Subsidence Research Articles

Spatiotemporal Relationship Between Land Subsidence and Ecological Environmental Quality in Shenfu Mining Area, Loess Plateau, China

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence.

Temporal gravimetry, campaign and permanent GNSS, and interferometric radar techniques: A comparative approach to quantifying land deformation rates in coastal Texas.

Land subsidence and local sea-level rise are well-known on-going hazards that negatively impacting coastal regions, exacerbating coastal flooding, threatening infrastructure stability, accelerating erosion, and amplifying the risks of inundation and property damage. This study used four techniques to quantify land deformation rates in the Texas Coastal Bend region and to investigate the controlling factors on two different spatiotemporal scales: (1) local scale, where biweekly temporal gravity and campaign Global Navigation Satellite System (GNSS) measurements were acquired at six locations over 2 years (October 2020-September 2022); and (2) regional scale, where vertical displacement time series were extracted from the Interferometric Synthetic Aperture Radar (InSAR) and 15 permanent GNSS stations over 5 years (January 2017-November 2021). The observed inconsistency between land deformation rates derived from gravity (range: -33.32 ± 149.30 to +338.68 ± 107.93 mm/yr) and campaign GNSS (range: -17.91 ± 14.90 to +5.69 ± 9.93 mm/yr) surveys could be attributed to the short campaign period and the infrequency of data acquisition. The InSAR-derived deformation rates (range: -12.16 ± 0.12 to +11.70 ± 0.00 mm/yr) were consistent with the permanent GNSS-derived rates (range: -14.0 ± 0.6 to +9.0 ± 3.0 mm/yr); both indicated spatiotemporal variability in land deformation rates across the Texas Coastal Bend. A total of 4 coastal towns (-2.3 ± 3.3 mm/yr), 3 cities (-1.4 ± 2.6 mm/yr), 3 inland towns (-3.6 ± 4.2 mm/yr), and 7 industrial plants (-3.8 ± 5.8 mm/yr) were found to have significant land subsidence rates due to sediment compaction, growth faulting, oil/gas extraction, and groundwater extraction. Results of this study emphasize the advantages and limitations of four different techniques in quantifying land deformation rates; can enhance estimates of local sea-level rise; and offer valuable insights to support coastal communities in mitigating the impacts of land subsidence and improving their resilience.

Machine learning approach for detection of land subsidence induced by underground coal fire using multi-sensor satellite data

ABSTRACT High-rank coal reserves in Jharia Coalfield (JCF, India), are invariably associated with underground coal fires and land subsidence. This study explores multi-sensor time series satellite data (Landsat 8 OLI and Sentinel-1) through machine learning (ML) to determine the regional ground deformation accompanying coal fires and their contextual relationship. The results show that the highest degree of subsidence is closely associated with the active mine benches with overburden dumps. The relationship between the coal fire and land subsidence parameters is considered as a binary classification problem, explored by calculating the probability of subsidence with a desirable categorical outcome through different ML models. The accuracy of the models is validated using performance metrics that shows that the Random Forest (RF) metrics predict the probability of deformation locations in response to the volume reduction of the burning coal fire and vertical compression due to Overburden Dump (OBD) near active mine benches. The estimated displacement trends have been used to forecast the Autoregressive Integrated Moving Average (ARIMA) method, estimated using Line-of-Sight (LOS) displacement values vary around the best fit within the 95% confidence limits. The trend shows ∼15–25% increase in subsidence compared to the cumulative subsidence.

Tunnel-induced land subsidence assessment in a densely populated residential area using Sentinel-1 PS-InSAR

Land subsidence detection in residential areas based on advanced remotely sensed data is an effective technique to mitigate catastrophic disasters in time. Synthetic aperture radar (SAR) in spaceborne platforms is a highly sophisticated technique to estimate ground surface subsidence with millimeter accuracy for wide-area observation. SAR is beneficial for large-scale social infrastructure project planning and monitoring in densely populated residential areas. In this study, we applied a permanent scatterer (PS) interferometric SAR (PS-InSAR)-based technique for monitoring land subsidence in a tunnel construction site in the Greater Tokyo Area to investigate tunnel-induced ground surface subsidence. Sentinel-1 repeat-pass SAR data in ascending and descending orbital directions were used for continuous observation. Consecutively estimated interferograms based on repeat-pass SAR data were generated to monitor and detect tunnel-induced land subsidence as well as confirm long-term stability along the tunnel excavation path. A bidirectional displacement of the ground area parallel to the earth’s surface in the east–west direction and perpendicular to the earth’s surface in the up–down direction was estimated at a high-phase consistency location using a PS interferometric stacking technique. The resultant data were then analyzed to detect anomalies in the ground area in the spatiotemporal domains by performing displacement time-series analysis. An area near Higashi-Tsutsujigaoka’s local administrative region in the metropolitan center of Tokyo was observed to have 15-mm subsidence and 30-mm displacement in the east–west direction during August–October 2020. The conventional level survey results in the same ground area showed 15–20-mm subsidence above the tunnel excavation path. The findings of this study offer insights into understanding land subsidence in residential environments.

