Search Results (708)

Differential surface settlement is a critical trigger for engineering disasters, with buried ground fissure activity serving as one of the core factors driving such subsidence. In this study, four groups of physical model tests were conducted. High-precision laser displacement meters were used to monitor real-time settlement at varying distances from the ground fissure. Based on this, the surface displacement evolution patterns of both loess layer structures and interbedded sand–soil structures under slow (0.01 m/h) and rapid (2 m/h) ground fissure activities were summarized. To evaluate the predictive performance of different models for time- and space-dependent nonlinear displacement evolution, five methods, namely polynomial regression, support vector regression (SVR), multilayer perceptron (MLP), random forest (RF), and gradient boosting decision tree (GBDT), were constructed and compared using the experimental displacement dataset. The physical model test results indicate that the interbedded sand–soil structure tends to suppress localized crack propagation while expanding the affected zone range, whereas the loess layer is prone to near-field deformation localization. The activity rate exerts a pronounced influence on fracture propagation within the loess layer, while its influence on the deformation of the interbedded sand–soil structure is relatively limited under the present test conditions. The model comparison results show that a cubic polynomial can effectively describe the displacement evolution pattern under slow and homogeneous conditions. MLP exhibited the most stable performance among the four machine learning algorithms, while SVR, GBDT, and RF provided complementary information for interpreting continuous displacement evolution and local nonlinear displacement variations. Overall, under the present physical model test conditions, the machine learning models provide a useful data-driven basis for quantitatively characterizing displacement evolution trends and interpreting the development of ground fissure-affected zones. Based on the experimental results—scaled up by a similarity ratio of 20:1 and incorporated with a safety factor of 1.1~1.3—the preliminary reference engineering avoidance distance in the ground fissure-affected zone is 20~24 m for the hanging wall and 14~16 m for the footwall, which are broadly consistent with the current codes and regulations. The findings provide controlled experimental evidence and a quantitative reference for ground fissure hazard assessment and engineering protection. Full article

Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, between 2019 and 2024. Using InSAR-derived datasets for training and validation, three machine learning architectures, namely two-dimensional convolutional long short-term memory (ConvLSTM2D), hybrid convolutional neural network–long short-term memory (hybrid CNN-LSTM), and hybrid convolutional neural network–bidirectional long short-term memory (hybrid CNN-BiLSTM), were developed to further analyze ground deformation and make future predictions. It was found that from SBAS-InSAR, the deformation rates for the whole Dongli District, Tianjin, ranged from −40.98 to 27.18 mm/year, with a mean of −2.41 mm/year from 2019 to 2024. Model performance was evaluated using held-out validation samples derived from the InSAR deformation dataset. The ConvLSTM2D model achieved the best performance, with an R² value of 0.99 and root mean squared error (RMSE) of 1.37 mm, compared with the hybrid CNN-LSTM (R² = 0.99, RMSE = 2.16 mm) and hybrid CNN-BiLSTM (R² = 0.99, RMSE = 2.19 mm). This optimized ConvLSTM2D model was applied to estimate the predictions of the ground deformation rate with −43.71 mm/year in the high-deformation zone between 2025 and 2028. These findings predict a continuing trend of land instability, highlighting the necessity for urgent geohazard mitigation and urban planning strategies in the affected regions. Full article

A frequent and sudden two-phase (solid–liquid) geological hazard in mountainous areas, the evolution of debris flows involves the coupling of multiple physical fields, making monitoring and early warning particularly challenging. To accurately reveal the dynamic patterns of debris flow evolution and improve early-warning accuracy, this study focused on the Ni Chang Valley area in Shimian County, Ya’an City, Sichuan Province. Based on the COMSOL Multiphysics coupling simulation platform, a multiphysics bidirectionally strongly coupled numerical model was proposed and constructed, integrating the SPH (smoothed particle hydrodynamics) meshless particle method, FLO-2D shallow-water dynamics, and the MassFlow full-process simulation approach. Using COMSOL as a unified framework, this model employs MassFlow’s deep-integration, continuous medium method to simulate rainfall triggering and material source activation, FLO-2D’s shallow-water equations to describe macroscopic flow-deposition processes, and SPH’s mesh-free particle method to accurately capture large deformations and free-surface flow. The model fully reproduces the entire dynamic chain of debris flow processes, from rainfall triggering and soil mobilization to fluid transport and channel deposition. The reliability and accuracy of the model were verified by comparing it with field measurements from the 20 September 2022 historical debris flow event at Ni Chang Valley. Quantitative analysis indicates that when the viscosity coefficient increases from 0.1 Pa·s to 100 Pa·s, the flow velocity decreases by approximately 47% and the flow depth increases by approximately 62%. When the yield stress increases from 1 Pa to 100 Pa, the deposition area shrinks from 269,900 m² to approximately 109,000 m², a reduction of about 60%. Combining the results of the dynamic analysis, daily maximum temperature, daily precipitation, moisture content, mud-water level, and ground surface displacement were selected as core monitoring indicators. The analytic hierarchy process (AHP) was used to determine the weights of each indicator, and a data- and physics-driven weighted summation model for debris flow monitoring and early warning was constructed to achieve a five-level debris flow monitoring and early-warning system. Historical disaster cases demonstrate that this early-warning model can provide advance predictions of debris flow disasters up to 2 h and 40 min in advance. The warning lead time is sufficient, the grading logic is clear, and the model is capable of accurately capturing precursor information on disasters. Full article

