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Ground subsidence is one of geohazards, particularly in urban environment due to its negative impact to nearby buildings and civil infrastructures. This study investigates the amount and spatial distribution of ground subsidence in Incheon City, South Korea, where ground excavation was made. Interferometric Synthetic Aperture Radar (InSAR) was used to monitor surface deformation around the excavation site. The study analyzed subsidence during an excavation project using Sentinel-1b satellite data, and the InSAR results were compared with traditional leveling methods and field measurements. Notable subsidence during the early construction phase was identified, which demonstrates the effectiveness of InSAR in urban environment. The research contributes to understanding of the excavation-inducing ground subsidence due to the reclamation layer. [ABSTRACT FROM AUTHOR]

Ground subsidence primarily occurs due to complex factors, such as damage to underground facilities and excavation work, and its occurrence can result in loss of life and damage to property. Therefore, factors that induce ground subsidence must be investigated to prevent accidents. This study aims to evaluate and predict the ground subsidence risk in urban centers in South Korea. To this end, a machine learning-based ground subsidence risk prediction model was constructed by utilizing data on the underground facility attribute information, permeability coefficient, stratigraphic thickness, and height. The random forest, XGBoost, and LightGBM machine learning algorithms were used to develop the prediction model, and the SMOTE sampling technique was employed to address data imbalance. The reliability of the developed model was verified using the evaluation metrics of F1-score and accuracy. The best-performing model was selected to create a risk map and visualize the areas with ground subsidence risk. The results indicate that the incorporation of additional data improves model performance and reliability. Thus, the machine learning model with various factors developed in this study offers foundational insights for the prevention and risk management of ground subsidence. [ABSTRACT FROM AUTHOR]

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals' lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas. [ABSTRACT FROM AUTHOR]

As the extraction of mineral resources intensifies, ground subsidence in mining areas has escalated, posing substantial challenges to sustainable development and operational safety. This subsidence, resulting directly from mining activities, significantly compromises the safety of nearby residents by damaging residential structures and infrastructure. Thus, developing precise and dependable methods for predicting ground subsidence is crucial. This study introduces an innovative Cabs-Unet model, which enhances the U-Net architecture by integrating a Convolutional Block Attention Module (CBAM) and Depthwise Separable Convolutions (DSC). This model aims to predict the spatiotemporal dynamics of the Interferometric Synthetic Aperture Radar (InSAR) time series. Employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) technology, we gathered and validated data on ground subsidence at the Pengzhuang coal mine from May 2017 to November 2021, covering 130 scenes, with its accuracy corroborated by levelling survey results. An empirical evaluation of the Cabs-Unet model in two distinct subsidence zones demonstrated superior performance over conventional methods like Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), with Root Mean Square Error (RMSE) values of 1.44 and 1.70, respectively. These findings highlight the model's efficacy in accurately predicting spatiotemporal InSAR ground subsidence. Further predictive analysis using InSAR data indicated an expected increase in subsidence, projecting cumulative declines of −457 mm in Area A and −1278 mm in Area B by 17 July 2022. Our model proves effective in assessing subsidence, promptly detecting potential risks and facilitating the rapid implementation of risk mitigation strategies. [ABSTRACT FROM AUTHOR]

This study addresses the issue of localized ground subsidence and its effect on buried pipelines. Timoshenko beam model, placed on a Pasternak foundation, is used to analyze the internal force response of buried pipelines under foundation subsidence. The load on the pipeline, resulting from localized ground subsidence, is assumed to be symmetric. The load distribution on the buried section of the pipeline is represented using a McLaurin series. Analytical solutions for the deflection and bending moment of the pipeline under arbitrary symmetrical loading are derived based on the theory of elastic foundation beams. Additionally, the accuracy of the analytical solutions is verified through comparisons with experimental studies, finite element analysis, and existing theories. In the analysis, the shear modulus of the Timoshenko beam is set to infinity, resulting in the degeneration of the model into the Euler-Bernoulli beam. The effect of the shear modulus and diameter-span ratio (D/l) of the Timoshenko beam is investigated in the parameter analysis, and the applicability for both beam models is determined. The results indicate that, for buried pipelines with a diameter-span ratio greater than 0.1, the Timoshenko beam model provides more accurate deflection calculations than the Euler-Bernoulli beam model. [ABSTRACT FROM AUTHOR]

