Search Results (2,563)

This paper addresses the important issue of the proper management and protection of subterranean monuments. It concerns the analysis and decoding of the microclimate that is created in heritage structures, which are structures located beneath the soil or carved into rock. The aim of this study is to understand the hygrothermal processes occurring in the mass of underground structural elements, such as evaporation, condensation, water content, and heat fluxes, based on the principles of building physics. The methodology used is the following: a systematic literature review on the topic, an overview of the factors affecting the microclimate, the assessment methodology, and the simulation tools used to decode and evaluate microclimate in subterranean heritage structures; a discussion of the current gaps; and finally, a proposal for future directions for research. A review of the literature reveals that researchers worldwide have employed similar methodologies to approach this complex issue. Recordings and analyses of the microclimate inside underground monuments lead to decision-making and the formulation of actions for optimal preservation. Due to the large number of parameters involved in microclimate analysis, computer software for numerical simulation has been used in many cases. Following the review of the relevant literature in the field of study, a critical discussion concludes by proposing directions for future research on this important topic. Basic results of this research identify current gaps, problems, and limitations. These include technical and practical issues or gaps concerning lack of data for material properties and weather conditions. Another significant limitation arises from the complexity of physical interactions, as well as from the human factor, which involves the proper use of the simulation program and the correct interpretation of the calculation results. This study demonstrates that the microclimate of subterranean heritage structures is the result of complex interactions between climate, geology, architectural design, material properties, and human use. Across different geographical and cultural contexts, subterranean monuments exhibit distinct microclimatic behaviors. The comparative analysis of case studies highlights that while subterranean environments generally benefit from thermal stability, they remain highly vulnerable to moisture dynamics, ventilation changes, and external climatic coupling. Hence, there is a necessity for context-specific approaches rather than generalized conservation solutions. Decoding subterranean microclimates requires a multidisciplinary framework that combines environmental monitoring, material indicators, architectural analysis, and numerical modeling. Full article

Column-shaped unstable rocks are commonly developed in canyons and karst regions, and their instability is strongly affected by the mechanical characteristics of the underlying base. In this study, the Zengziyan W12# unstable rock and the Wangxia W2 unstable rock in Chongqing, China, were selected as representative cases of a soft–hard interlayered base and a homogeneous weak base, respectively. Finite element models were established to compare the distributions of equivalent stress, shear stress, and displacement under self-weight loading. The results show that, in the soft–hard interlayered base, stress is mainly concentrated in the hard layers, and the high shear-stress zones are segmented by these interlayers. The instability-controlling location tends to shift upward to the degraded zone at the bottom of the unstable rock mass, indicating a fracture–collapse tendency. In contrast, the homogeneous weak base shows more continuous stress transmission and shear-stress development, which is favorable for the formation of a through-going shear zone within the base. These results indicate that the base structure type regulates the stress transfer path, shear-zone continuity, and instability-controlling location of column-shaped unstable rocks, thereby providing a mechanical basis for stability assessment and targeted prevention. Full article

This paper investigates the mechanical characteristics of rockbolt under combined tensile–shear loading conditions. By studying the stress and deformation throughout the elastic and plastic stages of rockbolt, a failure model for rockbolt under different tensile–shear combination loadings was established. Key parameters, including the maximum bending moment M_A and total plastic deformation λ, were identified and quantified as they evolve with changes in the displacement angle (combined tensile–shear state). The main novelty lies in formulating the key control parameters governing the elastic–plastic transition and failure process of rockbolts under combined tensile–shear loading and further incorporating them into FLAC^2D to improve the simulation of tensile–shear failure of rockbolts. Numerical simulations of rockbolts under combined tensile–shear loading were performed using FLAC^2D. The influence of a rock mass’ Young’s modulus and uniaxial compressive strength on the mechanical response of the rockbolt was investigated. The results indicate that the ultimate load-carrying capacity of the rockbolt remains essentially constant as the displacement angle increases, while the axial tensile force gradually decreases and the shear force gradually increases. The influence of a rock mass’ Young’s modulus on the stress–strain characteristics of the anchor exhibits a nonlinear positive correlation. When the uniaxial compressive strength of the rock mass is low, the rockbolt is prone to slippage during loading. Full article