Geo-Fenced Aerial Fire Response System

Purpose: We aim to create a prototype of a fire extinguisher drone equipped with advanced geo-fencing technology explicitly designed for use in high-rise buildings. This drone will be capable of both ground movement and flight, allowing it to quickly navigate narrow spaces and difficult-to-reach areas. Methodology: The study involves an in-depth analysis of various drones, features, and geo-fencing to develop a prototype for precise and targeted fire suppression. The aim is to minimize the damage caused by fires and improve overall safety by enabling swift intervention, particularly in confined or elevated locations where traditional firefighting methods may face challenges. Findings: The prototype drone, equipped with geo-fencing capabilities, has demonstrated its ability to be efficiently deployed within specific, pre-defined areas, significantly improving its precision and effectiveness in targeted fire suppression. This feature ensures that the drone operates only within designated zones, reducing the risk of interference or wandering into unsafe areas. The drone is controlled remotely, allowing it to fly freely and access hard-to-reach locations quickly. A small surveillance camera mounted on the drone enhances its effectiveness by providing real-time visuals of the fire's location and intensity. This capability allows operators to assess the situation more accurately and deploy the necessary response measures, making the drone a powerful tool for modern firefighting operations. Unique Contribution to Theory, Practice and Policy: The findings from this prototype provide valuable insights that can shape the future of fire response mechanisms and drone designs. The drones can target fires while minimizing risks to firefighting personnel, and they can potentially revolutionize emergency response strategies. The ability to remotely control drones, equipped with geo-fencing and surveillance capabilities, reduces the need for human intervention in dangerous situations and improves the speed and accuracy of fire suppression efforts. Furthermore, this technology can be applied to detect and combat wildfires, helping to minimize their destructive impact by enabling quicker detection and targeted responses in remote or difficult-to-access areas. These advancements pave the way for safer and more efficient firefighting methods in the future.

Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System

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.

Regional subsidence monitoring and prediction along high-speed railways based on PS-InSAR and LSTM

The rapid construction of high-speed railways in China has forced more and more routes to pass through slow subsidence zones caused by groundwater extraction or underground mining. It is crucial to carry out deformation monitoring and prediction those areas along high-speed railways to ensure the safe operation. Twenty-nine periods of Sentinel-1A data between Taiyuan South Station and Taigu East Station were first processed using the PS-InSAR technique, and the ground subsidence sequences at ten typical points were selected. Then the Variational modal decomposition (VMD) was combined with the Adaptive Boosting Algorithm (AdaBoost), and the VMD-LSTMAda-LSTMAda model was constructed by combining the long short-term memory (LSTM) model for the prediction of regional ground subsidence along the high-speed railway. The results show that the cumulative subsidence within the 200 m buffer of the line centreline is − 37.58 − 89.19 mm, and the annual average subsidence rate is − 26.57 − 82.63 mm/y. The proposed model can reduce the complexity of the deformation sequence and combines with AdaBoost to improve the prediction accuracy of the LSTM model. It performs well in terms of RMSE, MAE, MAPE, and R2 at the two feature points (3703: RMSE = 0.82 mm, MAE = 0.25 mm, MAPE = 6.31%, and R2 = 0.94; 522: RMSE = 1.32 mm, MAE = 0.46 mm, MAPE = 4.92%, R2 = 0.95). The model improves the accuracy of regional subsidence prediction along the high-speed railway.

Estimating the economic vulnerability of 2020–2099 storm flooding with sea level rise on Coastal Virginia

Estimating the economic impact of flooding is an essential element of flood risk management. Policy interventions to mitigate the impact of flooding must be balanced against the financial and economic costs of flooding. Using projections of sea level rise for 2040, 2060, and 2080 and estimates of flooding damage that are equaled or exceeded 10-, 50-, 100-, and 500-year period, we investigate the financial costs of flooding across eight planning districts in Coastal Virginia. We briefly discuss the methodology to estimate the average annualized loss (AAL) given discrete events and likelihoods and how these AALs flow into the estimation of the net present value of flooding in Coastal Virginia. The Hampton Roads metropolitan area accounts for approximately 80.7% of estimated nominal losses currently, rising to 88.1% by 2080 due to sea level rise. This vulnerability is linked to poorly planned development, population growth, ground subsidence, and economic inequities. The present value of unmitigated flooding ranges from $20.0 billion to $58.7 billion in 2021 dollars, depending on the discount rate. Discounted economic impacts for unmitigated sea level rise could reach $82.9 billion for Coastal Virginia this century. The concentration of costs in Hampton Roads, which produces 20% of Virginia's GDP, underscores the need for targeted economic and security policies.