Mobile cranes are essential for transporting heavy materials at construction sites, but their operation carries significant safety risks, particularly due to the potential for overturning accidents. These accidents can be classified into two main categories: mechanical accidents, which are caused by factors such as outrigger failure, excessive load weight, and operator skill, and environmental accidents, which arise from ground subsidence due to groundwater and sinkholes. While numerous studies have addressed the causes and prevention of mechanical accidents, there has been a lack of research focusing on the prevention of environmental accidents. This study presents the development of an Electrical Resistivity Measurement System (ERMS) designed to prevent overturning accidents caused by ground subsidence at mobile crane work sites. The ERMS, mounted on a mobile crane, continuously monitors the ground conditions in real time and predicts the likelihood of ground subsidence to prevent accidents. Unlike typical buried electrode methods, the proposed system features a foldable electrode mechanism and a water supply device, thereby making installation and removal more efficient. Furthermore, it uses a ground stability determination algorithm that qualitatively assesses soft ground conditions, which are the primary cause of ground subsidence. The performance of the ERMS was validated through comparisons with commercial equipment, and its applicability was further confirmed through field tests conducted at mobile crane installations. The ERMS is expected to significantly reduce the risk of accidents caused by ground subsidence during mobile crane operations and to contribute to enhancing overall safety in construction environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Ground subsidence is one of geohazards, particularly in urban environment due to its negative impact to nearby buildings and civil infrastructures. This study investigates the amount and spatial distribution of ground subsidence in Incheon City, South Korea, where ground excavation was made. Interferometric Synthetic Aperture Radar (InSAR) was used to monitor surface deformation around the excavation site. The study analyzed subsidence during an excavation project using Sentinel-1b satellite data, and the InSAR results were compared with traditional leveling methods and field measurements. Notable subsidence during the early construction phase was identified, which demonstrates the effectiveness of InSAR in urban environment. The research contributes to understanding of the excavation-inducing ground subsidence due to the reclamation layer.

Objective The spatial distribution of ground subsidence in the coastal area of Tianjin was predicted using a random forest machine learning model, in which the performance and significance of the variables were evaluated. Methods The random forest model was trained and validated in this study using datasets of ground subsidence in 2020, aquifer lithology, water level differences in aquifers in 2020, and hydrogeological parameters. Results The results demonstrate the effectiveness of the random forest model for fitting and predicting ground subsidence (R2=0.98, RMSE=0.52 mm). Moreover, it is found that water level difference emerges as the most influential factor affecting ground subsidence, followed by lithology and hydrogeological parameters. Conclusion The present study introduces several novel contributions: ① utilization of spatial distribution data for training ground subsidence models; ② identification of significant controlling factors based on physical mechanisms; ③ assessment of the relative importance of these controlling factors. Additionally, this paper highlights the limitations and future directions in ground subsidence research, offering valuable insights for the rapid and accurate prediction of ground subsidence using the random forest model.

Abstract Ground subsidence caused by extraction of longwall panels has always been a great concern all over the world. Conventional longwall mining system (CLMS) gives rise to wavy subsidence causing great damage to surface structures. A coal mine in Shanxi, China, utilizes a split-level longwall layout (SLL) for a sub-horizontal No. 8 coal seam to improve the cavability of mudstone interlayer and top coal. This layout, however, also produced unexpectedly favorable surface subsidence. Subsidence of No. 6 and No. 8 longwall panels was monitored while mining was conducted. Field instrumentation and numerical simulation were carried out. It is demonstrated that an asymmetric subsidence profile with stepped subsidence and cracks occurred on the tailgate side but relatively mild and smooth deformation on the other. Due to elimination of conventional parallelepiped gate pillar, No. 6 and No. 8 gobs were connected. Extraction of two SLL panels acted as one supercritical panel. The maximum possible subsidence was reached which lowers the likelihood of potential future secondary subsidence as underground gob fractures and voids have closed. Therefore, SLL is more favorable for post-mining land reuse as gobs are more consolidated underground.

The disturbance of shallow-buried tunnel excavation with large section is the main factor causing the ground subsidence, and the coupling effect between double-line tunnels makes the ground subsidence regularity more complicated. Based on the actual project, the law of ground subsidence and its influencing factors during the excavation of double-line tunnel under different compressive states are discussed by numerical simulation method. The results show that the ground subsidence curve of the double-line tunnel is asymmetrically W-shaped, and the maximum ground subsidence is inclined to the side with larger buried-depth; Under the non-biased state, the axial distance of the double-line tunnel has the most significant effect on the ground subsidence, while under the biased state, the influence factor is the transverse bias angle, and the maximum ground subsidence increases with the increase of the transverse bias angle. Based on the numerical results, three indexes are proposed to quantitatively describe the relationship respectively between the ground subsidence and the excavation section area, the axial distance of the double-line tunnel and the bias angle during the excavation of the double-line tunnel, and the indexes can be used to predict the ground subsidence triggered by the excavation of shallow-buried tunnel under the biased state. Furthermore, the study results can provide reference values for tunnel construction under similar conditions. [ABSTRACT FROM AUTHOR]