Ultra-deep excavations in soft soil pose major challenges for deformation control. Based on field monitoring of a 38.3 m deep five-story metro excavation in Tianjin, this study systematically investigates the spatiotemporal deformation of the diaphragm wall, columns, and surrounding environment. Key innovations include the proposal of an extended ground settlement influence model and the quantification of stage-wise deformation development ratios. The maximum lateral wall displacement is about 40 mm, ranging from 0.028% H_e to 0.184% H_e (average 0.087% H_e), outperforming comparable bottom-up excavations in Shanghai. Wall top vertical displacement varies from −0.23% H_emax to 0.04% H_emax, and column rebound averages 3.5 mm, is significantly lower than that of excavations using the bottom-up method. The extended settlement model shows that the maximum settlement occurs at H_e/3 from the wall, the primary influence zone extends to 3H_e, and the secondary zone reaches 5H_e. Building settlement strongly depends on distance and foundation type, with raft foundations settling much more than pile-raft foundations. Stage-by-stage analysis reveals that, immediately after the completion of diaphragm wall construction, the settlement already exceeded 60% of the final maximum ground settlement. Furthermore, the deformation on the long side developed at a faster rate than that on the short side. These findings provide quantitative benchmarks for designing ultra-deep excavations in soft soil. Full article

Anchored bored pile walls are widely used to control deformation in deep urban excavations, but their serviceability performance depends on soil stiffness, excavation depth, wall stiffness, anchor configuration, construction staging, groundwater conditions and seismic demand. This study compares three real excavation support projects in contrasting soil groups: soft to hard clay, hard to very hard clay, and dense to very dense gravel. The calculations follow a Eurocode 7-compatible Design Approach 2 framework. Static finite-element analyses, equivalent-static seismic analyses and scaled time-history analyses were compared with in situ inclinometer measurements. The seismic input included site-specific spectral parameters, horizontal acceleration coefficients, Rayleigh damping parameters and 11 scaled PEER ground-motion records. The key design insight is that increasing the number of anchor rows alone cannot compensate for low ground stiffness or limited wall stiffness; soil-structure interaction must be interpreted together with support configuration. The finite-element and measured maximum horizontal displacements were 79.97 and 75.80 mm for the sports hall excavation, 23.22 and 22.70 mm for the residential excavation, and 27.67 and 23.20 mm for the controlling square-project section. The study demonstrates the value of integrating Eurocode-based design checks, dynamic analysis and field monitoring for deep-excavation safety. Full article

It is essential to exercise control over the environmental impact of deep excavation construction in soft soil areas from the perspective of deformation in order to ensure engineering safety. A three-dimensional finite element model of the foundation pit was developed, thereby creating a comparison between the results of the numerical simulation and the actual on-site monitoring data. This process served to validate the precision of the simulations. The focal point of the study pertained to the three-dimensional effects of support structure deformation and ground settlement during excavation. A comprehensive analysis of the spatial distribution and evolutionary patterns of underground diaphragm wall deformation and ground settlement behind the wall at varying excavation depths was conducted. The results demonstrated that both support structure deformation and ground settlement behind the excavated structure exhibited substantial spatial effects. In particular, larger deformations were observed near the symmetrical plane of the excavation centre. Conversely, greatly smaller deformations were observed in the corners of the excavation. The research findings aim to provide useful references for practical engineering projects. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Geological observations, excavation performance records, field monitoring data, and support applications obtained during construction were examined in relation to excavation-induced structural behavior. Monitoring systems, including tiltmeters, extensometers, and distomat measurements, were used to evaluate deformation behavior in buildings located near the tunnel alignment. This study additionally discusses the differences between the preliminary FEM-based deformation predictions reported during the design stage and the structural behavior observed during excavation. The results indicate that groundwater-sensitive claystone sections generated significant excavation and stability problems despite controlled excavation and support measures. High groundwater inflow (30–35 L/s) caused base instability, unfavorable excavation conditions, and increased deformation risks. Field observations indicated substantially greater deformation behavior than the preliminary FEM-based predictions reported during the design stage. The findings demonstrate that shallow urban tunneling in weak and groundwater-sensitive formations may generate more complex ground–structure interactions than those represented in preliminary numerical assessments. This study highlights the importance of continuous field monitoring, adaptive support strategies, groundwater control measures, and observational excavation management practices for improving tunnel safety and reducing risks to nearby urban structures. Full article