Sustainability Nexus AID: landslides and land subsidence

Landslides and land subsidence pose significant threats that are both existing and growing in nature. These complex phenomena should not be considered in isolation but rather as interconnected challenges. To effectively understand and mitigate them, a data-driven nexus approach is necessary. Recognizing the importance of addressing this issue comprehensively, the United Nations University has launched the Sustainability Nexus Analytics, Informatics and Data Programme, a comprehensive initiative that intends to enable the nexus approach to problem solving in coupled human–environment systems. This paper provides a detailed background on the Programme’s “Landslides and Land Subsidence Module”, underscoring the crucial need for a nexus approach. Additionally, it highlights some of the tools and strategies that can be employed to tackle the challenges at hand. The success of this initiative hinges on active participation from various stakeholders. By embracing a holistic approach and fostering collaboration, we can strive towards better preparedness and long-term resilience against landslides and land subsidence.

Unveiling crustal deformation patterns along the north Tabriz fault from 2015 to 2022 using multi-temporal InSAR analysis

This paper presents a comprehensive study on the recognition of crustal deformation patterns surrounding the North Tabriz Fault in Northwestern Iran, utilizing Multi-Temporal InSAR analysis. The fault, despite its seismic inactivity for over two centuries, has a long history of ancient seismicity, with earthquake recurrence intervals exceeding two centuries. This makes it highly susceptible to future activity and the generation of significant and devastating earthquakes. However, limited research has been conducted on extracting and modeling deformation patterns of the North Tabriz Fault to identify its active segments. The primary objective of this study is to derive a general trend for fault displacement and investigate regions under pressure in terms of abnormal crustal movements. The results indicate that the Earth's crust in the surrounding regions of the central and northwest segments of the fault exhibits an upward movement ranging from approximately 2 to 10 millimeters per year from 2015 to 2022. In contrast, neighboring areas of the northwestern fault, as well as the northwestern, western, and southwestern parts of Tabriz County, experience ground subsidence with rates ranging from approximately 5 to 40 millimeters per year. These findings are consistent with GNSS-derived line-of-sight measurements obtained from some IPGN stations around the fault with an RMSE of 1.72 mm/yr. Furthermore, the study identifies critical points near the fault that exhibit varying and diverse displacement patterns over time, suggesting significant strain and notable stress within the subsurface environment. According to the analysis of time series data on crustal movements at the identified critical points, it has been found that the prevailing motion pattern of the Earth's crust within the fault zone largely conforms to a sinusoidal descending pattern. Additionally, recent earthquakes in the northwest vicinity of the fault have been observed to occur close to these critical points. Using line-of-sight (LOS) data acquired at these critical points, the study estimates a slip rate of 7.71 ± 0.01 mm/year and a locking depth of 11.27 ± 0.01 km, contributing to a better understanding of the fault's seismogenic behavior. These findings provide valuable insights into the crustal deformation patterns around the North Tabriz Fault, highlighting active segments and regions under pressure.

Land Subsidence Detection Using SBAS- and Stacking-InSAR with Zonal Statistics and Topographic Correlations in Lakhra Coal Mines, Pakistan

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan’s largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment.

Quantitative Evaluations of Pumping-Induced Land Subsidence and Mitigation Strategies by Integrated Remote Sensing and Site-Specific Hydrogeological Observations

Land subsidence is an environmental hazard occurring gradually over time, potentially posing significant threats to the structural stability of civilian buildings and essential infrastructures. This study presented a workflow using the SBAS-PSInSAR approach to analyze surface deformation in the Choushui River Fluvial Plain (CRFP) based on Sentinel-1 SAR images and validated against precise leveling. Integrating the InSAR results with hydrogeological data, such as groundwater levels (GWLS), multilayer compactions, and borehole loggings, a straightforward model was proposed to estimate appropriate groundwater level drops to minimize further subsidence. The results showed a huge subsidence bowl centered in Yunlin, with maximal sinking at an average 60 mm/year rate. High-resolution subsidence maps enable the quantitative analyses of safety issues for Taiwan High-Speed Rail (THSR) across the areas with considerable subsidence. In addition, the analysis of hydrogeological data revealed that half of the major compaction in the study area occurred at shallow depths that mainly included the first and second aquifers. Based on a maximal subsidence control rate of 40 mm/year specified in the CRFP, the model results indicated that the groundwater level drops from wet to dry seasons needed to be maintained from 3 to 5 m for the shallowest aquifer and 4–6 m for Aquifers 3 and 4. The workflow demonstrated the compatibility of InSAR with traditional geodetic methods and the effectiveness of integrating multiple data sources to assess the complex nature of land subsidence in the CRFP.

