Settlement Prediction Research Articles

An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data

The land subsidence occurring over a goaf area after coal mining is a protracted process. The accurate prediction of long-term land subsidence over goaf areas relies heavily on the availability of long-term land subsidence monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on interferometric synthetic aperture radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No. 2 mine in Shanxi Province, China. Initially, coal faces with the same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation on each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No. 2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term land subsidence monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence.

Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas.

A simplified algorithm for predicting creep settlement of high fills based on modified power law model

The calculation and prediction of creep settlement is an important basis for judging whether the settlement of high fills is stable and arranging subsequent construction. In this study, a modified power law model is established based on Hypothesis B and Bjerrum's classification of creep deformation. The modified model can uniformly describe the one-dimensional creep deformation of clay, sand and soil-rock mixture, and satisfies the requirement that the one-dimensional creep deformation tends to a constant value over time rather than increasing indefinitely. Then a simplified algorithm for predicting creep settlement is proposed based on the modified model. The simplified algorithm has only three parameters with explicit physical meaning, which are determined by settlement monitoring data. By the settlement monitoring data from high fills at Chengde Airport, Chengchi Expressway and Panzhihua South Railway Station, the fitting and predicted results of the simplified algorithm were compared with other three prediction algorithms commonly used in geotechnical engineering. The results show that the proposed simplified algorithm can predict the creep settlement of high fills more effectively and accurately.

Investigation of dynamic characteristics and cumulative plastic strain prediction model of clay-fouled round gravel under cyclic subway loading

This study conducts a series of saturated undrained dynamic triaxial tests using the DYNTTS large triaxial cyclic test system to investigate the dynamic characteristics and cumulative plastic strain evolution of clay-fouled round gravel under cyclic subway loading. The effects of clay fouling level (VCI), confining pressure, and dynamic stress amplitude on round gravel's dynamic properties and cumulative plastic strain are analyzed. In addition, a cumulative plastic strain prediction model is proposed by combining the time hardening theory with clay fouling level, confining pressure, and dynamic stress amplitude. The study's results indicated that with the increase in VCI, the accumulated plastic strain of the specimen tends to increase and then decrease, and the fouled clay changes from lubrication to filling, with the cut-off VCI generally between 20% and 30%. VCI significantly affects the cumulative plastic strain of specimens with small confining pressures and slightly impacts specimens with large confining pressures. With the increase in VCI, the specimen's elastic strain tends to increase and then decrease, while its resilient modulus shows the opposite trend, but both trends are more pronounced with the decrease in the confining pressure and the increase in the dynamic stress amplitude. Generally, the research results can be used as a reference for designing subway roadbed structures and their post-construction settlement predictions.

Study on Settlement of Self-Compacting Solidified Soil in Foundation Pit Backfilling Based on GA-BP Neural Network Model

In order to predict the settlement of self-compacting solidified soil in foundation pit backfilling, finite element software is used to study the influence of soil properties and the surrounding structural properties of the foundation pit on the settlement of backfilled self-compacting solidified soil based on a foundation pit project in the city of Nanjing. The degree of influence of various factors influencing settlement is considered, a grey relational grade analysis is conducted, and input layer parameters of the neural network are determined based on the results of the grey relational grade analysis. Based on the GA-BP neural network model, the settlement of soil is predicted using numerical simulation results. The results reveal that the settlement and structural disturbance of self-compacting solidified soil after backfilling are smaller than those of fine silty sand; self-compacting solidified soil significantly improves the engineering performance of excavated soil. In the grey relational grade analysis, the six influencing factors that have high correlation with soil settlement can be used as input layer parameters for the neural network model. Among them, the correlation degree between elastic modulus and soil settlement is the highest, reaching 0.8402. The correlation degrees of the remaining five influencing factors are above 0.5, and the values are close. The GA-BP neural network can improve the overfitting situation of a BP neural network trapped in local optima, with R2 reaching 0.9999 and RMSE only 0.0018 mm, achieving high-precision prediction of settlement of self-compacting solidified soil.

Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor

The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement deformation are of great significance for expressway construction and maintenance in the Qinghai–Tibet region. This paper establishes a thaw settlement prediction model using the thaw settlement coefficient and thaw depth. The thaw depth was predicted by the mean annual ground temperatures and active-layer thicknesses using the Radial Basis Function (RBF) neural network model, and the thaw settlement coefficient was determined according to the type of ice content. Further, the distribution characteristics of thaw settlement risk of the permafrost subgrade in the study region were mapped and analyzed. The results showed that the thaw settlement risk was able to be divided into four risk levels, namely significant risk, high risk, medium risk and low risk levels, with the areas of these four risk levels covering 3868.67 km2, 1594.21 km2, 2456.10 km2 and 558.78 km2, respectively, of the total study region. The significant risk level had the highest proportion among all the risk levels and was mainly distributed across the Chumar River Basin, Beiluhe River Basin and Gaerqu River Basin regions. Moreover, ice content was found to be the main factor affecting thaw settlement, with thaw settlement found to increase as the ice content increased.

Prediction of ground subsidence by shield tunneling using ensemble learning

This study addresses a significant issue related to shield construction, namely the impact of shield construction parameters on ground subsidence. Shield tunnels, owing to their concealed construction, often lead to ground subsidence, causing severe disruptions to ground facilities, potential collapse, and consequent casualties and property loss. Most calculation methods for ground subsidence have a delay, making it difficult to predict ground subsidence in advance based on construction parameters. Therefore, this study collates over 1500 cases of shield construction in China and summarises the impact mechanisms of shield construction on ground subsidence through a detailed study of natural factors, engineering geology, types of structures, and types of construction parameters. Based on the influence law of the data, this study establishes three intelligent prediction methods to forecast ground subsidence. To select the hyperparameters in the models more effectively, a Bayesian optimisation algorithm was utilised. Through a comparative analysis of the models, a subsidence prediction method that is more in line with the construction characteristics and exhibits high accuracy was determined. The findings of this study serve as a valuable resource for predicting ground subsidence resulting from shield construction, enabling timely adjustments to construction parameters and the implementation of preventive measures to mitigate excessive subsidence.

Prediction of shallow foundation settlement: usage of hybridized support vector regression model

Prediction of shallow foundation settlement: usage of hybridized support vector regression model

Widespread Permafrost Degradation and Thaw Subsidence in Northwest Canada

AbstractLong‐term (1991–2018) thaw tube measurements highlight widespread permafrost thaw and ground surface (GS) subsidence over a large portion of northwest Canada. Statistically significant positive trends in thaw penetration (TP), measured with respect to a fixed datum, were observed at 18 of 28 sites with data that span three decades at a median rate of 0.8 cm a−1. This rate implies thawing of about 22 cm of permafrost over the study period. Similarly significant trends in GS subsidence occurred at 21 of 28 sites, at a median rate of 0.4 cm a−1. In contrast with TP and GS elevation, long‐term trends in active layer thickness (ALT) were less consistent, with 10 of 28 sites having statistically significant trends indicating increasing ALT (median rate: 0.6 cm a−1), and 7 sites having trends indicating decrease in ALT (median: 0.3 cm a−1). The ALT measurements underestimated the thawing of ice‐rich permafrost due to GS subsidence. Sites with high rates of thaw had relatively warm permafrost, deep snow cover, and poor drainage. Masking of ice‐rich permafrost degradation by ALT measurements has important implications for modeling permafrost thaw and climate change reporting. Accurate simulation of ice‐rich permafrost thaw is conceivable for sites where conditions are well‐defined. However, the prediction of subsidence and TP at regional or broader scales is only possible in general terms due to variation in surface conditions and ground ice content. Trends in TP and GS subsidence more accurately characterize ice‐rich permafrost degradation than trends in ALT.

