Search Results (217)

The stability of underground building foundations in fractured rock masses is a critical concern in geotechnical engineering, particularly for urban projects situated in complex geological settings. In such environments, the interaction between weak planes, groundwater seepage, and in situ stress plays a decisive role in controlling deformation and failure mechanisms. This study presents a novel weak plane–seepage–stress coupling model specifically developed to evaluate the stability of underground excavations and foundation walls under these challenging conditions. Unlike conventional approaches that often assume isotropy or consider isolated factors, the proposed model integrates multiple interacting variables—including weak plane orientation, seepage coefficient, and excavation direction—to systematically assess their combined influence on stress redistribution and failure pressure. A key innovation lies in the quantitative evaluation of the permeability-sealing coefficient, which reflects the effectiveness of waterproofing measures, and its coupling with weak plane characteristics. The results demonstrate that weak planes significantly alter the surrounding stress field, inducing directional instability. The optimal excavation orientation for minimizing instability is identified within the range of 200° to 280°. Moreover, increasing δ from 0 to 1 leads to a substantial reduction in the required supporting pressure, underscoring the critical role of effective sealing and waterproofing in enhancing foundation stability. While the current model is based on a single weak plane assumption and focuses on short-term mechanical responses, it provides a foundational framework for understanding coupled instability mechanisms. Future work will extend the model to incorporate multi-set weak planes, time-dependent degradation, and dynamic excavation processes. This research offers both theoretical insights and practical guidance for optimizing geotechnical design in fractured rock environments, contributing to more resilient and sustainable underground construction. Full article

Human engineering activity, such as cross-regional transportation construction, often disturbs the geological environment and triggers landslides. This study investigated a landslide induced by tunnel excavation in the northeastern region of the Qinghai–Tibet Plateau, exploring how a seemingly low-risk local small-scale landslide can trigger an engineering disaster. Based on field geological and geomorphological surveys, unmanned aerial vehicle (UAV) remote sensing photography, and SBAS-InSAR data analysis (time-series monitoring from 2021 to 2023), the spatiotemporal evolution patterns and causative mechanisms of landslide deformation were systematically elucidated. The results indicate the following: (1) The landslide evolved from initial multiple small local slides, gradually expanding and connecting to form a larger and deep-seated landslide. (2) SBAS-InSAR analysis revealed that the landslide deformation rate ranged from −38.13 to 12.01 mm/a, with a maximum cumulative deformation of 121.91 mm. Substantial deformation was concentrated in April–June 2021, June–August 2022, and April–July 2023. Spatially, the deformation intensity exhibited a pattern of middle section > front > rear, with greater deformation closer to the tunnel construction point. (3) The landslide deformation is primarily related to tunnel construction disturbance. The topography, geological structure, and frozen ground thawing exerted certain influences. The deformation mechanism is summarized as follows: Slope toe excavation initially triggers local sliding, leading to tension cracking at the rear edge. Subsequently, tunnel construction further promotes landslide expansion, resulting in the formation of a deep-seated landslide. This study showed that the landslide resulted from the combined effects of engineering activity and natural conditions. The results reveal that, under disturbances from inappropriate engineering activities, local small landslides may develop into major disasters. Therefore, the construction plan for the tunnel must be revised to mitigate such risks. Full article

In underground construction, the drilling and blasting method is widely used due to its advantages, such as low cost, simple implementation, and applicability under various geological and hydrogeological conditions. One parameter that significantly affects the effectiveness of drilling and blasting is the post-blast tunnel cross-sectional area. In this study, multiple linear regression analysis (MLRA) and multiple nonlinear regression (MNLR) models were used to predict the area of a tunnel face after blasting, utilizing 136 datasets containing parameters measured from the tunnel face area after blasting during the Deo Ca tunnel construction project. Three deep learning models, an artificial neural network (ANN) and two hybrid models combining an ANN with the particle swarm optimization (PSO) algorithm and an ANN with a genetic algorithm (GA), were then developed to predict the tunnel face area after blasting. The input variables for the calculation and prediction models included the designed tunnel face area (S_d), the specific charge (SC) of the explosion, the average borehole length (L), and the rock mass rating (RMR) of the rock mass on the tunnel face. The GA-ANN model’s results, including determination coefficient (R²) and mean square error (MSE) values of R²_train = 0.9562, R²_testing = 0.94, MSE_training = 0.0156, and MSE_testing = 0.0302, indicate that it provides a better prediction of the tunnel face area after blasting than the other models. Full article

