Settlement Prediction Research Articles

Evaluation method of progressive failure of loess slope under shallow coal seam mining

Abstract When mining coal under loess gully, the surface slopes may exhibit significant discontinuous failure characteristics such as local failure and landslides. These characteristics, composed of failure and non-failure regions, form an essentially unstable mechanical system. This makes it difficult to obtain a true solution using continuous medium mechanics, greatly increasing the complexity of slope failure evaluation and management. To accurately evaluate the degree of slope failure during mining, clarify its mechanism, and propose appropriate management measures, the Ningtiaota Coal Mine in northern Shanxi was selected as the study area. Based on the unbalanced thrust method and the probability integral settlement prediction theory, and utilizing numerical calculations, we proposed a method for evaluating and managing slope failure in coal mining under loess gully. This method describes the changes in slope failure during mining through the deformation failure ratio of the slip surface and quantifies the degree of slope failure at different mining times, revealing the full process stability evolution characteristics of points and surfaces as the critical state of the slope progresses. When the deformation failure ratio of the slip surface is ≥1, it indicates overall slope failure and the beginning of slope sliding instability; when the deformation failure ratio of the slip surface is <1, it indicates local slope failure. Based on this, corresponding management methods were proposed according to the mechanical distribution characteristics of the slope slip surface in different stress areas. The research results were applied in the Ningtiaota Coal Mine in northern Shanxi, yielding positive outcomes.

Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City

Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models.

Settlement prediction of micropile supported raft using machine learning: modelling and performance evaluation

Settlement prediction of micropile supported raft using machine learning: modelling and performance evaluation

Data-Supported Prediction of Surface Settlement Behavior on Opencast Mine Dumps Using Satellite-Based Radar Interferometry Observations

To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. This dual approach—leveraging advanced data analysis tools and precise modeling—yields valuable insights into spatial settlement behavior. In particular, classic time–settlement models are applied to the InSAR data through least square regression and Taylor approximation. The integration of both approaches enables the more robust, data-validated forecasts of key geotechnical indicators, such as the time to settlement stabilization and the expected maximum settlement over large areas. An application at a mine in central Germany illustrates the method by generating spatial predictions of the settlement behavior over more than 200 ha. In general, the results provide a comprehensive dataset for investigating other factors influencing the settlement behavior of opencast mine dumps.

Prediction and Analysis of Subsurface Settlement in a Double-Line Shield Tunnel

Prediction and Analysis of Subsurface Settlement in a Double-Line Shield Tunnel

Research on the application of a hybrid intelligent algorithm in reducing the negative response of shield tunneling construction

Traditional shield project control relies on manual monitoring and management, which suffers from poor reliability, lag, and other defects. In this paper, a hybrid algorithm based on Bayesian optimization (BO), light gradient enhancer (LGBM), and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to realize intelligent control of shield construction safety. The BO-LGBM-NSGA-III is a multiobjective optimization algorithm that takes cutterhead wear and shield energy consumption as objective functions and the nonlinear mapping function of surface settlement and construction parameters as fitness functions. Based on the Pareto distribution of the optimized backing structure construction parameters, reasonable limits of the control parameters of the shield tunneling machine are obtained. The conclusions are as follows. (1) The R2 of the test set for surface subsidence prediction is 0.916, and the RMSE is 2.655. (2) The proposed method can achieve multiobjective optimization, and the surface settlement, cutter head wear, and shield energy consumption are optimized by 49.92%, 24.38%, and 41.88%, respectively. (3) By using this method, both prediction and optimization can be realized, and the optimal control scheme can be obtained. The negative impact of shield tunneling construction has been significantly reduced.

