Surface Subsidence Prediction Research Articles

Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function

Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area.

Forecasting and early warning of shield tunnelling-induced ground collapse in rock-soil interface mixed ground using multivariate data fusion and Catastrophe Theory

The high spatial variability of the rock-soil interface (RSI) in complex geological conditions introduces strong uncertainties in both subsurface stratigraphy and geotechnical properties. Inaccurate interpretation of such uncertainties during engineering geology investigations increases the geohazard risk of excessive surface settlement or even severe catastrophic ground collapse when shield machines are excavating in RSI mixed ground. Prediction and early warning of excessive surface settlement are necessary measures to address such a risk; however, unavoidable drawbacks such as overfitting, insufficient accuracy, and ineffectiveness remain in existing prediction models and early warning algorithms and have posed significant challenges. In this study, a novel framework using both a multivariate data fusion prediction model and a dynamic early warning algorithm was developed for forecasting and early warning of ground collapse during shield tunnelling in RSI mixed ground. The prediction model is the Differential Evolutionary Optimized Quadratic Taylor Series Extended Kalman Filter (DEQT-EKF); the early warning algorithm is based on Catastrophe Theory and uses the Gradient Ratio (GR) criterion to identify catastrophic singularities. The practicality and accuracy of the framework are well verified by a subway shield tunnelling-induced ground collapse incident in East China with complex RSI mixed ground conditions. The prediction results are compared with the surface settlement measurements and good agreement is obtained, indicating that the DEQT-EKF model can achieve satisfactory accuracy in predicting excessive settlement. The use of the GR criterion can trigger the early warning one time step before the ground collapse event, indicating that it is a competent and practical early warning strategy for shield tunnelling-induced ground collapse. The framework has the potential to significantly reduce the risk of ground collapse caused by geological uncertainties when constructing shield tunnels through complex ground conditions.

Prediction of ground surface settlements by analytical and finite element methods

The soil surrounding the excavation of shallow tunnels may experience destructive deformation near the tunnels. Particularly in densely populated areas where land resources are scarce, ground subsidence caused by tunnel excavation, which in turn affects surrounding structures, deserves more attention. Over the past few decades, various research methods for predicting the maximum vertical ground surface settlement have been proposed. In this study, an empirical estimation method, an analytical technique for evaluating ground movements, and a numerical simulation method using Plaxis 2D, a finite element model software, were selected as prediction approaches for ground surface settlements. Subsidence measurements were obtained from a metro tunnel excavated using an earth pressure balance shield tunnel boring machine and constructed in a dense sandy gravel layer. Comparison of the monitored deformations with the estimation results calculated by different prediction approaches showed that the maximum vertical settlement of the ground surface evaluated by the analytical technique and finite element model agreed well with the actual measured values. This suggests that these two prediction methods have the potential for conserving nearby structures during the construction of underground infrastructures.

Prediction of airport runway settlement using an integrated SBAS-InSAR and BP-EnKF approach

The prediction of surface settlement occupies a crucial role in achieving effective catastrophe prevention and mitigation, as well as facilitating the maintenance of airport runways. Given the challenges associated with the manual collection of settlement data and the suboptimal timeliness of the Back Propagation (BP) technique in neural networks, this study proposes an integrated prediction method that combines the Ensemble Kalman Filter (EnKF) with BP. This study focuses on the processing of Synthetic Aperture Radar (SAR) pictures obtained from the ascending orbit of the Sentinel-1A satellite. The Small Baseline Subset Interferometric synthetic aperture radar (SBAS-InSAR) technology is employed to derive the settlement time series on the runway of Kangding Airport. Moreover, three sites with high coherence within the primary settlement region are employed to assess the dependability of the model after the expansion of the data by cubic spline interpolation. The findings of the study indicate that both the BP-EnKF and BP models exhibit favorable outcomes in predicting airport runway settlement. However, following the alteration of data caused by external environmental influences, the BP-EnKF model has superior adaptability to variations in data. It has been shown that the BP-EnKF model exhibits a prediction accuracy that surpasses the BP model by 9.25%.

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

To attain precise forecasts of surface displacements and deformations in goaf areas (a void or cavity that remains underground after the extraction of mineral resources) following coal extraction, this study based on the limitations of individual time function models, conducted a thorough analysis of how the parameters of the model impact subsidence curves. Parameter estimation was conducted using the trust-region reflective algorithm (TRF), and the time function models were identified. Then we utilized a combined model approach and introduced the sliding window mechanism to assign variable weights to the model. Based on this, the combined model was used for prediction, followed by the application of this composite prediction to engineering scenarios for the dynamic forecasting of surface movements and deformations. The results indicated that, in comparison with DE, GA, PSO algorithms, the TRF exhibited superior stability and convergence. The parameter models obtained using this method demonstrated a higher level of predictive accuracy. Moreover, the predictive precision of the variable-weight time function combined model surpassed that of corresponding individual time function models. When employing six different variable-weight combination prediction models for point C22, the Weibull-MMF model demonstrated the most favorable fitting performance, featuring a root mean square error (RMSE) of 32.98 mm, a mean absolute error (MAE) of 25.66 mm, a mean absolute percentage error (MAPE) of 7.67%; the correlation coefficient R2 reached 0.99937. These metrics consistently outperformed their respective individual time function models. Additionally, in the validation process of the combined model at point C16, the residuals were notably smaller than those of individual models. This reaffirmed the accuracy and reliability of the proposed variable-weight combined model. Given that the variable-weight combination model was an evolution from individual time function models, its applicability extends to a broader range, offering valuable guidance for the dynamic prediction of surface movement and deformation in mining areas.