Reasonable adjustment of construction parameters is of great value to reduce surface settlement and ensure the safety of shield construction. A novel hybrid intelligent optimization framework based on combination weighting and gray correlation analysis methods (CWG), a long short-term memory (LSTM) model and a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (CPSOS) algorithm was proposed. The CWG method was employed to screen key construction parameters and determine the weights of various influencing factors of surface settlement, thereby constructing a CWG-LSTM prediction model for surface settlement. On this basis, the prediction model served as the objective function for optimizing construction parameters, and the CPSOS algorithm was used for the optimization of shield construction parameters. Based on the Qingdao Metro Line 4 in China, sample sets were collected to verify the performance of the developed framework. The CWG-LSTM model achieved coefficients of determination (R²) of 0.92 and 0.91 on the train and test sets, respectively, along with root mean square errors (RMSE) of 1.29 and 1.03, and mean absolute percentage errors (MAPE) of 15.60% and 17.18%. Its prediction ability surpasses that of other comparison models, such as the Gated Recurrent Unit, Random Forest, Transformer, and Multiple Linear Regression, demonstrating higher accuracy. Optimized construction parameters derived from the CWG-LSTM-CPSOS facilitated shield tunneling in the unconstructed section. All surface settlement monitoring results recorded during excavation fell within the safety threshold, demonstrating that the proposed hybrid intelligent optimization framework effectively manages surface settlement during shield tunneling and serves as a reliable optimization approach for construction parameters. Full article

The carved lacquer horse-hoof-shaped box excavated from Yulin, Shaanxi Province, represents a typical example of lacquerware preservation in the arid environment of northern China, exhibiting multiple deterioration phenomena, including substrate deformation, lacquer film peeling, and pigment fading. To systematically analyze its structural composition and craftsmanship features, this study employed multiple analytical techniques, including ultra-depth microscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), confocal laser micro-Raman spectroscopy (Raman), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Based on these analyses, a targeted conservation protocol was developed. Results revealed that the carved lacquer horse-hoof-shaped box has a wooden substrate structure, with the lacquer ash layer composed of mixed materials, including calcium carbonate (CaCO₃), quartz (SiO₂), and hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂). The lacquer film layer contains Chinese lacquer and plant oils, with cinnabar applied as surface decoration. Based on these findings, a stratified reinforcement conservation strategy was proposed: under dynamic monitoring with optical fiber sensors and three-dimensional scanning, the wooden substrate was reinforced with moisture-curable polyurethane (MCPU), the lacquer ash layer was strengthened with acrylic emulsion (Primal AC33), aged areas were restored with nano calcium hydroxide (Ca(OH)₂) aqueous dispersion, and polyethylene glycol (PEG 400) poultice application was implemented to restore the flexibility of the lacquer film. This research significantly enhanced the integrity and stability of the carved lacquer horse-hoof-shaped box, providing practical evidence and technical references for the scientific conservation of lacquerware excavated from arid regions of northern China. Full article