Estimation of Surface Water Level in Coal Mining Subsidence Area with GNSS RTK and GNSS-IR

Ground subsidence caused by underground coalmining result in the formation of ponding water on the ground surface. Monitoring the surface water level is crucial for studying the hydrologic cycle in mining areas. In this paper, we propose a combined technique using Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) and GNSS Interferometric Reflectometry (GNSS-IR) to estimate the surface water level in areas of ground subsidence caused by underground coal mining. GNSS RTK is used to measure the geodetic height of the GNSS antenna, which is then converted into the normal height using the local height anomaly model. GNSS-IR is employed to estimate the height from the water surface to the GNSS antenna (or, the reflector height). To enhance the accuracy of the reflector height estimation, a weighted average model has been developed. This model is based on the coefficient of determination of the signal fitted by the Lomb-Scargle spectrogram and can be utilized to combine the reflector height estimations derived from multiple GNSS system and band reflection signals. By subtracting the GNSS-IR reflector height from the GNSS RTK-based normal height, the proposed method-based surface water level estimation can be obtained. In an experimental campaign, a low-cost GNSS receiver was utilized for the collection of dual-frequency observations over a period of 60 days. The collected GNSS observations were used to test the method presented in this paper. The experimental campaign demonstrates a good agreement between the surface water level estimations derived from the method presented in this paper and the reference observations.

Linked Data-Driven, Physics-Based Modeling of Pumping-Induced Subsidence with Application to Bangkok, Thailand.

Research into land subsidence caused by groundwater withdrawal is hindered by the availability of measured heads, subsidence, and forcings. In this paper, a parsimonious, linked data-driven and physics-based approach is introduced to simulate pumping-induced subsidence; the approach is intended to be applied at observation well nests. Time series analysis using response functions is applied to simulate heads in aquifers. The heads in the clay layers are simulated with a one-dimensional diffusion model, using the heads in the aquifers as boundary conditions. Finally, simulated heads in the layers are used to model land subsidence. The developed approach is applied to the city of Bangkok, Thailand, where relatively short time series of head and subsidence measurements are available at or near 23 well nests; an estimate of basin-wide pumping is available for a longer period. Despite the data scarcity, data-driven time series models at observation wells successfully simulate groundwater dynamics in aquifers with an average root mean square error (RMSE) of 2.8 m, relative to an average total range of 21 m. Simulated subsidence matches sparse (and sometimes very noisy) land subsidence measurements reasonably well with an average RMSE of 1.6 cm/year, relative to an average total range of 5.4 cm/year. Performance is not good at eight out of 23 locations, most likely because basin-wide pumping is not representative of localized pumping. Overall, this study demonstrates the potential of a parsimonious, linked data-driven, and physics-based approach to model pumping-induced subsidence in areas with limited data.

Numerical evaluation of ground source heat pumps in a thawing permafrost region

Permafrost degradation poses significant environmental and geological challenges in Arctic and subarctic regions, particularly in areas like Umiujaq, Canada. The warming climate leads to thawing permafrost, causing ground instability, disrupting hydrology, and impacting local built environment. This study evaluates the use of Ground Source Heat Pump (GSHP) operation for mitigating ground subsidence in a permafrost region using a two-dimensional Thermo-Hydro-Mechanical (THM) coupled finite element analysis considering the ground poro-elastic and poro-plastic responses. The research uses a single-well scenario to demonstrate the interactions among thermal, hydraulic, and mechanical processes. The impact of GSHP operation under different temperature management strategies, including a scenario with a constant GSHP temperature of −5 °C throughout the year is numerically investigated. Results indicate that GSHP operation exacerbates ground deformation near the borehole, particularly during winter months. However, maintaining GSHP operation throughout the entire year can mitigate extreme subsidence fluctuations, leading to a more stable subsurface environment. While GSHP systems provide effective thermal regulation, their operation can introduce mechanical stresses that potentially disturb the ground close to the borehole. Therefore, careful design, operation, and further research are essential to balance thermal benefits with ground stability in permafrost regions.