The process of land subsidence deals with the removal of excess pore water pressure and the compaction of soil mass under the effect of natural or human factors. The detrimental effects of land subsidence include changes in the morphology of the land surface and the formation of earth fissures, as well as structural and non-structural damage to surface and subsurface infrastructures. In Joshimath on 2nd January 2023, an incidence of ground subsidence occurred which damaged many buildings and infrastructures. This study addresses the exploratory work on rapid visual damage assessment of buildings based on method developed by National Disaster Management Authority (NDMA) and European Macroseismic Scale (EMS) − 98. The building vulnerability was assessed using building attributes like typology, number of storeys, area, construction materials, occupancy, configuration, construction practice etc. The damage attributes considered are based on siting issues, soil and foundation conditions, architectural features and elements, structural aspects and components, material & construction details, crack monitoring etc. In the critical buildings, cracks were monitored using crack meters. This study concludes out of total 2364 building surveyed, 37%, 42%, 20%, 1% buildings fall under "Usable", "Further Assessment", "Unusable", "to be demolished", grades respectively. [ABSTRACT FROM AUTHOR]

Ground subsidence caused by extraction of longwall panels has always been a great concern all over the world. Conventional longwall mining system (CLMS) gives rise to wavy subsidence causing great damage to surface structures. A coal mine in Shanxi, China, utilizes a split-level longwall layout (SLL) for a sub-horizontal No. 8 coal seam to improve the cavability of mudstone interlayer and top coal. This layout, however, also produced unexpectedly favorable surface subsidence. Subsidence of No. 6 and No. 8 longwall panels was monitored while mining was conducted. Field instrumentation and numerical simulation were carried out. It is demonstrated that an asymmetric subsidence profile with stepped subsidence and cracks occurred on the tailgate side but relatively mild and smooth deformation on the other. Due to elimination of conventional parallelepiped gate pillar, No. 6 and No. 8 gobs were connected. Extraction of two SLL panels acted as one supercritical panel. The maximum possible subsidence was reached which lowers the likelihood of potential future secondary subsidence as underground gob fractures and voids have closed. Therefore, SLL is more favorable for post-mining land reuse as gobs are more consolidated underground. [ABSTRACT FROM AUTHOR]

ABSTRACTGround subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is proposed and applied to assess ground subsidence risk of the Shanghai Metro network. Initially, PS-InSAR is used for the ground subsidence survey in the Shanghai Metro area. Subsequently, ten subsidence causal factors are collected, and the LightGBM machine learning algorithm is employed to conduct the ground subsidence susceptibility analysis. Then, a risk matrix is introduced to define ground subsidence risk by combining subsidence intensity and susceptibility. Finally, the risk map is generated in ArcGIS and classified into five levels. The assessment results were used to identify ground subsidence risk at different scales. The results indicate that the risk is higher in the southwest part of the study area, and the ground subsidence risk of the metro network exhibits a regional-related characteristic. On-site investigations were conducted to verify the results. The method enables fast ground subsidence assessment over a large area at a low cost and the assessment results can provide data for the prevention and management of ground subsidence hazards in the city.

The Raniganj coalfield, which is the oldest coal mine in India, is susceptible to ground subsidence. For the purpose of detecting surface deformation, this work makes use of field surveys, interferometric synthetic aperture radar (InSAR), and Differential GPS (DGPS). The study utilised Sentinel-1 InSAR data spanning from 2017 to 2023. PS-InSAR was used for both ascending and descending datasets, while SBAS-InSAR was used for descending datasets alone. Records show a maximum subsidence rate of −21.18 mm/year. Three surface deformation maps were generated from time-series assessments of individual locations, revealing regions undergoing significant changes. Known mining collapse locations and continuing deformation zones were designated with four differential GPS stations. By analysing the DGPS data with the GAMIT/GLOBK software, we were able to measure surfaces that were undergoing rapid deformation. Using Google Earth Engine (GEE), we generated thermal maps to delve deeper into the coal fire activity. We found coal bed methane (CBM) mining causing substantial subsidence in Kataberia and the surrounding areas. Shyamsundarpur and New Kenda are impacted by mining voids and coal fire-induced subsidence, respectively. The results of this study give the Raniganj region’s decision-making procedures more credibility in terms of minimising and controlling geological dangers.

According to studies by relevant scholars, the ground subsidence rate in Sanhe City, Hebei, increased gradually from 2003 to 2016, with the most severe subsidence observed in the Yanjiao area , which has become contiguous with the subsidence area in Tongzhou, Beijing. However, the development trend of ground subsidence disasters in Sanhe City after 2016 remains unclear. With the implementation of national policies such as the integration of Beijing-Tianjin-Hebei region and the construction of Beijing’s sub-center, it is crucial to identify the development and evolution characteristics of land subsidence disasters in Sanhe City and analyze their causes to ensure the urban safety and sustainable development of Sanhe City. In this context, the authors used SBAS-InSAR technology to interpret the evolution characteristics of ground subsidence in Sanhe City from 2018 to 2020, and analyzed several inducing factors that led to land subsidence in Sanhe City, summarizing the main causes. Through this study, the spatial distribution and evolutionary characteristics of land subsidence disasters in Sanhe City were grasped: the western part of the city exhibits severe subsidence, while the eastern part is less affected, with a focus on the overall characteristics of key subsidence areas. Two main subsidence areas were identified, namely, the Yanjiao Town subsidence area with three subsidence funnels and the DuanJialing town subsidence area with one subsidence funnel, with the former being the most severely affected. From 2018 to 2020, the overall trend of ground subsidence disasters in Sanhe City showed a slowdown. Through comparative analysis, it was determined that severe overexploitation of groundwater, soil properties, urbanization development, and population growth are the main factors contributing to the occurrence and development of ground subsidence disasters in Sanhe City. The research results of this paper will provide reference for the prevention and control of ground subsidence disasters in the region.