Research on the surface subsidence characteristics and prediction models caused by coal mining under the reverse fault

Predicting and understanding the phenomenon of surface subsidence caused by coal mining in working faces with faults are important issues for safe coal mining and efficient production. In numerical simulation experiments, it was found that the phenomenon of surface subsidence manifests when faults exist, and the degree of influence of faults with different dip angles on surface subsidence varies. This phenomenon is attributed to fault activation. According to the experimental results, the impact of faults with different dip angles on surface subsidence falls into three levels: level I for 35° faults, level II for 45° and 55° faults, and level III for 65° and 75° faults. Similarly, the relationship between the difficulty of fault activation and the dip angle of faults can be categorized as 35° faults prone to activation, 45° and 55° faults difficult to activate, and 65° and 75° faults not prone to activation. The probability integral correction model for fault mining, which integrates the surface subsidence values caused by fault-induced attenuation and the subsidence arising from separation spaces, was introduced, thereby constructing a surface subsidence prediction model. This proposed prediction model can accurately predict surface subsidence, with a root mean square error of 10.74 mm between the predicted and measured values, as validated using DInSAR results from the III 6301 working face in the Jincheng mining area.

Database Analysis of Long-Term Settlement Behavior of Pile Foundations in Shanghai Soft Coastal Clays

A database of 195 field records of long-term settlement of high-rise buildings in Shanghai soft coastal clays is presented. The collected field records are divided into two categories based on pile type, i.e., bored pile and precast pile. Based on the settlement recorders collected, the long-term settlement behavior and settlement duration of the pile foundations in soft coastal clays are investigated. The statistical results show that the pile foundation in Shanghai soft coastal clays can be divided into three kinds of site conditions by considering the pile length and variation of long-term deformation behavior. Prediction models based on the power function are proposed to calculate the normalized final settlement for both bored pile and precast pile foundations. Based on the statistical results, the average normalized final settlement (settlement per floor), average settlement ratio (final settlement to the settlement when the superstructure is completed), and average settlement duration in the three site conditions for both bored pile and precast pile foundations are given. Additionally, an exponential settlement prediction model that can accurately forecast the entire settlement and post-construction settlement characteristics of buildings in Shanghai’s soft soil area is proposed. These statistical results can be referred to the preliminary foundation design works, which contribute to the quick evaluation of the possible settlement range, settlement ratio, and settlement duration of the proposed building constructed in the soft coastal clay area in Shanghai.

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.

Ground settlement prediction for highway subgrades with sparse data using regression Kriging

Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies. To resolve it, this paper proposes a regression Kriging (RK)—based method for ground settlement prediction with sparse data. Under the framework of RK, the stationarity of sample residual and trend structure are key factors for prediction accuracy. It is found that the use of Box–Cox transformation, which can help to achieve stationarity of sample residual, leads to significant increase of the prediction accuracy with sparse data. Specifically, the various evaluation metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), mean arctangent absolute percent error (MAAPE) and scatter index (SCI)) are significantly decreased when the Box–Cox transformation is incorporated. In addition, the first-order polynomial trend structure is found to be more appropriate than those with higher orders for predicting settlements resulting from primary consolidation. Moreover, comparative study is conducted among the proposed RK method, classical prediction methods and back propagation neural network (BPNN). It is found that the evaluation metrics obtained by the RK method are significantly smaller than those obtained by the other methods, indicating its highest accuracy. By contrast, BPNN has the worst performance among the various methods, because the sparse data are inadequate to establish a satisfactory BPNN model.

Machine learning finite element mesh optimization-based modeling and prediction of Excavation-Induced shield tunnel ground settlement

Machine learning finite element mesh optimization-based modeling and prediction of Excavation-Induced shield tunnel ground settlement

Coupling effects on class C predictions of soft soil settlements

AbstractIn uncoupled consolidation analysis, settlement and pore water pressure are solved independently, whereas in coupled analysis, they are solved simultaneously to ensure continuity (i.e., the volume change in soil due to compression must equal the water volume change caused by dissipation). This study investigates the coupling effects of soil deformation and pore water pressure dissipation in the back analysis of soft soil settlements. It further evaluates the suitability of both coupled and uncoupled constitutive models with different types of monitoring data, providing practical guidance for selecting consolidation models and achieving reliable long-term predictions. The one-dimensional governing equations for soft soil consolidation, incorporating prefabricated vertical drains and creep deformation, are first reviewed. A case study of a trial embankment in Ballina, New South Wales, Australia, is then used to demonstrate the impact of coupling effects and monitoring data on settlement predictions. The results show that considering coupling effects not only improves long-term settlement predictions but also reduces uncertainties in the updated soil parameters, especially when both settlement and pore water pressure data are used.