Stability analysis of pumped storage hydropower plant in abandoned open-pit mine affected by dynamic surface subsidence of combined mining.

The construction of a pumped storage hydropower plant (PSHP) in an abandoned open-pit mine is a potential alternative to green mining and energy storage, which can increase the utilization rate of renewable energy and develop residual resources of abandoned mines. Dynamic surface subsidence affected by combined underground and open-pit mining (CUOPM) seriously affects the construction and operation of the PSHP and is one of the critical scientific issues that needs to be solved immediately. The stability of the PSHP was analyzed and treatment scheme of the goafs was proposed based on on-site measurement, theoretical analysis, and numerical simulation. First, the distribution of goafs in the Haizhou open-pit mining area was investigated and surface subsidence value was obtained using InSAR technology and ground monitoring. Secondly, the surface subsidence mechanism affected by CUOPM is analyzed and indicates the subsidence maximum values and scope of influence are greater than those of single underground mining. A dynamic surface subsidence prediction model for combined mining is established based on the Knothe time function model. Thirdly, based on the CVISC model, the numerical calculation models were established by using FLAC3D, and the characteristics and laws of surface subsidence in different periods of CUOPM were studied. The comparative analysis of the observation results shows that the proposed model and numerical simulation calculation method have excellent applicability and accuracy. Finally, a stability evaluation method of PSHP was established, and the results of the evaluation show that the affected areas are the semi-ground powerhouse (SGPH) and the west side of the lower reservoir. The method of grouting filling was used to treat the goafs, and the results showed that it effectively alleviates the dynamic surface subsidence affected by CUOPM, and provides a safety guarantee for PSHP.

Prediction of surface settlement around subway foundation pits based on spatiotemporal characteristics and deep learning models

To obtain more accurate surface settlement prediction results around subway foundation pits, a novel spatiotemporal deep hybrid prediction model (STdeep model) that includes both the main and spatiotemporal blocks is proposed. The main block is constructed using the convolutional long short-term memory neural network, and the fundamental patterns of the surface settlement data from a network are extracted using the main block. To obtain more detailed spatiotemporal characteristics of the surface settlement data from a network, a spatiotemporal block is established. The convolutional neural network and the gated recurrent unit neural network are introduced into the spatiotemporal block to obtain the dynamic spatial and temporal characteristics of surface settlement data. To deeply integrate the main and spatiotemporal blocks, a dual-stream structure is designed. In addition, two cases are designed to test the STdeep model. The experiments indicate that the spatiotemporal relationships among the surface settlement data obtained from different monitoring points in a network are dynamic during the process of subway foundation pit excavation, and the STdeep model exhibits the best performance and excellent stability.

Monitoring and Prediction of Surface Subsidence in Mining Areas by Integrating SBAS-InSAR and ELM

Monitoring and Prediction of Surface Subsidence in Mining Areas by Integrating SBAS-InSAR and ELM

Surface Settlement Prediction of Rectangular Pipe-Jacking Tunnel Based on the Machine-Learning Algorithm

Surface Settlement Prediction of Rectangular Pipe-Jacking Tunnel Based on the Machine-Learning Algorithm

Prediction method of longitudinal surface settlement caused by double shield tunnelling based on deep learning

The deep learning method faces the challenges of small sample data and high dimensional shield operational parameters in predicting the longitudinal surface settlement caused by shield excavation. In this study, various optimization algorithms were compared, and the slime mould algorithm (SMA) was optimally chosen to optimize the hyperparameters of random forest (RF), and SMA-RF was used for dimensionality reduction and feature contribution analysis. A double-input deep neural network (D-DNN) framework was proposed for the prediction of surface settlement, which considers the influence of twin tunnels and effectively increases the high-fidelity data in the database. The results show that SMA performs best among various optimization algorithms; employing features that have a cumulative contribution value exceeding 90% as input can result in high prediction accuracy; there is significant uncertainty in the feature contribution analysis for small sample data; the reduced shield running parameters show a strong nonlinear relationship with surface settlement; compared with S-DNN, D-DNN takes into account the excavation of twin tunnels and expands the database capacity by more than 1.5 times, with an average increase of 27.85% in the R2 and an average decrease of 53.2% in the MAE.