Abstract The southeast Texas (SETX) coastal area, owing to its unique geographical location and geological attributes, is facing a spectrum of geological challenges, such as ground subsidence, flooding, and coastal erosion. This study endeavors to evaluate the recent instances of ground subsidence and their associated rates, focusing on comprehending their implications for flooding within SETX. Employing the Persistence Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, this study employs Sentinel-1 SAR satellite data with descending orbit observations spanning from January 2020 to March 2023. Our findings indicate that both the northwestern and eastern regions of Houston have been settled with rates up to 2 cm/year. This settlement trend is consistent with data derived from GPS and groundwater level measurements. This investigation explicitly shows the substantial temporal and spatial variations in subsidence rates, predominantly influenced by localized groundwater extraction due to urbanization (e.g., population growth, land development, etc.). By advocating for the integration of InSAR, GPS, and groundwater measurements, this research aspires to make valuable contributions toward the mitigation of subsidence and flood-related hazards in the SETX area.

Coniolo and Brusaschetto, are two small towns located in the Monferrato area of the Alessandria Province, northern Italy. These communities have similar histories related to development and subsequent abandonment of marl quarry activity that began more than a century ago and continued until recently. Quarrying occurred until soil conditions, water infiltration, and excessive depth made cost of extracting and7 lifting material prohibitive. Quarries consisted of tunnels located directly beneath the towns at about 150 m below ground surface. Collapse of the tunnels led to surface subsidence and destruction of overlying homes and much of the municipal infrastructure. In the early Twentieth Century, regulations pertaining to mine and quarry safety were typically deficient, entirely absent, or not followed. Extractive activities of non-energy mineral resources from quarries and mines were and continue to be widespread in Italy, which currently ranks fifth among what are now countries of the European Union (EU). Mining sites are present in all regions of Italy, particularly in the northern part of the country and along coasts, often in areas of geohydrogeological risk. Consequences of anthropogenic pressures that alter the natural environment, such as the physical size of aquifer drawdowns, are linked to issues for a number of extractive sites across the country. This report analyzes historical and technical documents, conducts a geomorphological analysis of hilly slopes surrounding these communities, and examines urban planning and geophysical surveys to determine the impact of subsurface quarrying activities on the overlying ground surface. The study highlights significant problems that are applicable to other localities globally. This research demonstrates: (a) the importance of geological considerations to development and abandonment of mining activity in inhabited areas; (b) the importance of establishing and following safety protocols; and (c) the manner in which economic interests can take precedence over the well-being and lives of those employed to extract resources. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details. [ABSTRACT FROM AUTHOR]

Within the framework of the elastoplastic model, the authors propose a theoretical description procedure for troughs of ground movements with high gradients of subsidence. The approach uses reduction of shear modulus in rock mass bordering a movement trough. The pre-requisite for the shear modulus reduction in such areas is the increased mining-induced pressure, which initiates damage and aggravates the defect structure of rocks through widening of micro-cracks, inter-grain contacts, etc. The estimation procedure of the induced reduction factor for the shear modulus uses the comparative analysis of ground slopes. This approach enables a reliable calculation of ground movements with high gradients of subsidence, and ensures the adequate stress–strain analysis of undermined rock mass, including location of plastic deformation zones. [ABSTRACT FROM AUTHOR]

According to studies by relevant scholars, the ground subsidence rate in Sanhe City, Hebei, increased gradually from 2003 to 2016, with the most severe subsidence observed in the Yanjiao area, which has become contiguous with the subsidence area in Tongzhou, Beijing. However, the development trend of ground subsidence disasters in Sanhe City after 2016 remains unclear. With the implementation of national policies such as the integration of Beijing-Tianjin-Hebei region and the construction of Beijing's sub-center, it is crucial to identify the development and evolution characteristics of land subsidence disasters in Sanhe City and analyze their causes to ensure the urban safety and sustainable development of Sanhe City. In this context, the authors used SBAS-InSAR technology to interpret the evolution characteristics of ground subsidence in Sanhe City from 2018 to 2020, and analyzed several inducing factors that led to land subsidence in Sanhe City, summarizing the main causes. Through this study, the spatial distribution and evolutionary characteristics of land subsidence disasters in Sanhe City were grasped: the western part of the city exhibits severe subsidence, while the eastern part is less affected, with a focus on the overall characteristics of key subsidence areas. Two main subsidence areas were identified, namely, the Yanjiao Town subsidence area with three subsidence funnels and the DuanJialing town subsidence area with one subsidence funnel, with the former being the most severely affected. From 2018 to 2020, the overall trend of ground subsidence disasters in Sanhe City showed a slowdown. Through comparative analysis, it was determined that severe overexploitation of groundwater, soil properties, urbanization development, and population growth are the main factors contributing to the occurrence and development of ground subsidence disasters in Sanhe City. The research results of this paper will provide reference for the prevention and control of ground subsidence disasters in the region. [ABSTRACT FROM AUTHOR]