Global urbanization is driving a rising demand for sand and gravel, which has intensified riverbed mining. This threatens fluvial stability, flood safety, and ecological integrity. Although previous studies have documented localized geomorphic and hydrological impacts, systematic assessments that integrate long-term incision trends, embankment stability mechanisms, and resource optimization under multiple objectives remain limited. In this study, we investigate the Wengang section of the Fu River (Jiangxi, China), a sediment-deficient river reach subjected to decades of intensive mining. Through the application of hydrosediment analysis, hydrodynamic modeling, geotechnical–hydrological–mechanical (GHM) simulations, and a dynamic optimization model, the sustained impacts of mining are quantified, and science-based management strategies are proposed. The results indicate that extensive excavation has resulted in irreversible riverbed incision, with a net volume increase of 12.97 × 10⁶ m³ between 2003 and 2023, far exceeding the natural sediment deposition volume (0.853 × 10⁶ m³). Although the overall longitudinal profile remains stable, localized flow velocities in the primary mining area are increased by 0.22–0.39 m/s. A GHM analysis identifies a critical safe distance of 13–14 m between pit edge and embankment toe and demonstrates that wide-shallow pit morphology is associated with reduced stability risk compared to narrow-deep pits. Based on these constraints, an economic optimization model incorporating flood safety and market demand is developed, yielding an optimal extraction plan for 2024–2028 with a total volume of 4.4848 million tons and an estimated revenue of 50.03 million USD. This study provides an integrated framework for assessing mining impacts and offers actionable strategies to support sustainable sediment management in vulnerable river systems. Full article

Nest-site selection in birds is a hierarchical process shaped by environmental filters operating across multiple spatial scales. In species that depend on burrows excavated by ecosystem engineers, understanding how these filters interact is essential for effective conservation. We evaluated nest-site selection by the Burrowing owl (Athene cunicularia) within colonies of the Mexican prairie dog (Cynomys mexicanus) in the southern Chihuahuan Desert using a multiscale analytical framework spanning burrow, site, colony, and landscape levels. During the 2010 and 2011 breeding seasons, we located 56 successful nests and paired each with an inactive non-nest burrow within the same colony. Eighteen structural and environmental variables were measured and analyzed using binary logistic regression models, with model selection based on an information-theoretic approach (AICc) and prior screening for predictor collinearity. Nest-site selection was associated with greater internal burrow development and reduced external exposure at the burrow scale, proximity to satellite burrows and low-to-moderate vegetation structure at the site scale, higher densities of active prairie dog burrows at the colony scale, and reduced predation risk and agricultural disturbance at the landscape scale. The integrated multiscale model showed substantially greater support and discriminatory power than single-scale models, indicating that nest-site selection emerges from interactions among spatial scales rather than from isolated factors. These findings support hierarchical habitat-selection theory and underscore the importance of conserving functional Mexican prairie dog colonies and low-disturbance grassland landscapes to maintain suitable breeding habitats for Burrowing owls in the southern Chihuahuan Desert. Full article

Background/Objectives: Along the Tyrrhenian coast of central Italy, multilayered caves have yielded significant Neanderthal-era human remains. Recent excavations at Guattari Cave uncovered hominin fossils dated to approximately 66–65 ka, revealing a population with notable morpho-anatomical variability exhibiting both plesiomorphic (primitive) and autapomorphic (derived) traits. Methods: Here we present detailed morphometric and comparative analyses of cranial, dental, and postcranial remains, demonstrating affinities with Homo erectus (sensu stricto [s.s.] and lato [s.l.]), Proto-Neanderthals, classical Neanderthals, and Homo sapiens. Results: These findings indicate notable morpho-anatomical variability among the Guattari Cave hominin remains, with affinities to multiple hominin lineages during the Middle and Late Pleistocene. Pleistocene. Conclusions: The Guattari Cave assemblage thus contributes to our understanding of Eurasian hominin diversity and evolutionary dynamics, highlighting the Mediterranean as a region of interest for studying the phyletic continuity and diversity preceding modern humans. Full article

Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations. Full article

An accurate in situ stress field is a prerequisite for evaluating the stability of surrounding rock in underground caverns of a pumped-storage power station (PSPS) and ensuring the long-term safe operation of underground powerhouses. However, in situ stress measurements in the field are typically characterized by a limited number of measurement points, strong data randomness, and high testing costs. Meanwhile, conventional regression inversion methods often yield stress fields with insufficient accuracy or unstable spatial distributions. To address these issues, this paper proposes an in situ stress field inversion method based on the particle swarm optimization–support vector regression (PSO-SVR) algorithm. Stress boundary conditions are formulated in terms of lateral stress coefficients combined with shear stresses, and PSO is employed to optimize the hyperparameters of the SVR model. The stress boundary conditions predicted by the PSO-SVR algorithm are then imposed on a numerical model to compute the stresses at the measurement points, and the optimal boundary conditions are identified by minimizing the root mean square error (RMSE) between the inverted and measured in situ stresses. On this basis, the stress components at the measurement points and the in situ stress field in the study area are obtained. The results demonstrate that the inverted in situ stresses agree well with the field measurements, exhibiting good consistency and spatial regularity. Specifically, compared with the traditional multiple linear regression (MLR) method, the PSO-SVR algorithm reduces the RMSE and mean absolute error (MAE) of the in situ stress measurement data by 48.21% and 47.01%, respectively, and produces inversion results with higher accuracy, more stable spatial patterns, and markedly fewer anomalous zones. Consequently, the PSO-SVR algorithm is well suited for in situ stress inversion in PSPSs and provides a reliable stress-field basis for subsequent optimization of underground cavern excavation and support. Full article

Loess subgrades are prone to significant strength reduction and deformation under cyclic traffic loads and moisture ingress. Permeable polyurethane polymer grouting has emerged as a promising non-excavation technique for rapid subgrade reinforcement. This study systematically investigated the fatigue behavior of polymer-grouted loess using laboratory fatigue tests and numerical simulations. A series of stress-controlled cyclic tests were conducted on grouted loess specimens under varying moisture contents and stress levels, revealing that fatigue life decreased with increasing moisture and stress levels, with a maximum life of 200,000 cycles achieved under optimal conditions. The failure process was categorized into three distinct stages, culminating in a “multiple-crack” mode, indicating improved stress distribution and ductility. Statistical analysis confirmed that fatigue life followed a two-parameter Weibull distribution, enabling the development of a probabilistic fatigue life prediction model. Furthermore, a 3D finite element model of the road structure was established in Abaqus and integrated with Fe-safe for fatigue life assessment. The results demonstrated that polymer grouting reduced subgrade stress by nearly one order of magnitude and increased fatigue life by approximately tenfold. The consistency between the simulation outcomes and experimentally derived fatigue equations underscores the reliability of the proposed numerical approach. This research provides a theoretical and practical foundation for the fatigue-resistant design and maintenance of loess subgrades reinforced with permeable polyurethane polymer grouting, contributing to the development of sustainable infrastructure in loess-rich regions. Full article

This contribution presents an integrated methodological pipeline for digital documentation and virtual reconstruction of large-scale urban archaeological excavations, developed through the Amba Aradam case study (Metro C line, Rome). The excavation revealed a 2nd-century A.D. military complex extending over 4770 m² at depths reaching 20 m, documented through multiple photogrammetric campaigns (2016–2018) as structures were progressively excavated and removed. We established an empirically validated texture density standard (1.26 mm²/texel) for photorealistic digital replicas suitable for immersive HMD and desktop exploration, with an explicit texture density calculation formula ensuring reproducibility. The temporal integration workflow merged 3D snapshots acquired across three excavation campaigns while maintaining geometric and chromatic consistency. Semantic documentation, through the extended matrix framework, recorded Virtual Stratigraphic Units linking archaeological evidence, comparative sources, and interpretative reasoning (paradata) for transparent virtual reconstruction. The complete pipeline, implemented through open-source 3DSC 1.4 and EMtools add-ons for Blender and Metashape v0.9 (available on GitHub), addresses specific challenges of documenting complex stratigraphic contexts within active construction environments where in situ preservation is not feasible. The spatial integration of the digital replica with previous archaeological data illuminated the urban evolution of Rome’s military topography during the 2nd–3rd centuries A.D., demonstrating the essential role of advanced digital documentation in contemporary urban archaeology. Full article