Bench slopes in open-pit mines are highly susceptible to progressive deformation and instability due to the coupled effects of excavation disturbance, rock mass weathering, and extreme rainfall, posing significant challenges to rapid risk assessment and engineering decision-making. To address the limitations of conventional methods in efficiency and adaptability under complex multi-factor conditions, this study proposes a hybrid simulation–artificial intelligence framework for rapid slope stability assessment and bench face angle optimization. Multi-scenario numerical simulations were conducted by integrating geological investigation data, laboratory and in situ mechanical parameters, and extreme rainfall conditions to characterize slope deformation and failure mechanisms and generate a dataset for machine learning model training. Machine learning models were trained using slope height, bench face angle, unit weight, cohesion, and friction angle as inputs, and safety factors under natural and extreme rainfall conditions as outputs, with hyperparameters optimized by Bayesian optimization. The results indicate that highly weathered rock masses dominate shallow deformation and act as critical weak zones, while extreme rainfall significantly accelerates instability evolution and reduces slope safety factors. Among the RF, SVR, and ELM models, the Bayesian-optimized support vector regression (BO-SVR) exhibits the best predictive performance (R² > 0.98). SHapley Additive exPlanations (SHAP) analysis reveals that slope height and shear strength parameters are the dominant controlling factors, whereas unit weight has a relatively limited influence. Validation using real landslide cases shows good agreement with numerical simulations, confirming the reliability of the proposed framework. The developed approach enables rapid risk evaluation and supports bench face angle optimization, providing an effective tool for intelligent slope management in open-pit mining. Full article

Understanding the anisotropy in the physical and mechanical properties of layered rocks is essential for predicting and preventing instability in layered rock masses. However, in-situ sampling is often hindered by the difficulty of obtaining specimens with controlled bedding orientations. Cement-based 3D printing (3DP) offers an efficient approach for fabricating rock analogues, yet the inherent anisotropy induced by the layer-by-layer deposition process has not been well characterized, hindering its broader application. The objectives of this study are (i) to systematically evaluate the intrinsic anisotropy of cement-based 3DP rocks and (ii) to compare the mechanical anisotropy and failure modes of 3DP layered rocks with those of natural layered sandstone. The key findings are as follows: (1) The uniaxial compressive strength (UCS), P-wave velocity, and computed tomography (CT) number of the 3DP rock vary by less than 6% among the X-, Y-, and Z-directions, indicating lower intrinsic anisotropy compared to typical sandstones and several other natural rocks. (2) The UCS, elastic modulus, and secant modulus of the 3DP layered rocks all decrease initially and then increase with bedding dip angle, reaching a minimum at 60°. (3) The main fracture characteristics of the 3DP layered rocks are similar to those of layered sandstone; notably, the 3DP layered soft rock exhibits the most pronounced shear failure features. This study quantifies the low intrinsic anisotropy of cement-based 3DP rocks and validates their similarity to natural layered sandstone in both mechanical anisotropy and failure modes. It thereby provides a reliable, reproducible basis for physical modeling of layered rock masses using 3DP, offering a new approach for laboratory-scale investigations of layered rocks. Full article