PERBANDINGAN PENURUNAN KONSOLIDASI METODE TERZAGHI DENGAN ASAOKA PADA VACUUM PRELOADING

Around 20 million hectares of Indonesia's total land area, or more than 10%, has soft soil. Soft soils can be problematic in construction projects due to their low bearing capacity, high water content, limited permeability, and high compressibility. Vacuum preloading, a technique to hasten consolidation by employing atmospheric pressure from the vacuum as a surcharge load, is one of the improvements for soft soil. The Terzaghi method is typically used to calculate the prediction of the final settlement and the time to reach the final settlement, however the forecast of settlement by the Terzaghi method produces better results than the actual settlement. To estimate the final settlement that takes place in the field, a method with a reliable level of accuracy is used—the Asaoka method. The main goals of this analysis are to determine the final settlement using the Asaoka method, the time needed to reach the final settlement using the Asaoka method, the degree of consolidation caused by vacuum preloading, and soil parameters that support notable differences between the Terzaghi method and Asaoka method using back analysis. According to both site reviews, the Terzaghi method produced settlement outcomes that were 36% and 79% higher than those obtained using the Asaoka method. The time required to reach 90% consolidation on both site reviews are 252 days and 196 days. The results of the Asaoka method used to calculate the degree of consolidation on both site reviews are 92.33% and 94.47%. The value of compression index (Cc), which was derived by back analysis calculations, is what most strongly supports the significant outcomes between the Terzaghi method and the Asaoka method.

Comparison of predictive performance for cage subsidence between CT-based Hounsfield units and MRI-based vertebral bone quality score following oblique lumbar interbody fusion.

To compare the predictive performance between CT-based Hounsfield units (HU) and MRI-based vertebral bone quality (VBQ) for cage subsidence (CS) following oblique lumbar interbody fusion combined with anterolateral single-rod screw fixation (OLIF-AF). A retrospective study was performed on consecutive patients who underwent OLIF-AF at our institution from 2018 to 2020. CS was determined by CT according to the change in the midpoint intervertebral space height. The VBQ score and HU value were measured from preoperative MRI and CT, respectively. Then, we evaluated the predictive performance of those two parameters by comparing the receiver operating characteristic (ROC) curves. The mean global and segmental VBQ scores were significantly higher in the CS group, and the mean global and segmental HU values were significantly lower in the CS group. The area under the curve (AUC) of CS prediction was higher in the operative segments' VBQ score and HU value than the measurement in the global lumbar spine. Finally, the combined segmental VBQ score and segmental HU value demonstrated the highest AUC. Both MRI-based VBQ score and CT-based HU value can achieve accurate CS prediction. Moreover, the combination of those two measurements indicated the best predictive performance. Both MRI-based VBQ score and CT-based HU value can be used for cage subsidence prediction, in order to take preventive measures early enough. • Osteoporosis is a risk factor for CS, both MRI-based VBQ score and CT-based HU value are important predictors during vertebral bone quality evaluation. • The VBQ score and HU value measured in the operative segments are better predictors of CS than the measurement in the global lumbar spine. • Combined segmental VBQ score and segmental HU value achieved the best predictive performance for CS.

Application of Drone LiDAR Survey for Evaluation of a Long-Term Consolidation Settlement of Large Land Reclamation

Drone surveys are gaining popularity for many construction applications, including in the fields of civil engineering, such as road construction, earthwork, structure monitoring, and coastal topography analysis. Drone surveying has a high potential for periodical long-term ground settlement measurement in the field of geotechnical engineering. Traditionally, manual measurement has been performed for limited points with controlled surface measurement points, but drone surveying may enable automated and periodical measurement for a wide and remote site. However, the accuracy of the elevation measurement and the surface settlement prediction has not been investigated, and the use of drone surveying has thus been limited. Therefore, an experiment was carried out to apply drone LiDAR (Light Detection and Ranging) surveying for soft ground settlement measurement at a large land reclamation site showing a very large settlement up to 10 m. Periodic drone LiDAR surveying was conducted, and the data were processed with direct georeferencing and with outlier removals (such as trees and construction vehicles) in order to generate a clean surface point cloud. We then compared the processed elevation data with ground control data to check the vertical accuracy and to predict the settlement as well as for consolidation. The drone LiDAR survey showed 13 cm, 42.9 cm, and 6.23% differences in RMSE (Root Mean Square Error) in terms of vertical accuracy, predicted long-term settlement, and consolidation, respectively. The drone LiDAR accuracy seems very useful for monitoring settlement over a large and remote land reclamation site of soft ground, showing settlement up to several meters where, without a surface measurement, installment is limited.