Earthquake-induced landslides represent a critical threat to transportation infrastructure in tectonically active mountainous regions, particularly in tropical volcanic settings where weak, highly weathered geomaterials dominate. This study develops an integrated framework that directly links physically based seismic threshold modelling with real-time landslide monitoring and operational early warning. The approach is demonstrated in the Cugenang area of Cianjur Regency, West Java, Indonesia, which was severely impacted by the moment magnitude (Mw) 5.6 earthquake in 2022. Slopes composed of highly weathered pyroclastic deposits [Plasticity Index (PI) = 54–68%; porosity > 60%] exhibit low shear strength and high sensitivity to seismic loading. Limit equilibrium analysis using the Morgenstern–Price method that combines the influence of seismic loading and groundwater conditions suggests that a horizontal seismic coefficient (kh) of approximately 0.06, corresponding to a Peak Ground Acceleration (PGA) of about 0.12 gravitational acceleration (g), is a critical threshold for initial landsliding. This comparatively low threshold challenges commonly reported values and demonstrates that slope failure in tropical volcanic terrains can occur under moderate ground shaking, reinforcing the need for site-specific hazard characterisation. The derived thresholds are operationalised within a multi-sensor early warning system integrating Micro-Electro-Mechanical Systems (MEMS) accelerometers and inclinometer measurements. Three hazard levels—Normal (<0.06 g), Alert (0.06–0.12 g), and Emergency (≥0.12 g)are combined with deformation thresholds [<10 milimeter (mm), 10–30 mm, >30 mm] to capture progressive failure processes and minimise false alarms. By coupling geotechnical modelling and real-time monitoring, this study provides a transferable and scalable framework for enhancing infrastructure resilience in landslide-prone regions. Full article

Slope stability in open-pit mining is not a static condition but evolves continuously as excavation progresses and geomechanical conditions change. In this study, an integrated approach combining ground-based radar monitoring, satellite-based InSAR time-series analysis, and numerical stability modeling was applied to evaluate slope behavior in a large-scale open-pit copper mine with complex geological and structural characteristics. Radar data revealed progressive and episodic deformation concentrated in specific slope sectors, while InSAR observations showed that deformation continued at lower rates after the main movement phase, providing a longer-term perspective of slope response. Stability analyses using limit equilibrium and finite element methods indicate that the slope operates close to a limit equilibrium condition, particularly under saturated scenarios where factors of safety approach critical levels and strain localization becomes more pronounced. The results show a clear link between observed deformation patterns and calculated stability conditions, with structural discontinuities and groundwater playing a dominant role in controlling slope behavior. Based on these findings, an integrated workflow is proposed that links monitoring data with stability assessment, enabling the identification of critical zones and supporting the evaluation of slope conditions during ongoing mining operations. This approach contributes to more reliable decision-making and supports safer and more sustainable open-pit mining practices. Full article

The continuous analysis of ground deformation is essential for both the assessment of natural hazards and the monitoring of human-induced activities. In this study, we present the results of a Persistent Scatterer Interferometry (PSI) analysis of ground deformations in the region of Cluj-Napoca, Romania. The PSI was performed using more than 10 years of Sentinel-1 ascending and descending Synthetic Aperture Radar data from 2014 to 2025, using a dual master approach. Results show significant displacements at many locations, including recently built-up areas at the edges of the city, often caused by the combined effect of anthropogenic activities and geological conditions. In this study, we highlight three case studies: the surroundings of a reclaimed mine, subsidence induced by dewatering, and a large-area, slow landslide, wherein we examined natural and anthropogenic influences. The accurately mapped and quantified ground deformations can be used for a better understanding of the geological processes and assessing the risk of the urban development in the area. Full article

