate results in seconds. It can seamlessly integrate with finite element models for automated laboratory data processing and physically informed models to refine calibration parameter ranges. Future work will expand its applicability to various conditions and parameters.

Current in-situ stress determination methods are typically conducted inside a drillhole after its creation. However, the drilling process itself is not utilized for measuring in-situ stress or rock strength, despite being a form of direct mechanical testing on the rock mass. Crucially, drilling contains valuable information about in-situ stress and rock strength, as rocks under high compressive stresses exhibit greater strength. This paper presents a novel in-situ stress determination method, supported by the experimental result of rock drilling monitoring tests using a mine hydraulic-rotary drilling machine. Key drilling parameters-including thrust force, rotation speed, torque and drilling speed-are monitored in real time to determine the drilling specific energy per unit volume of rock. A concave-upward relationship between drilling specific energy and rotation speed is identified, which can characterize rock compressive strength and tensile strength with consistent regularity. Further drilling tests are conducted on the same rock samples under varying confining pressures. Results indicate that as confining pressure increases, the concave-upward curve of drilling specific energy shift upward, reflecting enhanced rock strengths due to confinement. The paper outlines the complete methodology for in-situ stress determination using drilling monitoring techniques, bridging the research gaps among drilling monitoring, rock mechanics, and in-situ stress analysis.

Evaluation of compressive strength in underground lining structures is critical for ensuring structural integrity and safety. Traditional assessment methods are often destructive, time-consuming, and impractical in confined environments such as tunnels and utility corridors. This study introduces an automated, nondestructive approach to visualize and estimate the compressive strength of underground concrete lining using hyperspectral imaging (HSI) combined with deep neural network (DNN) models. High-dimensional spectral data of concrete lining are assembled and trained to develop two DNN-based regression models, namely the Mono-Spectrum Deep Neural Regressor (MS-DNR) and the Segmented-Spectrum Deep Neural Regressor (SegS_DNR). Utilizing the SegS_DNR model, two-dimensional (2D) compressive strength distribution heatmaps were generated for visualization and assessment of strength variations. The SegS_DNR model demonstrated excellent predictive performance, achieving a coefficient of determination () of 0.925 and a Residual Prediction Deviation (RPD) of 5.28 on the testing set for compressive strength estimation. The idea is further validated in site by investigating the capability of identifying the defect regions of the tunnel concrete lining, namely the cracked, spalling, and leaking areas, and demonstrated promising performance in comparison with experienced inspectors on site. This approach offers a contact-free technique for automated structural health monitoring, contributing to safer and more sustainable underground maintenance practices.

The actively-heated fiber-optic (AHFO) method can near-continuously measure soil water content along the AHFO sensors by sensing the temperature variation during an actively heated pulse. Different heating materials, structures, and fiber-optic temperature sensing techniques significantly impact the measurement performance of AHFO sensors. However, there has been no systematic evaluation regarding the measurement performance of soil water content by different AHFO sensors. To address this issue, this study focuses on the measurement performance and monitoring potential of six different AHFO sensors (i.e. actively-heated fiber Bragg grating (AH-FBG) alundum tube, AH-FBG cable, carbon fiber heated cable (CFHC), copper metal heated cable (CMHC), CFHC sensing tube, and CMHC sensing tube). Numerical models were built first for simulating the thermal response process of six AHFO sensors to quantify the measurement accuracy and sensitivity of soil water content. Then, the in situ applications of six AHFO sensors were carried out in Yan'an, China. The numerical and in situ monitoring results indicate that the measurement accuracy and sensitivity of soil water content are both highest by using CFHC sensing tube and CMHC sensing tube. CMHC sensing tube is most suitable for fine and accurate monitoring of in situ soil, while AH-FBG alundum tube and AH-FBG cable are best suited for long-term real-time remote monitoring. In practical applications, it is recommended that geotechnical engineers, when selecting AHFO sensors for a specific site project, should take into account a variety of factors, including measurement performance, spatial resolution, monitoring duration, site installation, and power supply conditions.

The phase-field method (PFM) has emerged as a robust tool for fracture simulation; however, applying this technique to rock materials poses significant challenges, particularly in accurately modeling the propagation of multiple cracks in the presence of complex three-dimensional (3D) mixed-mode loading involving tensile, tensile-shear, and compressive-shear cracks. To address these limitations, this study aims to introduce an enhanced PFM that integrates frictional effects and Lode angle dependence while unifying the volumetric deviatoric (VD) and spectral decomposition (SD) methods. The proposed model incorporates a modified driving force for 3D compressive-shear cracks by embedding a triple shear energy strength (TSES) criterion within the energy decomposition framework. This refinement guarantees that crack behavior remains physically realistic under compression-dominated loading while effectively preserving well-established tensile fracture mechanisms. The validation of the numerical implementation is also conducted through both analytical verification against theoretical solutions and 3D finite element simulations of fissured rock and heterogeneous specimens. Furthermore, numerical case studies demonstrate the model's ability to effectively capture the 3D propagation of multiple cracks and replicate realistic true 3D mechanical responses. The findings present valuable insights and practical guidelines for the application of PFM in rock engineering.

Callovo-Oxfordian (COx) claystone has been selected as the host rock formation for the deep geological disposal of radioactive waste in France, called the Cigéo project. The excavation of drifts in the COx formation induced damage zones with an anisotropic shape, while the stress state around the drifts is almost isotropic. This is due to the anisotropic properties of the host rock formation and the instability caused by the brittle damage. In this study, the mechanical anisotropy of COx claystone was investigated through a triaxial shear test, where the axial stress was maintained while the lateral stress was decreased. Such a method was proposed for simulating one of the possible unloading paths involved during the excavation. The triaxial samples were prepared along different directions based on the angle between the axial loading direction and the one perpendicular to the bedding plane. Results show that the stress-strain curve exhibited an elasto-plastic pattern. With increasing deviatoric stress, a minor decline in Young's modulus E was observed, suggesting progressive damage behaviour. The shear strength changed with increasing the loading angle, showing the anisotropic property of COx claystone. Moreover, the results in this study and collected from other works show a time-dependent behaviour of COx claystone. It is attributed to the coupled effect of creep and pore pressure dissipation inside claystone.

Hazardous geophysical granular flows, such as debris flows and rock avalanches, can exert intense impact forces on obstacles and threaten downstream structures located in their paths. Installing protective structures can mitigate damage, but quantifying their influence on flow evolution and impact loading remains challenging. This study investigates the interactions of granular shock waves (GSWs) generated in front of two cylindrical obstacles with varying spacings through chute experiments and discrete element modeling. Impact pressure sensors were mounted on the upstream surface of each cylinder and on the chute bed to measure dynamic impact pressures in the GSW region. Granular flow velocity and depth were obtained using image processing. Results demonstrate that cylinder spacing significantly influences the geometric characteristics of GSWs. Runup increases with steady-state Froude number (Fr_steady) but decreases as spacing narrows. The granular vacuum length grows with bed slope but decreases significantly with decreasing cylinder spacing. Impact pressures on the cylinders and the chute bed increase linearly with Fr_steady. Low-frequency power spectral density (PSD) is positively correlated with Fr_steady, whereas centroid frequency and pressure impulse counts exhibit low sensitivity to Fr_steady. The dimensionless impact pressure coefficient (α) decreases nonlinearly with increasing Froude number (Fr). At low Fr, α values for dry granular flows are lower than those for debris flows, but the difference diminishes at higher Fr. These findings may improve our understanding of granular flow-obstacle interactions and might help to design protective structures.