Search Results (5,020)

During the long-term operation of salt cavern gas storage, multiple injections and extractions of gas will cause periodic temperature changes in the storage, resulting in thermal fatigue damage to the surrounding rock of the salt cavern and seriously affecting the stability of the storage. This article takes the salt rock samples after thermal fatigue treatment as the research object, adopts a uniaxial compression test, and combines DIC and Acoustic Emission (AE) technology to study the influence of different temperatures and cycle times on the mechanical properties of salt rock. The results indicate that as the number of cycles and upper limit temperature increase, thermal stress induces continuous propagation of microcracks, leading to continuous accumulation of structural damage, enhanced radial deformation, and intensified local displacement concentration, causing salt rock to enter the failure stage earlier. The initial stress for expansion and the volume expansion at the time of failure both show a decreasing trend. After 40 cycles, the compressive strength and elastic modulus decreased by 23.8% and 27.4%, respectively, and the crack failure mode gradually shifted from tension-dominated to tension-shear composite. At the same time, salt rock exhibits typical “elastic-plastic creep” behavior under uniaxial compression, but the uneven expansion and thermal fatigue effects caused by periodic temperature changes suppress plastic slip, resulting in an overall decrease in peak strain energy. The proportion of elastic strain energy increases from 21% to 38%, and the deformation process shows a trend of enhanced elastic dominant characteristics. The changes in the physical and mechanical properties of salt rock under periodic temperature effects revealed by this study can provide an important theoretical basis for the long-term safe operation of underground salt cavern storage facilities. Full article

Conventional unconditional random field (URF) models were shown to neglect in-situ monitoring data and thus misrepresent real slope stability. To address this, a conditional random field (CRF) generator was proposed, in which Fast Fourier Transform (FFT) filtering was coupled with co-Kriging to assimilate site observations. A representative three-bench slope was adopted, and the failure-mode distribution and the statistics of the factor of safety (FoS) produced by the URF, the independent random field (IRF), and the CRF were examined across bedding-dip angles of 15–75° and two cross-correlation states ($\rho$ = −0.2, 0). It was found that eliminating cross-correlation decreased the mean FoS by 0.006, increased its standard deviation by 10.26%, and raised the frequency of low-FoS events from 7.49% to 12.30%. When field constraints were imposed through the CRF, the probability of through-going failure was reduced by 12%, the mean FoS was increased by 0.01, the standard deviation was reduced by 15.38%, and low-FoS events were suppressed to 2.30%. The CRF framework was thus demonstrated to integrate stochastic analysis with field measurements, enabling more realistic reliability assessment and proactive risk management of slopes. Full article

Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including graph convolutional network (GCN), GraphSAGE, and several physics-informed graph attention network (GAT) variants—against conventional artificial neural network (ANN) baselines, using both shape coefficient and energy use intensity (EUI) stratification across three distinct residential districts. Extensive ablation and cross-district generalization experiments reveal that models explicitly incorporating interpretable physical edge features, such as inter-building distance and angular relation, achieve significantly improved prediction accuracy and robustness over standard approaches. Among all models, GraphSAGE demonstrates the best overall performance and generalization capability. At the same time, the effectiveness of specific GAT edge features is found to be district-dependent, reflecting variations in local morphology and spatial logic. Furthermore, explainability analysis shows that the integration of domain-relevant spatial features enhances model interpretability and provides actionable insight for urban retrofit and policy intervention. The results highlight the value of physics-informed GNNs (PINN) as a scalable, transferable, and transparent tool for urban energy modeling, supporting evidence-based decision making in the context of aging residential building upgrades and sustainable urban transformation. Full article