Magmatic–hydrothermal systems transport heat through coupled conduction and buoyancy-driven fluid flow in porous rock, behavior conventionally modeled with grid-based finite-difference simulators such as HYDROTHERM. We demonstrate that a physics-informed neural network (PINN), built on the NVIDIA PhysicsNeMo framework using automatic differentiation and mesh-free collocation, can produce a stable two-dimensional time-dependent solution for a magma-chamber configuration based on the Rio Pisco pluton in the Peruvian Coastal Batholith. Boundary conditions and material parameters are taken from a prior HYDROTHERM study of the same pluton, and 28 temperature samples digitized from that study are used as a supervised constraint. The PINN couples Fourier conduction, advective heat transport, Darcy flow with a temperature-dependent permeability law, and a mass-conservation formulation; the mass-conservation equation is written in two-phase form, but in the regime studied here, the simulation remains below the boiling curve, so the steam-phase saturation stays at zero and the formulation reduces to its single-phase liquid–water limit. The network reproduces the conductive temperature gradient and a directionally consistent buoyancy-driven flow field, with weaker and less organized circulation than the reference simulation, and a cooling time of approximately $1.6\times {10}^5$ years, comparable to the ∼175,000 years reported for the matching $k={10}^{-16}{\mathrm{m}}^2$ HYDROTHERM reference scenario from which the supervised training data was digitized. We discuss the conditions under which the mesh-free, automatically differentiable PINN approach offers a useful alternative to grid-based solvers. Full article

To investigate the load-bearing characteristics of lightweight surface-mounted slewing cable anchorage, this paper takes the Yellow River Three Gorges Bridge project as an example, establishing a nonlinear finite element model and verifying its effectiveness through a 1:100 scale physical model test. Furthermore, a theoretical stability analysis model was established to quantify the contributions of base friction and toothed block clamping action. By analyzing displacement behavior, rock mass shear characteristics, and plastic zone evolution, the combined load-bearing mechanism was revealed. The results show that the anchorage system begins to destabilize when the load reaches 18P. Both numerical and theoretical analyses confirm that the toothed blocks significantly improve the stability of the anchorage system; the safety factor increases from 6.84 considering only friction to 16.59 considering clamping action, which is consistent with the 17P plastic threshold observed in the simulation. Rock mass resistance is generated from bottom to top, providing passive resistance through shear action. The final determined failure mode is the interconnection of local plastic zones and the overturning failure of the anchorage system. Full article

This study investigates vertical surface displacements in an area previously impacted by extensive underground hard coal extraction, specifically focusing on the closed “Kazimierz-Juliusz” mine in the Upper Silesian Coal Basin (Poland). The cessation of mining operations and formal decommissioning do not necessarily signify the termination of ground instability; rather, the discontinuation of mine water pumping triggers a progressive groundwater rebound within the rock mass. This hydrogeological shift leads to a redistribution of stresses in the geological structure, inducing deformation processes that manifest as surface uplift. This research aims to characterize the temporal evolution and magnitude of post-closure surface elevation changes by integrating satellite radar interferometry with conventional geodetic surveys. The analysis, spanning a 28-month observation period, utilizes both Persistent Scatterer Interferometry (PSInSAR) and European Ground Motion Service (EGMS) data, complemented by precise geometric leveling. The results reveal a low-magnitude deformation process, with detected uplift rates reaching approximately 1 cm/year. The synergistic integration of InSAR-based monitoring and classical geodesy allowed for robust cross-validation, significantly enhancing the reliability of the findings both qualitatively and quantitatively. Full article

Rockfall events are significant natural hazards on fractured rock cliffs, often driven by environmental forcing, including thermal variations that induce stress and fatigue in rocks. This study presents the first application of Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) for high-resolution monitoring of sub-millimeter thermally induced displacements on a rock slope. An eight-day pilot experiment conducted at the La Cornalle molasse cliff (Vaud, Switzerland) revealed cyclic displacement signals with a clear 24 h periodicity, identified through Fourier and wavelet analyses, with a mean amplitude of 5 × 10⁻⁴ m. Simultaneously, infrared thermography (IRT) and a weather station recorded rock surface and air temperature variations, allowing a first estimation of the time lag between thermal forcing and mechanical response, with delays of 1–8 h relative to air temperature and 1–6 h relative to solar radiation. An analytical deformation model based on thermal diffusion predicts a daily displacement amplitude of 4.2 × 10⁻⁵ m, highlighting a significant difference with GB-InSAR observations and emphasizing the influence of structural complexity and thermo-hydro-mechanical processes in rock slopes. These results demonstrate the capability of combined high-resolution remote sensing techniques to quantify thermo-mechanical behavior in rock masses and provide a methodological framework for future investigations of rockfall-prone slopes. Full article

