Search Results (128)

With the rapid urbanization and increasing development of underground spaces, foundation pit groups in complex geological environments encounter considerable challenges in deformation control. These challenges are especially prominent in cases of adjacent constructions, complex geology, and environmentally sensitive areas. Nevertheless, existing research is lacking in systematic analysis of construction sequencing and the interaction mechanisms between foundation pit groups. This results in gaps in comprehending stress redistribution and optimal excavation strategies for such configurations. To address these gaps, this study integrates physical model tests and PLAXIS 3D numerical simulations to explore the Nanjing Jiangbei New District Phase II pit groups. It concentrates on deformations in segmented and adjacent configurations under varying excavation sequences and spacing conditions. Key findings reveal that simultaneous excavation in segmented pit groups optimizes deformation control through symmetrical stress relief via bilateral unloading, reducing shared diaphragm wall displacement by 18–25% compared to sequential methods. Sequential excavations induce complex soil stress redistribution from asymmetric unloading, with deep-to-shallow sequencing minimizing exterior wall deformation (≤0.12%He). For adjacent foundation pit groups, simultaneous excavation achieves minimum displacement interference, while phased construction requires prioritizing large-section excavation first to mitigate cumulative deformations through optimized stress transfer. When the spacing-to-depth ratio (B/He) is below 1, horizontal displacements of retaining structures increase by 43% due to spacing effects. This study quantifies the effects of excavation sequences and spacing configurations on pit group deformation, establishing a theoretical framework for optimizing construction strategies and enhancing retaining structure stability. The findings are highly significant for underground engineering design and construction in complex urban geological settings, especially in high-density areas with spatial and geotechnical constraints. Full article

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts. Full article

Urbanization, escalating agriculture, tourism, and industrial development in the Chiang Mai–Lamphun groundwater basin in northern Thailand have increased water demand, causing widespread groundwater extraction. Over the past few decades, there has been a rapid, unrecoverable steady drop in groundwater levels in several areas in Chiang Mai and Lamphun provinces. This study employed hydrogeological investigations, hydrometeorological data analyses, stable isotopic analysis (δ¹⁸O and δ²H), and groundwater flow modeling using a 3D groundwater flow model (MODFLOW) to quantify groundwater recharge and delineate important groundwater recharge zones within the basin. The results showed that floodplain deposits exhibited the highest recharge rate, 104.4 mm/y, due to their proximity to rivers and high infiltration capacity. In contrast, younger terrain deposits, covering the largest area of 1314 km², contributed the most to total recharge volume with an average recharge rate of 99.8 mm/y. Seven significant recharge zones within the basin, where annual recharge rates exceeded 105 mm/y (average recharge of the entire basin), were also delineated. Zone 4, covering parts of densely populated Muaeng Lamphun, Ban Thi, and Saraphi districts, had the largest area of 330 km² and a recharge rate of 130.2 mm/y. Zone 6, encompassing Wiang Nong Long, Bai Hong, and Pa Sang districts, exhibited the highest recharge rate of 134.6 mm/y but covered a smaller area of 67 km². Stable isotopic data verified that recent precipitation predominantly recharged shallow groundwater, with minimal evaporation or isotopic exchange. The basin-wide average recharge rate was 104 mm/y, reflecting the combined influence of geology, permeability, and spatial distribution. These findings provide critical insights for sustainable groundwater management in the region, particularly in the context of climate change and increasing water demand. Full article