Geophysical investigation of the ground fissures and ground subsidence near Karla lake (eastern Thessaly basin, Greece)

Ground fissures have occurred in the last four decades across the eastern Thessaly basin (Greece) resulting in damage to the villages of the area. Several studies for the area refer to the over-pumping of ground water as the main reason for their occurrence, causing sediment compaction due to the reduction of the aquifer level. In this paper, we depict the results of a joint geophysical survey trying to determine the subsurface regime of the basin and the exact reasons contributing to the existence of the ground fissures. The focal part of the survey includes gravity measurements for deeper investigation, combined with existing and already presented geoelectrical and electromagnetic data. Several borehole data have also been used for the calibration of the geophysical interpretation. The differential GNSS data of the gravity campaign revealed the ground subsidence of the area, reaching up to 9.16 m. The alpine basement of the area is comprised mainly of metamorphic rocks, such as marbles, mica schists and gneiss-schists, covered by thick fluviοterrestrial and alluvial deposits. Several structural maps were generated in order to delineate the lateral density variations that could be related to fault zones along with the interpretive sections for geological modelling. The alpine bedrock was adumbrated in relatively great depths, with a large anticline of NW-SE direction, rising and separating the basin in two parts. In the east part, the fluvioterrestrial deposits, which are expected to play an important role in the compaction due to their water aquifer, are located only west of this anticline. At the east part, where the old lake Karla was hosted, the alluvial deposits lay directly on the alpine basement in smaller depths. This complicated regime is responsible for differential sediment compaction and the ground subsidence of the surface.

Sustainable Development Challenges in Shanxi's Coal Industry: A case study of Datong Coal Mine Group

The purpose of this study is to investigate the sustainable development challenges faced by coal resource-based areas, focusing specifically on Shanxi Province in China. The study aims to understand the long-term impacts of extensive coal mining on the ecological environment, safety incidents, and the local economic system. It seeks to draw lessons from case studies of coal enterprises to identify innovative measures for companies in transition and development, with the ultimate goal of promoting sustainable practices that address livelihood issues and contribute to the modernization process. The research employs a qualitative case study methodology, using Datong Coal Mine Group in Shanxi Province as a representative example. This approach enables an in-depth analysis of the specific challenges related to coal mining in the region. The findings highlight the significant ecological and safety challenges posed by widespread coal mining practices in Shanxi Province, including environmental degradation, ground subsidence, and recurring safety incidents. The study suggests a need for innovative measures to transition towards more sustainable and diversified economic activities. The research provides valuable insights and recommendations for policymakers, industry leaders, and communities in similar resource-dependent regions to adapt to the demands of modernization and sustainability.

Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities

This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities.

Construction of a deep basement with a single-level prop – prop forces and wall movements

This paper describes the construction of a deep basement in central London. The construction sequence for the basement was a combination of top-down and blue-sky excavation to enable phased delivery of the project. Temporary single-level props enabled blue-sky construction by managing ground movements. Ground movements associated with the basement construction and impact on third-party assets were monitored to validate the design using three-dimensional targets and inclinometers. The ground movements were predicted at each stage of construction using simple models with more complex three-dimensional soil–structure finite-element analysis used when examining the whole basement behaviour under the temporary and permanent loads. Temporary prop loads, and thermal loads on props were also monitored during the bulk digging. The small strain constitutive models for London Clay was shown to closely predict the observed movements.

Study on the Relationship between Groundwater and Land Subsidence in Bangladesh Combining GRACE and InSAR

Due to a heavy reliance on groundwater, Bangladesh is experiencing a severe decline in groundwater storage, with some areas even facing land subsidence. This study aims to investigate the relationship between groundwater storage changes and land subsidence in Bangladesh, utilizing a combination of GRACE and InSAR technologies. To clarify this relationship from a macro perspective, the study employs GRACE data merged with GLDAS to analyze changes in groundwater storage and SBAS-InSAR technology to assess land subsidence. The Dynamic Time Warping (DTW) method calculates the similarity between groundwater storage and land subsidence time series, incorporating precipitation and land cover types into the data analysis. The findings reveal the following: (1) Groundwater storage in Bangladesh is declining at an average rate of −5.55 mm/year, with the most significant declines occurring in Rangpur, Mymensingh, and Rajshahi. Notably, subsidence areas closely match regions with deeper groundwater levels; (2) The similarity coefficient between the time series of groundwater storage and land subsidence changes exceeds 0.85. Additionally, land subsidence in different regions shows an average lagged response of 2 to 6 months to changes in groundwater storage. This study confirms a connection between groundwater dynamics and land subsidence in Bangladesh, providing essential knowledge and theoretical support for further research.