Procedure Design and Reliability Analysis for Prediction of Surface Subsidence of a Metal Mine Induced by Block Caving Method—A Case Study of Pulang Copper Mine in China

Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed.

Ground subsidence prediction with high precision: a novel spatiotemporal prediction model with Interferometric Synthetic Aperture Radar technology

ABSTRACT 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.

A physics-constrained neural network for predicting excavation-induced ground surface settlement in clay

Accurate prediction of ground surface settlement (GSS) adjacent to an excavation is important to prevent potential damage to the surrounding environment. Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data. In the present study, we proposed a physics-constrained neural network (PhyNN) for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks (NNs). This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN. Moreover, we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process. The performance of the proposed PhyNN model is verified using data from a case study project. Results show that our PhyNN model achieves higher prediction accuracy, better generalization ability, and robustness than the purely data-driven NN model when confronted with data containing noise and outliers. Remarkably, by incorporating physical constraints, the admissible solution space of PhyNN is significantly narrowed, leading to a substantial reduction in the need for the amount of training data. The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.

Controls on the Stratigraphic Architecture of the US Atlantic Margin: Processes Forming the Accommodation Space

AbstractAccommodation space governs the spatial and temporal distributions of sediments in continental margins. Mapping the sedimentation patterns, therefore, offers insights into the solid‐Earth processes that shape accommodation space. We assembled an unprecedented amount of seismic and borehole data along the Eastern North American Margin and used it to divide the margin's sedimentary package into eight chronostratigraphic intervals, identifying temporal shifts in depocenters under the continental shelf, slope, and rise. The Jurassic depocenters follow the syn‐rift structure and its thermal subsidence loci. The Long Island Platform is the only margin segment where the early post‐rift sediment thickness matches subsidence predictions from uniform‐stretching models, whereas in Georges Bank Basin (GBB) and Baltimore Canyon Trough (BCT), sediment thickness is 1.5–3 times higher than predicted, pointing to other factors at play. A margin‐wide Jurassic transient shoulder uplift is inferred from the occurrence of stratigraphic onlaps above thinned crust. Unlike the Jurassic, the Cretaceous and Cenozoic depocenters disregard the inherited subsidence pattern. The accommodation space over the shelf and coastal plain during the Cretaceous was affected by regional isostatic compensation of the sedimentary loads accumulated on the shelf and rise. Accommodation space development in the GBB was interrupted during the Cretaceous after the margin crossed the Great Meteor Hotspot track, resulting in a widespread permanent uplift, erosion, and sediment redistribution. The distribution of anomalous Neogene subsidence in the BCT challenges previous suggestions of mantle dynamic control on the accommodation space and favors flexural downwarping of the shelf by sediment accumulation on the rise.

Physics-Informed Neural Network (PINN) model for predicting subgrade settlement induced by shield tunnelling beneath an existing railway subgrade

For the shield tunnelling beneath an existing railway subgrade, factors such as disturbance during shield tunnel excavation can cause settlements of the existing subgrade above. Traditional prediction methods for subgrade settlement may not fully reflect the construction site conditions. Recently, machine learning has been widely applied due to its fast computation speed and accurate results. However, data-driven machine learning is considered a “black-box” algorithm, lacking a mechanical theory framework and having weak generalization capabilities. To address the limitations of machine learning and enhance the efficiency and accuracy of subgrade settlement prediction during shield tunnel construction, this paper proposes a prediction model based on a physics-informed neural network (PINN). Firstly, by integrating the non-uniform displacement mode function of the tunnel, the physical equations are constructed for the shield tunnel excavation process. Subsequently, the physical equations are coupled with the machine learning framework to develop a predictive model for shield tunnelling beneath an existing railway subgrade based on the PINN model. The performance of the proposed PINN model is evaluated through the application of two engineering cases and compared with traditional numerical simulations and data-driven machine learning. The results indicate that the proposed model can not only predict the settlement of the existing railway subgrade under scant and noisy monitoring data but also have accuracy in the inversion of tunnel displacement mode parameters. Finally, parametric analyses are conducted to reveal the influence of critical variables on the prediction performance of the PINN model.