Taking the segmented utility tunnel crossing f5 ground fissures in Xi’an Xingfu forest belt as the research object, this paper investigates the acceleration response and the variation of displacement and stress of the segmented utility tunnel under the El-Centro seismic wave through 3D finite element simulation. The results show that under the orthogonal condition, the peak acceleration of foot wall soil is greater than that of hanging wall soil; conversely, under oblique loading, the hanging wall exhibits higher peak acceleration. In both loading conditions, the peak soil acceleration initially increases and then decreases with depth, while the amplification effect weakens as depth increases. Furthermore, the seismic response and deformation of the tunnel are more pronounced under oblique loading than under orthogonal loading. This study offers quantitative guidance for the seismic design of segmented utility tunnels crossing ground fissures. Full article

To address the challenges of controlling coal roadway roofs with large spans, this study employed theoretical analysis, mechanical modeling, numerical simulation, and field testing to investigate the deformation and failure modes of large-span roadway roofs. An elastic foundation beam model for roof deformation under area support was established. The critical time-effective roof control principle and technology based on area support were proposed. Numerical simulation research and field trials of critical time-effective roof control using area support were conducted in the tailgate (open-off cut) of Panel II513 at the Huaibei Shuanglong Coal Mine. The results indicate that roof stability decreases with decreasing rock beam thickness and increasing span. The deformation and failure modes of large-span roadway roofs include anchor failure within the anchored zone and delamination outside the anchored zone. Case studies based on the mechanical model, numerical simulations, and field tests all demonstrate that the critical time-effective roof control technology utilizing hydraulic support for area support can significantly reduce roof deflection deformation. Full article

The deep mining of coal resources can lead to surface subsidence, thereby damaging the stability of buildings on the surface; however, this issue can be effectively mitigated through goaf backfilling with coal gangue and other solid wastes. In this study, the technical principles of goaf solid waste backfill mining are illustrated, and the surface movement and deformation patterns under different burial depths and compression rates are examined. Additionally, the monitoring and analysis of the roof subsidence and surface building conditions after goaf solid waste backfill mining are described. The results show that as the burial depth and compression rate increase, the maximum values of the surface subsidence and horizontal deformation gradually decrease. When the burial depth is between 800 and 1200 m, the design value of the compression rate for the goaf should be higher than 79%. The actual measurement results show that the maximum subsidence value of the roof after goaf backfilling is 635 mm, and the compression rate is about 80.8%, which is consistent with the theoretical design value. Hence, surface buildings are little affected by mining activities and are within the safe range for use. Full article

Moisture in porous building materials significantly affects all their technical parameters. For this reason, it is important to accurately determine coefficients that describe moisture transport inside these materials. The main parameters concerning the hygroscopic range are as follows: water vapor permeability δ, water vapor resistance factor μ, and water vapor diffusion coefficient D. Autoclaved aerated concrete (AAC), one of the most popular materials used for the construction of external walls, was tested. The study focused on the four density classes: 400, 500, 600, and 700. Using a modified cup method, measurements of the corresponding coefficients δ, μ, D were carried out in six ranges of relative air humidity: 11–30, 30–54, 54–60, 60–75, 75–85, 85–98%. The results prove that not only the level of humidity tested, but also the structure within the same material group has a significant impact on all parameters, strongly differentiating their values. In this regard, precise numerical simulations concerning moisture transport processes in autoclaved aerated concrete must take into account both its density class and the moisture range in which these processes occur. Full article