The southeast Texas (SETX) coastal area, owing to its unique geographical location and geological attributes, is facing a spectrum of geological challenges, such as ground subsidence, flooding, and coastal erosion. This study endeavors to evaluate the recent instances of ground subsidence and their associated rates, focusing on comprehending their implications for flooding within SETX. Employing the Persistence Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, this study employs Sentinel-1 SAR satellite data with descending orbit observations spanning from January 2020 to March 2023. Our findings indicate that both the northwestern and eastern regions of Houston have been settled with rates up to 2 cm/year. This settlement trend is consistent with data derived from GPS and groundwater level measurements. This investigation explicitly shows the substantial temporal and spatial variations in subsidence rates, predominantly influenced by localized groundwater extraction due to urbanization (e.g., population growth, land development, etc.). By advocating for the integration of InSAR, GPS, and groundwater measurements, this research aspires to make valuable contributions toward the mitigation of subsidence and flood-related hazards in the SETX area. [ABSTRACT FROM AUTHOR]

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the construction economy in Foshan City has led to increasingly apparent geological phenomena of ground subsidence, causing damage to buildings and infrastructures, lowering groundwater level, soil degradation, and environmental pollution. These issues have severely constrained the economic development of Foshan City. This paper utilized SBAS-InSAR technology to monitor ground deformation in Foshan City over the 2017–2022 period and identified 214 potential subsidence hazards. Additionally, integrating extensive surface deformation data from a long time series establishes a risk assessment index system for ground subsidence, primarily driven by construction activities. Further, the mechanism of the ground subsidence was analyzed systematically. Meanwhile, introducing the analytic hierarchy process method (AHP) to assess the risk of Foshan City, the results showed that the proportion of area from high-risk to low-risk zones is 8.55%, 8.64%, 11.79%, 22.38% and 48.64%, respectively. Moreover, this paper proposed corresponding measures to prevent and control ground subsidence. The evaluation results can provide a scientific reference for future geological disaster prevention and management in Foshan City.

Ground subsidence primarily occurs due to complex factors, such as damage to underground facilities and excavation work, and its occurrence can result in loss of life and damage to property. Therefore, factors that induce ground subsidence must be investigated to prevent accidents. This study aims to evaluate and predict the ground subsidence risk in urban centers in South Korea. To this end, a machine learning-based ground subsidence risk prediction model was constructed by utilizing data on the underground facility attribute information, permeability coefficient, stratigraphic thickness, and height. The random forest, XGBoost, and LightGBM machine learning algorithms were used to develop the prediction model, and the SMOTE sampling technique was employed to address data imbalance. The reliability of the developed model was verified using the evaluation metrics of F1-score and accuracy. The best-performing model was selected to create a risk map and visualize the areas with ground subsidence risk. The results indicate that the incorporation of additional data improves model performance and reliability. Thus, the machine learning model with various factors developed in this study offers foundational insights for the prevention and risk management of ground subsidence.

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas.

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ground deformation associated with naturaland anthropogenic activities can be damaging for infrastructure and can cause enormous economic loss, particularly in developing countries which lack measuring instruments. Remote sensing techniques like interferometric synthetic aperture radar (InSAR) can thus play an important role in investigating deformation and mitigating geohazards. Rawalpindi and Islamabad are twin cities in Pakistan with a population of approximately 5.4 million, along with important government and private entities of national and international interest. In this study, we evaluate rapid paced subsidence in this area using a modified small baseline subset technique with Sentinel-IA imagery acquired between 2015 and 2022. Our results show that approximately 50 mm/year subsidence occurs in the older city of Rawalpindi, the most populated zone. We observed that subsidence in the area is controlled by the buried splays of the Main Boundary Thrust, one of the most destructive active faults in the recent past. We suggest that such rapid subsidence is most probably due to aggressive subsurface water extraction. It has been found that, despite provision of alternate water supplies by the district government, a very alarming number of tube wells are being operated in the area to extract ground water. Over 2017-2021, field data showed that near-surface aquifers up to 50-60 m deep are exhausted, and most of the tube wells are currently extracting water from depths of approximately 150-160 m. The dropping water level is proportional to the increasing number of tube wells. Lying downstream of tributaries originating from the Margalla and Murree hills, this area has a good monsoon season, and its topography supports recharge of the aquifers. However, rapid subsidence indicates a deficit between water extraction and recharge, partly due to the limitations inherent in shale and the low porosity near the surface lithology exposed in the area. Other factors amplifying the impacts are fast urbanization, uncontrolled population growth, and non-cultivation of precipitation in the area. [ABSTRACT FROM AUTHOR]