Tunnel construction requires accurate and timely classification of surrounding rock masses to ensure safety and guide excavation. This research addresses the limitations of conventional methods and unimodal intelligent approaches by proposing a novel hybrid deep model, Random-Mamba, that integrates drilling parameters and digital images for enhanced classification performance. A dataset of 3361 synchronized samples was constructed, containing six drilling parameters, digital face images, and expert-classified rock mass grades. The model employs a dual-branch architecture: a Random Forest processes the drilling parameters, and a MambaVision network extracts visual features, with a multilayer perceptron performing the fusion. The proposed model achieved an overall accuracy of 92.12% and a macro-F1 score of 91.66%, outperforming the most comparable hybrid model by 2.61% in accuracy. It demonstrated particularly high precision in identifying Class III rock with an F_1-score of 93.2%. Ablation and comparative experiments confirmed its superiority over both single-modality models, such as SVM and ResNet, and other hybrid architectures, like Random-Swin. SHAP-based sensitivity analysis further revealed that feed speed was the most influential drilling parameter for classification. The effective fusion of complementary mechanical and visual data provides a robust and practical solution for real-time rock mass assessment in tunneling engineering. Full article

Oil and gas reservoirs have been discovered in various strata within the Kekeya structural belt of the Tarim Basin, primarily consisting of pure gas reservoirs, condensate gas fields, and oil reservoirs. There are significant differences in the physicochemical properties of the oil and gas. 17α(H)-rearranged hopanes are commonly found in crude oil and may be key indicators for determining oil and gas migration pathways and correlating oil sources in the study area. Through chromatographic and mass spectrometric analyses of crude oil samples from five different strata in the Kekeya structural belt, the distribution patterns of 17α(H)-rearranged hopanes and 18α(H)-neohopanes were investigated. A comparative analysis of source rocks and crude oil confirmed that 17α(H)-rearranged hopanes originate from the Permian Pusige Formation source rocks. Further analysis of crude oil maturity and oil and gas migration revealed that low to moderate levels of 17α(H)-rearranged hopanes are influenced by maturity, whereas abnormally high levels are associated with oil and gas migration. Therefore, detecting the distribution characteristics of 17α(H)-rearranged hopanes in crude oil provides crucial evidence for determining the source and migration pathways of oil and gas in the Kekeya area. Full article

Early-stage landslides and rockfalls are often characterized by very small internal accelerations associated with creep and progressive deformation, which are difficult to capture using conventional surface-based displacement monitoring techniques. To address this, the study presents the design and laboratory validation of a prototype in-mass inertial monitoring device, referred to as a Smart Rock, intended for embedded monitoring of rock mass motion. The developed device integrates low-noise inertial measurements with on-board processing to enable real-time characterization of motion signatures within a moving mass. Two sensing configurations, including a low-noise accelerometer-only configuration and a full inertial measurement unit (IMU) configuration, were implemented to evaluate their relative performance for in-mass motion monitoring. Embedded signal processing approaches suitable for landslide motions were developed to identify quasi-static, step-change, and impact-related motion regimes. Laboratory experiments using a controlled robotic testbed generated repeatable motion scenarios representative of creep-like movement, abrupt displacement changes, and impact events. Results showed that Smart Rock resolved very low-magnitude acceleration signatures on the order of 10⁻⁵ g and distinguished these from higher-energy motion and impact events, with improved signal stability observed for IMU-based configurations. These findings demonstrated the feasibility of in-mass inertial devices for characterizing landslide and rockfall motion in geotechnical applications. These results should be interpreted as proof-of-concept laboratory validation under controlled conditions. Full article