The rapid urbanization in the Kabul River Basin has increased the demand for water for both drinking and commercial purposes, leading to domestic and industrial water insecurity. Assessing the groundwater potential of the Kabul River Basin is highly crucial for effective water management. The aim of this paper is to identify potential zones for groundwater by employing a Geographic Information System and an Analytical Hierarchy Process approach to formulate a cumulative score based on seven thematic images—rainfall, geology, lineament density, drainage density, land use/land cover, soil type, and slope—within the Kabul River, with assigned weightages of 32%, 27%, 12%, 10%, 8%, 6%, and 5%, respectively, with a consistency ratio of 0.053 (5%), demonstrating the reliability of the results. The study shows that the first three factors contribute more to the percentages of Groundwater Potential Zones. The identified groundwater potential is classified into very good, good, medium, poor, and very poor zones, covering 35.45% (19,989 km²), 37.2% (20,978 km²), 23.16% (13,063 km²), 4.13% (2332 km²), and 0.06% (19 km²), respectively. Groundwater potential in the basin is predominantly classified as good to medium; however, there are notable variations across sub-basins. The Swat sub-basin and western parts of the Kabul River Basin, encompassing the Panjshir and Parwan districts, exhibit exceptionally high groundwater potential. In contrast, the Panjkora sub-basin (Dir district) and southwestern areas of the Kabul River Basin, covering parts of the Ghazni and Wardak districts, have very limited groundwater potential. Full article

With rapid urbanization, the utilization of underground space has become an important part of infrastructure. However, the stability of underground spaces such as large caverns remains a key challenge in civil engineering throughout the lifecycle of a project. Traditional methods of stability assessment rely on static models and periodic monitoring and often fail to capture real-time changes in rock behavior, leading to potential safety risks and, in severe cases, even the collapse of underground infrastructure. To address this challenge, this study introduces a digital twin (DT) framework to improve stability assessments and monitor deformations in underground structures. The framework enables the continuous monitoring and adaptive optimization of rock support systems by combining real-time sensor data with virtual simulations. A five-dimensional DT framework comprises physical objects, virtual objects, service systems, DT data, and their interconnections. It incorporates six key modules, which are structure, geology, material, behavior, performance, and environment, to enhance the understanding of cavern stability. The framework is based on Industry Foundation Classes standards to ensure seamless data exchange, interoperability, and the standardized representation of geotechnical and structural data. A seven-step methodology is developed for this framework, encompassing geological assessment, virtual modeling, Building Information Modeling (BIM)-based design, construction processes, real-time monitoring, and optimization strategies. To evaluate its effectiveness, the framework is applied to a case study, demonstrating improvements in deformation monitoring and rock support efficiency. The findings highlight the potential of integrating DT with BIM to enhance safety, reliability, and long-term stability in underground construction projects. Full article

The environment is being affected by the great development of human activities, which is why, in recent years, the need to protect the environment has increased, through the carrying out of a Strategic Environmental Assessment (SEA). Within this assessment, environmental geology constitutes an instrument for territorial and urban planning based on the analysis of conservation and the integral analysis of risks, obtaining cartography that can be useful in territorial and regional planning strategies. The methodology carried out in this article consists of applying a multi-criteria analysis in territorial planning, combining vector and raster data. This novel, low-cost, and effective methodology assesses conservation areas and risks, using map algebra and network analysis to identify priority areas and facilitate decision-making in a precise and quantitative manner. This analysis has been carried out in the Arribes del Duero Natural Park, which stands out as a place where numerous environmental values coexist, i.e., geological, geomorphological, and edaphological, forming unique landscapes. With regard to the results obtained, the cartography of conservation quality classifies the territory into four categories according to its degree of conservation: very high, high, low, and very low quality. The integral risk cartography identifies the areas with the greatest geological risks, such as erosion and landslides, and establishes limitations for land use. Also, by integrating both cartographies, it is determined which activities are compatible with each zone, considering both conservation and risks. Finally, it can be concluded that the cartographies obtained are useful for efficient land management, protecting the environment, and allowing human development in a controlled manner. Full article