This article assesses the causes behind the ongoing subsidence-induced instabilities in Dar village, which is situated over deposits of palaeo-landslides in Kumaun Himalaya. A detailed field investigation has been carried out in and around the study area to identify the indications of subsidence there and to understand the factors responsible for the activation of the same. Subsequently, the geotechnical characterisations of overburden slope material and numerical slope stability assessment have been performed to substantiate the field observations. It has been found that the village is situated on a slope comprising weak material ~90% finer particles (sand, silt and clay) rich in disintegrated micaceous sediments having low cohesion and plasticity index. Including this, the physiographical characteristics of the slope make it vulnerable to mass movement. The slope stability assessment also indicated the critical state of the slope with a factor of safety 1.05, posing vulnerability to sliding failure. However, it is evident that modification of slope geometry with the construction of the road from Sobla to Tidang, which passes through the base of the village, and consequently, modification of the hydrological characteristics in the study area are the most important contributing factors to the ongoing subsidence in Dar. Since the village is at risk of a possible disaster, preliminary mitigation measures are also proposed, which will be helpful in minimising the risk to life and property. [ABSTRACT FROM AUTHOR]

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area. [ABSTRACT FROM AUTHOR]

Mobile cranes are essential for transporting heavy materials at construction sites, but their operation carries significant safety risks, particularly due to the potential for overturning accidents. These accidents can be classified into two main categories: mechanical accidents, which are caused by factors such as outrigger failure, excessive load weight, and operator skill, and environmental accidents, which arise from ground subsidence due to groundwater and sinkholes. While numerous studies have addressed the causes and prevention of mechanical accidents, there has been a lack of research focusing on the prevention of environmental accidents. This study presents the development of an Electrical Resistivity Measurement System (ERMS) designed to prevent overturning accidents caused by ground subsidence at mobile crane work sites. The ERMS, mounted on a mobile crane, continuously monitors the ground conditions in real time and predicts the likelihood of ground subsidence to prevent accidents. Unlike typical buried electrode methods, the proposed system features a foldable electrode mechanism and a water supply device, thereby making installation and removal more efficient. Furthermore, it uses a ground stability determination algorithm that qualitatively assesses soft ground conditions, which are the primary cause of ground subsidence. The performance of the ERMS was validated through comparisons with commercial equipment, and its applicability was further confirmed through field tests conducted at mobile crane installations. The ERMS is expected to significantly reduce the risk of accidents caused by ground subsidence during mobile crane operations and to contribute to enhancing overall safety in construction environments.

In recent years, land subsidence issues have become relatively prominent in the northern plain area of Anhui province, and there is lack of quantitative research on the driving forces of regional land subsidence. In order to further investigate the developmental characteristics of subsidence disasters and provide scientific, this paper takes Bozhou City as an example. Based on 62 scenes of Sentinel-1 data, SBAS-InSAR technology is employed to obtain the spatial-temporal distribution characteristics of land subsidence from October 2021 to October 2022. Additionally, a geographic weighted regression model is applied to explore the main driving factors of land subsidence in Bozhou city. The research results indicate: (1) The main subsidence rate in Bozhou City ranges from 5 to 30 mm/year,with an average subsidence rate of 5.7 mm /year. (2) The most serious subsidence area is located north of Gongji Temple Town in Woyang County, with an amplitude of 84.3 mm/year, mainly caused by coal mining. In non-coal mining subsidence areas, the maximum subsidence rate is 25.8 mm/year, located in the northeast of Qiaocheng District. (3) The contribution order of various driving factors to ground subsidence is as follows: fluctuation of deep water level, fluctuation of middle-deep water level, burial depth of middle-deep groundwater, burial depth of deep groundwater, GDP per unit area, thickness of loose layer, road density, and population density. The study results can provide basic data support for geological disaster prevention and control.