To ensure the sustained stability of absolute gravity benchmark points from 2017 to 2024, observational records from superconducting gravimeters (SGs) and absolute gravimeters were comprehensively examined in this work, and the environmental effects on gravitational acceleration were quantitatively assessed. The annual fluctuation of the SG (iGrav-012k) scale factor reached 0.268 μGal/V, with a weighted average of (–92.8702 ± 0.0265) μGal/V (relative precision of 0.3‰), providing a precise scale factor for long-term SG monitoring. By removing step discontinuities in the SG data using FG5-X249 absolute gravimeter measurements, the residual fitting error decreased to 6.3 μGal. In addition, the SG drift was estimated as 1.0 μGal/year through international comparison datasets and FG5 measurements, substantially improving the consistency of the time series. Further investigation showed that the SG residuals exhibited clear seasonal oscillations, which were mainly attributed to local hydrological processes and ground deformation near the benchmark sites. By integrating groundwater level and deformation monitoring data and applying a neural network model to separate hydrological load components, the peak-to-peak residual amplitude was reduced from 13 μGal to 3.5 μGal. Quantitative analysis indicated that hydrological effects contributed about 9.5 μGal to the seasonal variation, whereas surface deformation had only a minor impact (<2 μGal). The findings confirm that careful data correction and isolation of environmental effects are effective for maintaining the long-term stability of gravity benchmarks. The developed workflow provides a reproducible framework for high-precision gravity site maintenance and supports future dynamic monitoring of regional environmental load responses. Full article

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional manual disaster investigation is inefficient because the pipelines are widely distributed, access is limited and the terrain may be rugged. Therefore, efficient and accurate disaster identification and risk assessment have become a priority that the industry urgently needs to address. Taking the Jiangxi section of the West Line II Zhangshu–Xiangtan connection line as the research area, this study combines the SBAS-InSAR technology with spatial analysis based on GIS to support early disaster identification, surface deformation monitoring and vulnerability assessment. The analysis of 48 Sentinel-1A satellite images shows that the regional ground deformation range is −19.5 to 19.1 mm per year, and most areas show a slow deformation of within ±10 mm per year. The preliminary visual interpretation of the SBAS-InSAR ground deformation data yields 121 preliminary high-deformation disaster points. Combined with the 9 key assessment factors in the GIS platform and the entropy-weighted information model obtained from the geological disaster susceptibility evaluation map and using the optical remote sensing images, 21 human interference points are excluded, and finally 100 potential geological disaster hazard areas are retained. Field verification was conducted through ground reconnaissance surveys and confirmed that 78 of these areas have geological disaster hazards such as landslide, collapses, and slope water damage, providing solid technical support for geological disaster management, monitoring and early warning along the pipeline route. This study proposes a multi-source integrated framework combining SBAS-InSAR, GIS-based susceptibility assessment, and optical validation for improving the reliability of early geological hazard identification. Full article

Ground deformation monitoring is critical for safety and environmental management in modern mining. Active mining sites are highly exposed to terrain instabilities and subsidence, risking infrastructure integrity, disrupting operations, and posing hazards to communities. In this context, Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques provide an effective and non-invasive tool capable of detecting millimetric surface displacements. This study implements the Small Baseline Subset (SBAS) technique through an open-source workflow based on the Python package hyp3_sbas, enabling semi-automated and reproducible interferometric processing by combining HyP3 with MintPy. The workflow is applied to the Björkdal gold mine (Sweden), a pilot site of the Horizon Europe XTRACT project focused on enhancing resilience in critical raw material supply chains. Integrating Sentinel-1 viewing geometries resolves the true vertical deformation field, yielding an overall mean velocity of −3.99 mm/year across the mining complex, with significant displacement rates concentrated below the 25th percentile (Q1) at −11.07 mm/year. Sector-specific analysis reveals localised subsidence accelerating over underground footprints and tailings storage facilities (mean velocities of −6.56 and −3.98 mm/year; Q1 thresholds near −13.00 mm/year), contrasting with the geomechanical stability observed at the open-pit area (mean: −0.45 mm/year). The proposed open-source framework shows strong potential for operational satellite-based monitoring, supporting predictive maintenance and early-warning strategies for risk management in mining environments while simplifying and standardising the interferometric processing workflow. Full article

Shield tunnel construction inevitably disturbs existing upper buildings. This paper takes the section from Zhongyi Road Station to Housihu Fourth Road Station of Wuhan Metro Line 12 as the engineering background, where twin shield tunnels pass beneath Zizhu Kindergarten. Based on field monitoring data, this paper systematically analyzes the development laws of surface settlement and building settlement. Numerical simulation is adopted and compared with measured data to verify the reliability of the model. With the validated numerical model, this paper investigates the influencing factors of building settlement. The results show that the maximum ground surface settlement during shield construction is approximately 6.84 mm, and the maximum building settlement is about 4.63 mm. The horizontal relative position between piles and tunnels changes the superposition mode of ground settlement troughs. Building settlement reaches the minimum when twin tunnels pass beneath symmetrically. Eccentric crossing aggravates building settlement to a certain extent. The maximum building settlement increases with the rise of tunnel buried depth. The research results can provide a reference for deformation control and construction optimization of similar twin shield tunnels crossing beneath buildings with piled-raft foundations. Full article