The rapid and effective extraction of fault entities is a fundamental process in constructing a fault knowledge graph. As a key method for recording and preserving fault data, a fault investigation report holds significant potential for extracting valuable information. This paper proposes a fault knowledge annotation system that incorporates geographic information, fault attribute, fault structure, fault activity, fault geomorphology, and fault hazard. The system is developed based on a comprehensive analysis of the textual characteristics of fault investigation reports. Additionally, we establish a fine-grained corpus tailored for this task and apply a combination of BERT and BiLSTM-CRF for named entity recognition in the fault domain. We compare the performance of our model with a non-pre-training baseline model. The experimental results demonstrate that (1) the F1 value of entity recognition based on the faulty corpus exceeds 80%, which validates the efficacy of the faulty corpus; (2) the BERT model can effectively utilize available information. The corpus to adjust the subsequent tasks, thus improving the model output; (3) the proposed BERT-BiLSTM-CRF model and ALBERT-BiLSTM-CRF models have superior extraction performance in comparison to the no-pre-training model. This study not only provides a theoretical basis for the effectiveness of the BERT-BiLSTM-CRF model in fault entity identification, but also establishes a solid data foundation for the subsequent construction of the fault knowledge map. In addition, it offers reliable technical support for practical application areas such as geological surveys, disaster early warning, and urban planning, thereby promoting the advancement of data-driven research in the field of geology. Full article

Deep mining in metal mines faces more and more complex geological conditions, such as “three highs and one disturbance” (high ground stress, high permeability, high temperature, and mining-induced disturbance), which can easily trigger water inrush disasters and seriously affect the safety and efficiency of deep mining. This paper focuses on the deep hydrogeological structural characteristics of the Huize lead–zinc mine. Firstly, two main factors affecting the production safety of the mining area, namely the water source and water channel of the mine, were analyzed. Based on this analysis, nine factors were determined as indicators for the risk assessment of water inrush, including the water head difference, water-bearing capacity, permeability coefficient, aquifer thickness, water pressure, fault type, fault scale, fault water conductivity, and karst zoning characteristics. Then, a water inrush risk assessment model for the deep mine was constructed, and the weights of the individual factors were determined using the analytic hierarchy process (AHP) and entropy weight method (EWM). Combined with the multi-factor spatial fitting function of the GIS, a zoning map of the risk assessment of water inrush was developed. The results showed that the aquifer groups of the Permian Liangshan Formation and the Carboniferous Maping Formation (P₁l + C₃m) were relatively safe, whereas the karst fissure aquifer of the Qixia–Maokou Formation (P₁q + m) posed a high risk of water inrush, necessitating advanced exploration and water drainage in the area. These findings provide guidance for water control measures in the Huize lead–zinc mine and offer valuable insights into the prediction and prevention of mine water hazards associated with ore body mining in karst aquifers. Full article

Mine water disaster is one of the main natural disasters in underground mining operations, and seriously threatens the safety of mine production and personnel’s life, affecting mine safety and sustainable development. The research on the prevention and control of the disaster of water inrush in fractured rock mass has become a major international frontier issue in the field of underground engineering, and it is also a major national demand. The key to effectively preventing and controlling disasters is to reveal the mechanisms of disasters. Taking the Huize lead–zinc mine as an example, this paper deeply studies the application method of the transient electromagnetic method (TEM) in advance water detection in shaft and roadway development and field test results. In view of the complicated hydrogeological conditions of the mine and the serious threat of water damage, this paper puts forward a kind of advanced water detection technology for the Huize lead–zinc mine based on the mine transient electromagnetic method. The technology uses the principle of electromagnetic induction to detect the water-bearing structure ahead by placing the transmitting and receiving coils in the shaft. In the field test, the multi-turn small wire frame device is used to detect the direction of the roof, bedding and floor of the roadway head on. In roadway excavation, if the site meets the detection requirements, the abnormal low-resistance area in the test area can be exposed by drilling first. The degree of structural development and the peak value of water gushing in the target area have been mastered. Then, it is determined whether it is necessary to increase borehole exploration in other relatively high-resistance low-risk areas. The experimental results show that the mine transient electromagnetic method can accurately identify the low-resistance water in front, and provide reliable technical support for mine water disaster prevention. The research in this paper not only enriches the application field of the mine transient electromagnetic method, but also provides a useful reference for mine water damage prevention under similar conditions. Full article