Ground deformation associated with natural and anthropogenic activities can be damaging for infrastructure and can cause enormous economic loss, particularly in developing countries which lack measuring instruments. Remote sensing techniques like interferometric synthetic aperture radar (InSAR) can thus play an important role in investigating deformation and mitigating geohazards. Rawalpindi and Islamabad are twin cities in Pakistan with a population of approximately 5.4 million, along with important government and private entities of national and international interest. In this study, we evaluate rapid paced subsidence in this area using a modified small baseline subset technique with Sentinel-1A imagery acquired between 2015 and 2022. Our results show that approximately 50 mm/year subsidence occurs in the older city of Rawalpindi, the most populated zone. We observed that subsidence in the area is controlled by the buried splays of the Main Boundary Thrust, one of the most destructive active faults in the recent past. We suggest that such rapid subsidence is most probably due to aggressive subsurface water extraction. It has been found that, despite provision of alternate water supplies by the district government, a very alarming number of tube wells are being operated in the area to extract ground water. Over 2017–2021, field data showed that near-surface aquifers up to 50–60 m deep are exhausted, and most of the tube wells are currently extracting water from depths of approximately 150–160 m. The dropping water level is proportional to the increasing number of tube wells. Lying downstream of tributaries originating from the Margalla and Murree hills, this area has a good monsoon season, and its topography supports recharge of the aquifers. However, rapid subsidence indicates a deficit between water extraction and recharge, partly due to the limitations inherent in shale and the low porosity near the surface lithology exposed in the area. Other factors amplifying the impacts are fast urbanization, uncontrolled population growth, and non-cultivation of precipitation in the area.

Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized to develop a model describing the velocity of ground subsidence due to underground coal mining. Based on the subsidence velocity model, a dynamic subsidence model is established by taking an integral of the velocity model. Coefficients of the model, which depend on maximum subsidence, maximum subsidence velocity and the time corresponding to the maximum subsidence velocity, are related to the geological and mining conditions of the coal seam being investigated. A Levenberg–Marquardt-algorithm-based method is also proposed to calculate the optimal model coefficients based on subsidence velocity observations. Four continuously operating Global Navigation Satellite System (GNSS) stations were constructed above a typical longwall coal mining working face in the Jining mining area, China. These GNSS stations collected subsidence observations over two years, which were used to validate the developed prediction model. The results show that the root-mean-square (RMS) of the model-predicted ground subsidence error is 56.1 mm, and the maximum relative error is 2.5% for all four GNSS stations, when the ground subsidence is less than 6000 mm. [ABSTRACT FROM AUTHOR]

This research evaluates the coastal vulnerability of Shanghai, evaluates the effectiveness of existing natural habitats in reducing that vulnerability, and, finally, provides recommendations to improve the resilience of the coastal areas. Shanghai is an important economic center that is home to a large population. However, the combination of ground subsidence, rising sea levels, and more frequent coastal flooding due to tropical cyclones poses escalating climate risks for Shanghai, demanding urgent mitigation measures. The InVEST Coastal Vulnerability Model was used in this study to assess Shanghai's coastal vulnerability under the current situation and various scenarios that simulated the absence of natural habitats. The assessment results were analyzed through a comparison between different scenarios and spatial aggregation analysis. This study pinpointed highly vulnerable areas, primarily located on the east coast of Chongming Island, the east and northeast coasts of Hengsha Island, and the east coast of the mainland of Shanghai. These areas need to be prioritized for intervention. Also, it demonstrated the effectiveness of existing natural habitats in reducing coastal vulnerability, with large green spaces and salt marshes playing a greater role compared to small green spaces. This is the first study applying the InVEST Coastal Vulnerability Model to Shanghai, demonstrating the model's potential in providing valuable information regarding coastal protection against the impacts of climate change in Shanghai. Insights from the findings of this study are useful in crafting sustainable land-use policies and plans for Shanghai. [ABSTRACT FROM AUTHOR]

We develop a moving-mesh permafrost model that contains a ground subsidence computation module to estimate the effects of ground subsidence on permafrost simulation under different warming scenarios. Including the ground subsidence process in the permafrost simulation produces only a relatively small improvement in the simulation performance of the ground temperature field, as validated by observations from two sites on the Qinghai–Tibetan Plateau (QTP). It is shown that ignoring ground subsidence tends to achieve a larger active layer thickness (ALT) but a smaller original thickness of permafrost that has thawed when simulating permafrost changes in a warming climate. The heat consumed by permafrost changes will be underestimated in simulations that do not consider ground subsidence. The effects that ground subsidence exerts within the permafrost simulation are clearly demonstrated under a strong warming scenario, which will influence the global energy budget. Projections indicate that the permafrost in the continuous permafrost area of the QTP may be close to the phase transition temperature to become zero thermal gradients in 2030–2040 under the SSP5-8.5 scenario, and there will be a great risk of ground subsidence by that stage. For permafrost regions with rich ground ice, the downward propagating temperature signals caused by ground subsidence are more attenuated. However, the heat calculation error will be larger in a simulation that does not consider ground subsidence there. This study quantifies the effects of ground subsidence, which can provide a better understanding of the permafrost thaw and energy budget of the QTP. [ABSTRACT FROM AUTHOR]