To reveal the fracture mechanism of overburden aquifers during mining under anticlinal structural zones in western mining areas, this study takes Panel 1309 of the Guojiahe Coal Mine as the engineering background and employs field investigations, physical similarity simulation, and numerical simulation methods to systematically investigate the overburden fracture and crack evolution laws during extra-thick coal seam mining in anticlinal zones. The research results demonstrate the following: (1) The large slope angle of the anticlinal zone and significant elevation difference between slope initiation points and the axis constitute the primary causes of water inrush-induced support failures in working face 1309. The conglomerate of the Yijun Formation serves as the critical aquifer responsible for water inrush, while the coarse sandstone in the Anding Formation acts as the key aquiclude. (2) Influenced by the slope angle, both overburden fractures and maximum bed separation zones during rise mining predominantly develop toward the goaf side. The water-conducting fracture zone initially extends in the advance direction, when its width is greater than its height, and changes to a height greater than its width when the key aquifer fractures and connects to the main aquifer. (3) The height of the collapse zone of the working face is 65 m, and the distribution of broken rock blocks in the collapse zone is disordered; after the fracture of the water-insulating key layer, the upper rock layer is synchronously fractured and activated, and the water-conducting fissure leads to the water-conducting layer of the Yijun Formation. (4) Compared to the periodic ruptures of the main roof, the number of fractures and their propagation speed are greater during the initial ruptures of each stratum. Notably, the key aquiclude’s fracture triggers synchronous collapse of overlying strata, generating the most extensive and rapidly developing fracture networks. (5) The fracture surface on the mining face side and the overlying strata separation zone jointly form a “saddle-shaped” high-porosity area, whose distribution range shows a positive correlation with the working face advance distance. During the mining process, the porosity variation in the key aquiclude undergoes three distinct phases with advancing distance: first remaining stable, then increasing, and finally decreasing, with porosity reaching its peak when the key stratum fractures upon attaining its ultimate caving interval. Full article

This review article synthesizes recent experimental research on the breaching of noncohesive embankment dams and levees caused by overflow, with a specific focus on coarse-grained soil materials. Despite the high incidence of embankment dam collapses leading to significant socio-economic and environmental impacts, comprehensive understanding of the underlying physical processes remains incomplete. Historically, studies have largely concentrated on embankments made from uniform materials ranging from fine cohesive soils to noncohesive clean rockfill. However, recent shifts in focus to well-graded heterogeneous coarse-grained soil materials underscore the complexity of predicting breach mechanics, given the absence of physically based models for these materials. This review aims to compile and elucidate the factors affecting breaching in an effort to inform future research and practical applications in dam safety assessments. Full article

Traditional methods for predicting surrounding rock grades ahead of tunnel faces encounter challenges: image-based approaches are susceptible to environmental interference, while parameter-based classification may disrupt construction. This study proposes an intelligent rock grade identification method by integrating longitudinal displacement profile (LDP) evolution patterns with deep learning. First, the numerical model was validated against V-D theoretical curves, and LDP evolution laws were systematically analyzed for three rock types (GSI = 15, 30, 50) under nine geological combinations. The results indicate that (1) homogeneous strata exhibit deformation peaks followed by declines; (2) GSI = 15 strata show significantly larger deformations; and (3) stratified schemes display pre-interface deformation peaks and post-interface deformation controlled by subsequent lithology. A novel hybrid neural network was developed to classify strata using LDP curves as input. The model achieved 93.25% training accuracy and 91.20% validation accuracy. Ablation experiments demonstrated their superiority over the other four models with partial module deletions, achieving improvements in test accuracy of 3.24%, 3.08%, 4.16%, and 6.48%, respectively, compared to those models. This lightweight solution effectively overcomes the limitations of manual expertise dependency in conventional models and environmental sensitivity in visual methods. By synergizing LDP evolution analysis with deep learning, this framework provides a reliable approach for real-time rock grade prediction during tunnel advancement. Full article