The development and utilization of urban underground space represents a crucial strategy for achieving sustainable urban development. Three-dimensional (3D) geological models provide a data foundation and technical support for research in urban planning and construction, as well as the prevention and control of environmental geological issues. However, current urban 3D geological modeling generally faces the challenge of multi-source heterogeneous modeling data. This often necessitates varying degrees of generalization in data processing, resulting in the majority of current urban 3D geological models being relatively coarse and insufficient to fulfill the demand for detailed geological information in contemporary urban development and management. Therefore, determining how to formulate or optimize the 3D geological modeling schemes to enhance the utilization of multi-source heterogeneous data is a key challenge in current urban 3D geological modeling. This study, taking the 3D geological structure modeling of Wuhan’s metropolitan development area (MDA) as an example, develops an automated scheme for standardizing modeling data based on multi-scale geological chronostratigraphy. By utilizing the standardized stratigraphy as a unified and independent geological framework for layered modeling, a high-precision 3D geological model of Wuhan’s MDA, characterized by large-scale and ultra-complex geological conditions, is constructed through a methodology that integrates the global discrete constrained points modeling approach with the global layered modeling approach, without generalizing the multi-source heterogeneous modeling data. This research not only holds significant practical implications for the prevention and control of comprehensive urban geological issues in Wuhan but also provides novel technical insights into the methodology of 3D urban geological modeling. Full article

Accurate prediction of the risk levels of debris flows is crucial for devising effective disaster prevention and mitigation strategies. This study, based on debris flow sample data from Yunnan Province, initially employs Principal Component Analysis to reduce the dimensionality of the raw data, extracting key features and minimizing data dimensions. Subsequently, a 5-fold cross-validation method is utilized to segment the dataset into training and testing sets, and a predictive model integrating Principal Component Analysis with an Elman Neural Network (PCA-Elman) is constructed. The study investigates the impact of data imbalance and spatial variability on the model’s predictive accuracy and attempts to enhance the model’s generalization capabilities by employing the Adaptive Synthetic Sampling algorithm and incorporating samples from unknown regions. The results indicate that the model demonstrates high accuracy and generalization in predicting debris flow risks, with its Area Under Curve value, Average Precision value, and average precision scores surpassing those of traditional models, achieving an accuracy rate of 88.57%. After processing the data with the Adaptive Synthetic Sampling algorithm, the model’s accuracy rate increases to 98.33%. Furthermore, incorporating samples from unknown regions into the trained model significantly improves the prediction accuracy for debris flow risks in those areas. This research offers new insights into the assessment of debris flow hazards and disaster prevention and mitigation efforts, and provides a reference for the construction of predictive models for similar natural disasters. Full article

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity and developmental abnormalities. To assess the ecological health risk of PFAS in Pennsylvania stream surface water, we conducted a comprehensive analysis that included both measured and predicted estimates. The potential combined exposure effects of 14 individual PFAS to aquatic biota were estimated using the sum of exposure-activity ratios ($\Sigma$EARs) in 280 streams. Additionally, machine learning techniques were utilized to predict potential PFAS exposure effects in unmonitored stream reaches, considering factors such as land use, climate, and geology. Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of ~65%). Feature importance analysis highlighted key variables that contributed to the CNN’s predictive power. The most influential features highlighted the complex interplay of anthropogenic and environmental factors contributing to PFAS contamination in surface waters. Industrial and urban land cover, rainfall intensity, underlying geology, agricultural factors, and their interactions emerged as key determinants. These findings may help to inform biotic sampling strategies, water quality monitoring efforts, and policy decisions aimed to mitigate the ecological impacts of PFAS in surface waters. Full article