Experimental Study on the Axial Deformation Characteristics of Compacted Lanzhou Loess under Traffic Loads

It is beneficial to the sustainable development of expressway engineering to reuse excavated soil as roadbed filling material. There are a large number of filling projects using loess as a filling material in Northwest China. In this paper, the loess subgrade of an expressway in Lanzhou is taken as the research object, and a series of experimental studies are conducted using a hollow cylindrical torsion shear system to simulate the formation of a “heart-shaped” stress path and the principal stress rotation (PSR) under long-term traffic loads. The effects of the vertical cyclic dynamic stress ratio, torsion shear stress ratio, initial static shear stress, and intermediate principal stress coefficient on the axial plastic deformation and rebound deformation of compacted loess in Lanzhou were studied. The results show that the vertical cyclic stress ratio (VCSR) has a significant effect on the axial deformation of compacted loess in Lanzhou. When the VCSR is less than 0.6, all the axial strain curves develop stably with the number of cycles. With an increasing VCSR, the axial plastic deformation increases obviously, and the axial rebound deformation also increases. The vertical cyclic dynamic stress of the specimen is constant. Moreover, increasing the torsional shear stress ratio (that is, increasing the amplitude of cyclic shear stress) can greatly increase the development of axial deformation, but it has no effect on the rebound deformation curve. When the initial static shear stress exists in the specimen, the larger the initial static stress ratio (SSR) is, the larger the axial plastic deformation. The axial plastic deformation increases by approximately 33% for every 0.1 increase in the SSR. The rebound deformation of different SSRs fluctuates at the initial stage of cyclic loading, but the final stable rebound deformation is basically the same as that at the initial stage of cyclic loading. The intermediate principal stress coefficient has no effect on the development of axial strain, and the effect on axial rebound deformation is negligible. Finally, the calculation model of the axial plastic strain of Lanzhou compacted loess under traffic loads is obtained. The research results can provide a reference for the durability and settlement prediction in loess engineering.

Deep learning-based prediction of steady surface settlement due to shield tunnelling

Predicting ground movement produced by shield tunnelling in densely built urban areas is of practical significance. This study introduces an artificial intelligence method to predict the steady surface settlement caused by shield tunnelling. The proposed method comprises three models: a hybrid deep neural network model (HDNN), an error correction model (ECM), and an auxiliary model. Based on the settlement mechanism associated with tunnelling, the factors related to the ground movement were classified into five categories. The first four types of factors were used as the input parameters of the HDNN to predict steady surface settlement. The ECM considers unmeasurable factors. Five datasets from different subway lines were used to assess the performance and generalisation capacity of the proposed method. The results show that the proposed method outperforms classical deep neural networks as it considers the settlement mechanism and spatial and temporal characteristics of the input parameters.

Study on surface subsidence prediction method of shallow coal seam backfill-strip mining under the hard roof

Study on surface subsidence prediction method of shallow coal seam backfill-strip mining under the hard roof

Research on a Space-Time Continuous Sensing System for Overburden Deformation and Failure during Coal Mining.