The extraction of fracture parameters and the classification of surrounding strata are crucial criteria for assessing the stability of a tunnel face. To overcome the limitations of conventional manual sketching, this paper proposes a tunnel face fracture identification, extraction, and surrounding strata classification technique based on deep learning technology. Based on the collection of on-site tunnel face images, we construct a comprehensive database comprising 20,000 dataset samples. By refining the conventional UNet deep learning network model and incorporating the channel and spatial attention modules (Convolutional Block Attention Module, CBAM), we achieve automated identification of fracture traces on the tunnel face, yielding remarkable recognition outcomes. Through training and testing the CBAM-UNet network model on this extensive database, we conduct a comparative analysis with alternative deep learning approaches and conventional edge detection algorithms. The results unequivocally demonstrate the exceptional performance of the CBAM-UNet model in fracture recognition. Subsequently, we conduct statistical analysis and grouping of the identified fractures, as well as calculate the integrity indices of the surrounding rock mass. This enables the expeditious assessment of the tunnel face’s surrounding rock grade. Full article

To investigate the slope stability of the north bank anchorage of the Yellow River Three Gorges Bridge during foundation pit excavation and operational stages, a true three-dimensional geological model was established using Rhino6 and numerical simulations were performed using FLAC3D7.0, supplemented by stereographic projection kinematic analysis and the shear strength reduction (SSR) method. Systematic simulations were conducted for foundation pit excavation, main cable load application, heavy rainfall, and two seismic loading conditions, and the deformation characteristics and plastic zone evolution patterns of the slope under different conditions were analyzed. The stereographic projection kinematic analysis indicates that the dominant discontinuity sets do not constitute kinematically admissible planar sliding, wedge sliding, or toppling failure modes, confirming the validity of adopting a continuum model. The numerical simulation results show that the maximum slope displacement after foundation pit excavation is 13.13 mm, with the plastic zone exhibiting a discontinuous scattered distribution, and the slope is overall stable. After the application of the main cable load, the maximum displacement decreases to 7.86 mm; the counterweight effect of the anchorage self-weight significantly improves the deep stability, while the horizontal cable force generates a wedge-shaped shear plastic zone at the slope toe. Under heavy rainfall conditions, rock mass saturation leads to an increase in the maximum displacement to 11.76 mm with expanded plastic zone volume, where the deterioration of strength parameters and the increase in pore water pressure are the primary causes of reduced stability. Under seismic conditions, the maximum displacements under the natural and artificial seismic waves are 15.83 mm and 17.29 mm, respectively, exhibiting a significant elevation amplification effect with extensive plastic zone development in the shallow surface layer. The shear strength reduction analysis yields factors of safety of 2.4 and 2.27 for the heavy rainfall and seismic conditions, respectively, both significantly exceeding the code requirements, demonstrating that the slope possesses an adequate safety margin under extreme loading conditions. Full article

Aiming to address the challenge of the high-precision monitoring of underground coal and rock fractures, this paper proposes and verifies a roadway full-section synchronous monitoring method utilizing a Wi-Fi wireless sensor network. To address the inherent difficulties of detecting complex rock mass fractures through surface sensors, our methodology employs a synchronized array of surface-mounted vibration sensors covering key mechanical structural points. The feasibility of this approach is technically substantiated through the strict implementation of rigid coupling techniques—utilizing industrial-grade epoxy resin and customized metal mechanical fixtures—combined with hardware low-pass filtering to eliminate air gap attenuation and maximize the signal-to-noise ratio. Using this validated setup, we successfully extracted and manually verified 63 high-fidelity rupture events. The data reliability is further demonstrated through a comprehensive Python-based processing pipeline that calculates 17-dimensional time–frequency characteristics. Statistical analysis confirms that the extracted data strictly conforms to the physical laws of rock fracture, evidenced by a significant negative correlation between maximum amplitude and dominant frequency (r = −0.84, p < 0.001). Unsupervised clustering of these signals reveals excellent inter-class separability. By transparently substantiating the data acquisition and verification process, this study provides a publicly shared pilot dataset and methodology for algorithm evaluation and preliminary dynamic disaster mechanism exploration. Full article