Geothermal energy is a crucial component contributing to the development of local thermal energy systems as a carbon-neutral and reliable energy source. Insights into its availability derive from knowledge of geology, hydrogeology and the thermal regime of the subsurface. This expertise helps to locate and monitor geothermal installations as well as observe diverse aspects of natural and man-made thermal effects. Temperature measurements were performed in hydrogeological boreholes in south-western Poland using two methods, i.e., manual temperature logging and optical fibre distributed temperature sensing (OF DTS). It was assumed the water column in each borehole was under thermodynamic equilibrium with the local geothermal gradient of the subsurface, meaning rocks and aquifers. Most of the acquired results show typical patterns, with the upper part of the log depending on altitude, weather and climate as well as on seasonal temperature changes. For deeper parts, the temperature normally increases depending on the local geothermal gradient. The temperature logs for some boreholes located in urban agglomerations showed anthropogenic influence caused by the presence of infrastructure, the urban heat island effect, post-mining activities, etc. The presented research methods are suitable for applications connected with studies crucial to selecting the locations of geothermal installations and to optimize their technical parameters. The observations also help to identify zones of intensified groundwater flow, groundwater inrush into wells, fractured and fissured zones and many others. Full article

To effectively manage the sustainable urban development of cities, it is crucial to quickly understand the geological and geotechnical attributes of the underground. Carrying out such studies entails significant investments and focused reconnaissance efforts, which might not align seamlessly with large-scale territorial planning initiatives within a city accommodating more than 3 million inhabitants, like Casablanca in Morocco. Additionally, various specific investigations have been conducted by municipal authorities in recent times. The primary aim of this study is to furnish city managers and planners with a tool for informed decision-making, enabling them to explore the geological and geotechnical properties of soil foundations using Geographic Information Systems (GISs) and geostatistics. This database, initially intended for utilization by developers and construction engineers, stands to economize a substantial amount of time and resources. During the urban planning of cities and prior to determining land usage (five- or seven-floor structures), comprehending the mechanical traits (bearing capacity, water levels, etc.) of the soil is crucial. To this end, geological and geotechnical maps, along with a collection of 100 surveys, were gathered and incorporated into a GIS system. These diverse data sources converged to reveal that the underlying composition of the surveyed area comprises silts, calcarenites, marls, graywackes, and siltstones. These formations are attributed to the Middle Cambrian and the Holocene epochs. The resultant geotechnical findings were integrated into the GIS and subjected to interpolation using ordinary kriging. This procedure yielded two distinct maps: one illustrating bearing capacity and the other depicting the substratum. The bearing capacity of the soil in the study zone is rated as moderate, fluctuating between two and four bars. The depth of the foundation remains relatively shallow, ranging from 0.8 m to 4.5 m. The outcomes are highly promising, affirming that the soil in Casablanca boasts commendable geotechnical attributes capable of enduring substantial loads and stresses. Consequently, redirecting future urban planning in the region toward vertical expansion seems judicious, safeguarding Casablanca’s remaining green spaces and the small agricultural belt. The results of this work help to better plan the urban development of the city of Casablanca in a smarter way, thus preserving space, agriculture, and the environment while promoting sustainability. In addition, the databases and maps created through this paper aim for a balanced financial management of city expenditures in urban planning. Full article

We conducted a habitat suitability assessment for two burrowing rodents, Anatolian or Nehring’s blind mole rat (Nannospalax xanthodon) and Harting’s vole (Microtus hartingi), on the island of Lesvos using a niche-based approach. We collected data on the presence of the two species across the island and selected several environmental variables, including land cover, geology, and habitat topography, to assess their influence on habitat suitability. We used the Maxent species distribution modelling algorithm to predict suitable habitats. The results showed that both species preferred habitats with low slopes and specific geological substrates, i.e., alluvial deposits and volcanic rocks. M. hartingi showed a preference for open habitats such as saltmarshes and non-irrigated arable land, while N. xanthodon preferred non-irrigated arable land, pastures, and discontinuous urban fabric. The model predicted a wider area of suitable habitats for Microtus hartingi compared to N. xanthodon. Interestingly, the two species are absent from the southeastern part of the island despite our model predicting high suitability; this indicates that a natural barrier of hilly terrain, extensive pine forests, and limestone rock formations may exist that impedes dispersal. Our study provides valuable insights into the habitat preferences of these two burrowing rodents on the island of Lesvos, which can inform biodiversity conservation and ecosystem management strategies. Full article