With the increasing depth of coal resource extraction, the creep characteristics of gangue backfill in deep backfill mining are crucial for the long-term deformation of rock strata. Existing research predominantly focuses on the instantaneous deformation response of either the backfill alone or the strata movement, lacking systematic studies that reflect the long-term time-dependent deformation characteristics of the strata-backfill system. This study addresses gangue–roof composite specimens with varying gangue particle sizes. Utilizing physical similarity ratio theory, graded loading confined compression creep experiments were designed and conducted to investigate the effects of gangue particle size and moisture content on the creep behavior of the gangue–roof composites. A fractional-order creep constitutive model for the gangue–roof composite was established, and its parameters were identified. The results indicate the following: (1) The creep of the gangue–roof composite exhibits two-stage characteristics (initial and steady-state). Instantaneous strain decreases with increasing particle size but increases with higher moisture content. Specimens reached their maximum instantaneous strain under the fourth-level loading, with values of 0.358 at a gangue particle size of 10 mm and 0.492 at a moisture content of 4.51%. (2) The fractional-order creep model demonstrated a goodness-of-fit exceeding 0.98. The elastic modulus and fractional-order coefficient showed nonlinear growth with increasing particle size, revealing the mechanism of viscoplastic attenuation in the gangue–roof composite. The findings provide theoretical support for predicting the time-dependent deformation of roofs in deep backfill mining. Full article

The objective of the research was to use sigmoidal mathematical models for the planning and control of rigid pavement works. A dataset was constructed using 140 technical files, which were then analyzed to extract the valued work schedules. These schedules contained the variables time and cost per month. Subsequently, two groups were created from the dataset: a training group comprising 80% of the data and a test group comprising the remaining 20%. Subsequently, the variables were normalized and adjusted with the proposed logistic, Von Bertalanffy, and Gompertz models using Python 3.11.13. Following the implementation of training and validation procedures, the logistic model was identified as the optimal fit, as indicated by the following metrics: R² = 0.9848, MSE = 0.0026, RMSE = 0.0506, and MAE = 0.0278. The implementation of the aforementioned model facilitates the establishment of an early warning system with a high degree of effectiveness. This system enables the evaluation of the discrepancy between the actual progress and the planned progress with an R² greater than 98%, thereby serving as a robust instrument for the adjustment and revalidation of activities before and following their execution. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

Concrete is an essential material in the construction industry due to its strength and versatility. However, its quality can be compromised by environmental factors during its fresh and early-age states. To address this vulnerability, various sensors have been implemented to monitor critical parameters. While high-precision sensors (e.g., piezoelectric and fiber optic) offer accurate measurements, their cost and fragility limit their widespread use in construction environments. In response, this study proposes a cost-effective, Arduino-based wireless monitoring system to track temperature and humidity in fresh and early-age concrete elements. The system was validated through laboratory tests on cylindrical specimens and industrial applications on self-compacting concrete New Jersey barriers. The sensors recorded temperature variations between 15 °C and 35 °C and relative humidity from 100% down to 45%, depending on environmental exposure. In situ monitoring confirmed the system’s ability to detect thermal gradients and evaporation dynamics during curing. Additionally, the presence of embedded sensors caused a tensile strength reduction of up to 37.5% in small specimens, highlighting the importance of sensor placement. The proposed solution demonstrates potential for improving quality control and curing management in precast concrete production with low-cost devices. Full article

Thermal stimulation represents an effective method for enhancing reservoir permeability, thereby improving geothermal energy recovery in Enhanced Geothermal Systems (EGS). The phase-field method (PFM) has been widely adopted for its proven capability in modeling the fracture behavior of brittle solids. Consequently, a coupled thermo-mechanical phase-field model (TM-PFM) was developed in COMSOL 6.2 Multiphysics to probe thermal fracturing mechanisms in reservoir rocks. The TM-PFM was validated against the analytical solutions for the temperature and stress fields under steady-state heat conduction in a thin-walled cylinder, three-point bending tests, and thermal shock tests. Subsequently, two distinct thermal fracturing modes in reservoir rock under high-temperature conditions were investigated: (i) fracture initiation driven by sharp temperature gradients during instantaneous thermal shocks, and (ii) crack propagation resulting from heterogeneous thermal expansion of constituent minerals. The proposed TM-PFM has been validated through systematic comparison between the simulation results and the corresponding experimental data, thereby demonstrating its capability to accurately simulate thermal fracturing. These findings provide mechanistic insights for optimizing geothermal energy extraction in EGS. Full article