Underground coal mining can cause the deformation, failure, and collapse of the overlying rock mass of a coal seam. If the mining design, monitoring, early warning, or emergency disposal are improper, in that case, it can often lead to mining disasters such as roof falls, water inrush, surface collapse, and ground fissures, seriously threatening the safety of mine engineering and the geological environment protection in mining areas. To ensure the intrinsic security of the entire coal mining process, aspace-time continuous sensing system of overburden deformation and failure was developed, which breaks through the limitations of traditional monitoring methods that characterize the evolution process of overlying rock deformation and ground subsidence. This paper summarizes the classification of typical overburden deformation and failure modes. It researches the space-time continuous sensing of rock-soil mass above the coal seam based on Distributed Fiber Optic Sensing (DFOS). A multi-range strain optical fiber sensing neural series from micron to meter was developed to achieve synchronous sensing of overburden separation, internal micro-cracks, and large rock mass deformation. The sensing cable-rock mass coupling test verified the reliability of the optical fiber monitoring data. The sensing neural network of overburden deformation was constructed using integrated optical fiber layout technology on the ground and underground. Different sensing nerves' performance and application effects in overburden deformation and failure monitoring were compared and analyzed with field monitoring examples. A physical model was used to carry out the experimental study on the overburden subsidence prediction during coal mining. The results showed that the optical fiber monitoring data were reliable and could be used to predict overburden subsidence. The reliability of the calculation model for overlying rock subsidence based on space-time continuous optical fiber sensing data was verified in the application of mining subsidence evaluation. A systematic review of the shortcomings of current overburden deformation observation technology during coal mining was conducted, and a space-time continuous sensing system for overburden deformation and failure was proposed. This system integrated sensing, transmission, processing, early warning, decision-making, and emergency response. Based on the fusion of multi-parameter sensing, multi-method transmission, multi-algorithm processing, and multi-threshold early warning, the system realized the real-time acquisition of space-time continuous information for the overburden above coal seams. This system utilizes long-term historical monitoring data from the research area for data mining and modeling, realizing the prediction and evaluation of the evolution process of overburden deformation as well as the potential for mining subsidence. This work provides a theoretical reference for the prevention and control of mining disasters and the environmental carrying capacity evaluation of coal development.

Construction and Application of Regional Multivariable Settlement Prediction Model

Construction and Application of Regional Multivariable Settlement Prediction Model

Prediction for the surface settlement of double-track subway tunnels for shallow buried loess based on peck formula

In the process of constructing double-track subway tunnels in shallow buried loess areas, the interaction of double-track tunnels is significantly influenced by the net distance and the cross-section size, which is challenging to control the surface settlement. Therefore, the surface settlement prediction is essential while constructing double-track subway tunnels in shallow buried loess areas. The paper analyzed the surface settlement law of shallow buried double-track tunnels in loess areas through theoretical research and numerical simulation. The research results show that with the decrease of the net distance, the surface settlement superimposed curve was double V shape -W shape - single V shape. When the superimposed curve is double V shape and W shape, the Peck formula was used to calculate the surface settlement curve of the single-track tunnel, then superimposed to obtain the final surface settlement curve. When the superimposed surface settlement curve was V shape, based on the Peck formula, the formula for predicting the surface settlement suitable for symmetry and asymmetry was established. The net distance ratio and the area ratio were defined, and considering the tunnel’s interaction, the value and position of the maximum were corrected. Then numerical tests were carried out 16 times with different net distance ratios and area ratios, to determine the parameters of increments and position offsets of the maximum regarding the net distance ratio and the area ratio. Finally, two engineering were conducted for verifying the rationality and applicability exhibited by the prediction formula. The prediction formula served for predicting the surface settlement of double-track subway tunnels in shallow buried loess areas. Which can reduce construction risks and assure the safety of buildings above the ground.

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

Undercrossing construction can cause severe structural deformation of the above existing tunnel in operation. The induced longitudinal differential settlement between the segments can pose a huge risk to running subways, hence it is of great importance to monitor and predict the settlement. Within this study, a Wireless Sensor Network (WSN) system was implemented to obtain hourly monitoring data of settlement from the very beginning of undercrossing to post construction period. An improved direct multi-step (DMS) forecasting model called ConvRes-DLinear is proposed, which fuses monitoring data with time and process encoding bias to deeply extract and learn temporal correlation of time series. A residual LSTM model is also constructed to compare the accuracy of the improved DLinear model. The training and testing experiment on the monitoring data of longitudinal settlement obtained by WSN system shows that the ConvRes-DLinear model with time and process encoding bias performs surprisingly well with a minimum prediction error. The features of the proposed model are discussed to make the results explainable. The monitoring system and time series forecasting model proposed in this study have a guiding significance for the monitoring and prediction of longitudinal differential settlement of tunnels under environmental disturbance.

Settlement prediction of existing metro induced by new metro construction with machine learning based on SHM data: a comparative study

Settlement prediction of existing metro induced by new metro construction with machine learning based on SHM data: a comparative study