Prestressed flexible support systems have become essential in deep excavation engineering, with the load distributive compression anchor (LDCA) widely adopted to enhance load-bearing performance through effective load dispersion among multiple anchoring units. Structural parameters of the anchor, particularly perforation ratio and height-to-diameter ratio, play a critical role in determining the mechanical behavior of the surrounding grout. In this study, grout located 500 mm behind the anchor body was selected as the test specimen. Unconfined compression tests were conducted to evaluate the ultimate load-bearing capacity under varying anchor configurations. Based on experimental measurements, a numerical simulation model was established and calibrated to investigate the internal stress distribution of the grout under different perforation ratios and height-to-diameter ratios. Results indicate that the perforation ratio significantly influences both the magnitude and location of stress peaks within the grout, with higher perforation ratios shifting the x-directional stress peak toward the anchor orifice and gradually reducing ultimate load-bearing capacity. Reducing the height-to-diameter ratio leads to a more uniform stress distribution, mitigating stress concentration while maintaining near-constant load-bearing capacity, although it increases anchor deformation. Optimal perforation ratio ranges were determined as [11%, 23%], [31%, 37%], and [42%, 50%] for anchors 1, 2, and 3, respectively, and the recommended height-to-diameter ratio is [15%, 17%]. The integration of experimental testing and numerical simulation provides quantitative insights into the effects of anchor design on grout performance, offering practical guidance for optimizing LDCA structures in deep excavation projects. Full article

Microwave fracturing and assisted mechanical breakage offer efficient and cost-effective rock excavation potential. However, these methods have not been well studied or understood for the deployment of in situ recovery (ISR) in mining, which could benefit from microwave-induced cracking to accelerate in situ leaching. This paper reports on investigations into the effects of microwaves on rock transport properties, specifically for in situ recovery applications. The research focused on microwave fragmentation of a synthetic ore with composition and particle size similar to many wet ore-bearing deposits, as well as hard lithium-bearing rock (spodumene) as a natural analogue, to assess changes in porosity and permeability after microwave treatment. The experiments involved exposing samples with varying water content to heating with different microwave energy levels, followed by examining the impact on the induced crack characteristics. All the samples were characterized by a suite of measurements before and after microwave treatment, including scanning electron microscopy (SEM), Nuclear Magnetic Resonance (NMR), nitrogen gas permeameter-porosimeter, and P-wave velocity measurements. The results showed a strong dependence of rock properties after microwave treatment on water content. At high water content (100%), NMR results showed a substantial increase in porosity, by nearly 17% and a dramatic 47-fold rise in permeability, from 0.65 mD to 311 mD. However, the treatment also caused partial melting of the sample, rendering it unsuitable for further testing, including permeability and P-wave velocity. At moderate water content (20%), permeability substantially increased (233–3404%), which was consistent with the observation of multiple cracks in SEM images. These changes led to low P-wave velocity values. This research provides crucial insights into microwave fracturing as a method for in situ recovery in mining. Full article

Brown glazed wares, as some of the famous Song wares, attract significant attention nowadays due to the glaze containing a large amount of metastable ε-Fe₂O₃, a promising multiple-functional electromagnetic material. In this work, typical fragments were systematically analyzed by micro-Raman spectroscopy combined with scanning electron microscopy as well as X-ray fluorescence. Abundant ε-Fe₂O₃ crystals were observed in the glaze surfaces, with the exception of numerous hematite crystals detected in the surfaces of fragments excavated in Hunyuan kilns (Shanxi province). The correlative analyses of Raman and XRF data indicate that relatively high SiO₂ and low CaO contents in the system may benefit ε-Fe₂O₃ precipitation, and the crystallization temperature may range from 1150 to 1200 °C. In addition, various crystals were detected in the glazes, including magnetite, magnesioferrite, zircon, anatase, pseudobrookite, rutile, cordierite, cristobalite, quartz, and mullite. Full article