To further accurately analyze the mechanical response of buried pipelines and pavement structures subjected to ground subsidence, this study established a dual-beam model consisting of an upper Euler–Bernoulli beam and a lower Euler–Bernoulli beam to simulate pavement structures and pipelines. The embankment fill layer and foundation soil were idealized as in the Winkler foundation model, which was divided into three zones. Coupling differential equations for the behavior of this dual-beam–foundation system were established according to the force equilibrium among the pavement structure, buried pipeline, embankment fill, and foundation soil with voids. The stress distribution and deformation of buried pipelines and pavement structures were solved according to the boundary and geometric conditions. The proposed mechanical model was evaluated on test data and corresponding finite element codes, concretely verifying the model's effectiveness, and a parametric study was proposed to investigate the behavior of this system, such as the bending stiffness of pipelines, traffic load, the strength of fill layers, and foundation soil. [ABSTRACT FROM AUTHOR]

Coniolo and Brusaschetto, are two small towns located in the Monferrato area of the Alessandria Province, northern Italy. These communities have similar histories related to development and subsequent abandonment of marl quarry activity that began more than a century ago and continued until recently. Quarrying occurred until soil conditions, water infiltration, and excessive depth made cost of extracting and7 lifting material prohibitive. Quarries consisted of tunnels located directly beneath the towns at about 150 m below ground surface. Collapse of the tunnels led to surface subsidence and destruction of overlying homes and much of the municipal infrastructure. In the early Twentieth Century, regulations pertaining to mine and quarry safety were typically deficient, entirely absent, or not followed. Extractive activities of non-energy mineral resources from quarries and mines were and continue to be widespread in Italy, which currently ranks fifth among what are now countries of the European Union (EU). Mining sites are present in all regions of Italy, particularly in the northern part of the country and along coasts, often in areas of geohydrogeological risk. Consequences of anthropogenic pressures that alter the natural environment, such as the physical size of aquifer drawdowns, are linked to issues for a number of extractive sites across the country. This report analyzes historical and technical documents, conducts a geomorphological analysis of hilly slopes surrounding these communities, and examines urban planning and geophysical surveys to determine the impact of subsurface quarrying activities on the overlying ground surface. The study highlights significant problems that are applicable to other localities globally. This research demonstrates: (a) the importance of geological considerations to development and abandonment of mining activity in inhabited areas; (b) the importance of establishing and following safety protocols; and (c) the manner in which economic interests can take precedence over the well-being and lives of those employed to extract resources.

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details.

Expansive clay soils are subject to considerable volume changes due to seasonal variations in soil–water content. Its shrink–swell characteristics can lead to differential movements, which can cause damage to lightly weighted structures and pavements. The damage can be severe with the presence of trees, as the root system would take a significant quantity of water from the soil, resulting in restricted shrinkage settlement. This paper presents a case study of tree-root-induced subsidence and cracks in the surface of a netball court located in an eastern suburb of Melbourne, Australia. A geotechnical investigation was conducted, and four boreholes were drilled close to and sufficiently away from a group of Australian native Eucalypts (Corymbia maculata). The daily water use of the selected tree was monitored using a sap flow instrument. The soil suction and water content profile were developed based on the measurement results, indicating that the severe soil drying developed at 1.5 m below the surface of the netball court, where the soil was the driest up to 1.0 m located approximately 7.6 m from the tree group. A contour map was delineated based on the level survey carried out around the cracking surface of the court, and the results revealed that the crack is most likely attributed to the soil settlement caused by the drying effect of tree roots. [ABSTRACT FROM AUTHOR]

Displacement prediction of transmission towers is essential for the early warning of transmission network deformation. However, there is still a lack of prediction on the ground subsidence of the tower foundation. In this study, we first used the multi-temporal interferometric synthetic aperture radar (MT-InSAR) approach to acquire time series deformation for the transmission lines in the Salt Lake area. Based on the K-shape clustering method and field investigation results, towers #95 and #151 with representative foundation deformation characteristics were selected for displacement prediction. Combined with field investigations and the characteristics of saline soil in the Salt Lake area, the trigger factors of transmission tower deformation were analyzed. Then, the displacement and trigger factors of the transmission tower were decomposed by variational mode decomposition (VMD), which could closely connect the characteristics of the foundation saline soil with the influence of the trigger factors. To analyze the contribution of each trigger factor, the maximum information coefficient (MIC) was quantified, and the best choice was made. Finally, the hyperparameters of the long short-term memory (LSTM) neural networks were optimized using a convolutional neural network (CNN) and the grey wolf optimizer (GWO). The findings reveal that the refined deep learning models outperform the initial model in generalization potential and prediction precision, with the CNN–LSTM model demonstrating the highest accuracy in predicting the total displacement of tower #151 (RMSE and R2 for the validation set are 0.485 and 0.972, respectively). Given the scant research on the multifactorial influence on the ground subsidence displacement of transmission towers, this study's methodology offers a novel perspective for monitoring and early warning of ground subsidence disasters in transmission networks. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)