Magnetic Resonance Imaging-Based Vertebral Bone Quality Score for Prediction of Cage Subsidence and Screw Loosening in Patients Undergoing Degenerative Lumbar Surgery: A Meta-analysis.

Poor bone quality is a risk factor for postoperative complications after degenerative lumbar fusion surgery. The magnetic resonance imaging-based vertebral bone quality (VBQ) score is a good tool for assessing bone quality, and this is the first meta-analysis performed to summarize the predictive value of the VBQ score for cage subsidence and screw loosening in patients undergoing degenerative lumbar surgery. Studies were comprehensively searched in electronic databases. The quality of the studies was assessed. The pooled sensitivity, specificity and summary receiver operating characteristic curve were calculated. Publication bias was assessed and meta-regression was conducted. We ultimately included 9 studies with a total of 1,404 patients with a mean age of 60.4 years and a percentage of females of 57.0%. According to the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to assess methodological quality, the quality of the included studies was relatively low and risks of bias might exist. Results showed that a high VBQ was significantly associated with cage subsidence and screw loosening, and risk factor analysis revealed that the merged odds ratio was 5.37 for cage subsidence and 3.87 for screw loosening. With a VBQ cutoff value of 3.34±0.45, the pooled sensitivity and specificity for the diagnosis of postoperative complications were 0.75 and 0.75, respectively, and the area under the curve was 0.82 (95% confidence interval, 0.78-0.85). A high VBQ was associated with a high risk of cage subsidence and screw loosening in patients who underwent degenerative lumbar surgery. The VBQ score could be considered for identifying high-risk patients for further evaluation.

Subsidence Prediction Method Based on Elastic Foundation Beam and Equivalent Mining Height Theory and Its Application

Grouting technology in overburden separation is recognized as an effective method to prevent surface subsidence and reuse solid waste. This study used mechanical analysis to explore deflection characteristics of key strata and accurately predict and control surface subsidence. Conceptualizing the coal–rock mass beneath the key strata as an elastic foundation, we developed a method to calculate the elastic foundation coefficients for various regions and established an equation for key strata deflection, validated through discrete element numerical simulations. This simulation also examined subsidence behavior under different grout injection–extraction ratios. Additionally, combining the equivalent mining height theory with the probability integral method, we formulated a predictive model for surface subsidence during grouting. Applied to the 8006 working face of the Wuyang Coal Mine, this model was supported by numerical simulations and field data, which showed a maximum surface subsidence of 546 mm at a 33% injection–extraction ratio, closely matching the theoretical value of 557 mm and demonstrating a nominal error of 2%. Post-grouting, the surface tilt was reduced to below 3 mm/m, meeting regulatory standards and eliminating the need for ongoing surface structure maintenance. These results confirm the model’s effectiveness in forecasting and controlling surface subsidence with grouting. The study can provide a basis for determining the grouting injection–extraction ratios and evaluating the effectiveness of surface subsidence control in grouting into overburden separation projects.

A Multi-Objective Prediction XGBoost Model for Predicting Ground Settlement, Station Settlement, and Pit Deformation Induced by Ultra-Deep Foundation Construction

Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are more popular than physical models because they can handle dynamic process data. However, these models frequently fail to establish an appropriate balance between accuracy and generalization capacity when dealing with multi-objective prediction. This work proposes a multi-objective prediction model based on the XGBoost algorithm and introduces the Random Forest Bayesian Optimization method for hyperparameter self-optimization and self-adaptation in the prediction process. This model was trained with monitoring data from a deep foundation pit at Luomashi Station of Chengdu Metro Line 18, which are characterized by a sand and pebble stratum, cut-and-cover construction, and a depth of 45.5 m. Input data of the model included excavation rate, excavation depth, construction time, shutdown time, and dewatering; output data included settlement, ground settlement, and pit deformation at an operating metro station only 5.7 m adjacent to the ongoing pits. The training effectiveness of the model was validated through its high R2 scores in both training and test sets, and its generalization ability and transferability were evaluated through the R2 calculated by deploying it on adjacent monitoring data (new data). The multi-objective prediction model proposed in this paper will be promising for monitoring the data processing and prediction of settlement of surrounding buildings for ultra-deep foundation pit engineering.