Prediction of surface subsidence in Gequan coal mine based on probability integral and numerical simulation

Prediction of surface subsidence in Gequan coal mine based on probability integral and numerical simulation

Investigation of Empirical and Analytical Methods Accuracy for Surface Settlement Prediction in Train Tunnel Excavation Projects

Investigation of Empirical and Analytical Methods Accuracy for Surface Settlement Prediction in Train Tunnel Excavation Projects

Bayesian updating for ground surface settlements of shield tunneling

Accurate prediction of ground surface settlements induced by shield construction is of great significance for ensuring the safety of shield construction. This paper proposes a ground surface settlement prediction method for shield tunneling based on Bayesian updating. The sequential observation data during the advance of excavation is utilized to update the key soil parameters, leading to a more accurate settlement prediction for the subsequent excavation stages. Response surfaces are constructed to replace the finite element model as the forward models for higher computational efficiency. A tunnel excavation project in Hangzhou, China, is selected to illustrate the effectiveness of the proposed method. The shield excavation face passes through four soil layers, and two soil parameters (i.e., Young’s modulus and friction angle) of these soil layers are selected as random variables to be updated. The results show that the soil parameters can be effectively updated based on the observation data at multiple points and various excavation stages. The predictions of ground surface settlements are improved by using the updated soil parameters. The prediction accuracy of the proposed method increases as more stages of observation data are sequentially obtained and incorporated.

Analysis and prediction of surface settlements during the digging of underground mining works (Algeria)

Purpose. To analyze, study and predict surface settlements during the digging of a tunnel in an urban area located in the Algerian capital and to take the necessary measures. Methodology. Based on the physical and mechanical parameters and the geological characteristics of the actual traversed layer, and taking into account the geometric parameters of the tunnel, the mechanical model is established, and the numerical simulation is designed to determine the settlement deformation and displacement of the overlying zone under mining disturbance. Findings. Due to the impact of the excavation works, the land will undergo large deformations such as collapse. So, it is necessary to take corrective measures to limit its effect on the surrounding environment and protect urban areas. Originality. The application of a complex of methods allowed providing a predictive assessment of the safety of mining workings in urban conditions. The study was conducted in two main stages; a geotechnical characterization in situ and in the laboratory to determine the necessary properties of the soil and rock mass used in our model, and in a second step, the development of feedback analysis using numerical modelling based on the data collected. Practical value. From this study, the results obtained seem to show vertical displacements that exceed international standards in urban areas (1/1000, which can induce significant ground movements and therefore an influence on the surrounding environment. As a solution, there is a possibility of reducing the deformations by improving the mechanical properties of the soil carrying the project using the Jet-Grouting technique – the technique has shown its effectiveness in reducing settlements with a reduction rate of 78 %.

A Novel Surface Subsidence Prediction Model Based on Stochastic Medium Theory for Inclined Coal Seam Mining

Most coal resources are deposited in the form of inclined coal seams, and surface subsidence basin morphology induced by the mining of inclined coal seams is frequently skewed. The probability integral method with symmetrical distribution characteristics is widely used at present in surface subsidence prediction in coal mining in China. However, this method performs poorly when the inclined coal seam mining subsidence is predicted, and prediction accuracy decreases considerably with an increase in coal seam inclination. To solve this problem, this study first establishes three coordinate systems: a working surface rectangular coordinate system, a working face body-following coordinate system, and a surface rectangular coordinate system. Then, a random medium theory is applied to realize the superposition integral operation of the subsidence influence of unit mining in the working faces body-following coordinate system. Subsequently, the subsidence effect of a certain point on the surface is converted into the surface rectangular coordinate system. Finally, the inclined coal seam mining subsidence prediction model is constructed under the surface rectangular coordinate system. Results show that the surface subsidence caused by the mining of the inclined coal seam units conforms to the Weibull polar distribution law, and the effectiveness of the prediction model is verified through examples. The relative mean squared error of the prediction is less than 10%. The results of the study can provide theoretical and technical support for the subsidence prediction of similar mining areas.

Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination

The accurate prediction of mining area surface deformation is essential to preventing large-scale coal mining-related surface collapse and ensure safety and daily life continuity. Monitoring subsidence in mining areas is challenged by environmental interference, causing data noise. This paper employs the Sparrow Search Algorithm, which integrates Sine Cosine and Cauchy mutation (SCSSA), to optimize variational mode decomposition (VMD) and combine multi-models for prediction. Firstly, SCSSA is employed to adaptively determine the parameters of VMD using envelope entropy as the fitness value. Subsequently, the VMD method optimized using SCSSA adaptively decomposes the original mining area subsidence data sequence into various sub-sequences. Then, SCSSA-VMD is applied to adaptively decompose the original mining subsidence data sequence into multiple sub-sequences. Meanwhile, using sample entropy, the sub-sequences are categorized into trend sequences and fluctuation sequences, and different models are employed to predict sub-sequences at different frequencies. Finally, the prediction results from different sub-sequences are integrated to obtain the final prediction of mining area subsidence. To validate the predictive performance of the established model, experiments are conducted using GNSS monitoring data from the 110801 working face of Banji Coal Mine in Bozhou. The results demonstrate the following: (1) The hybrid model enhanced the prediction accuracy and trends by decomposing the data and optimizing the parameters with VMD. It outperformed single models, reducing errors and improving predictive trends. (2) The hybrid model significantly improved the prediction accuracy for subsidence data at work surface monitoring stations. It is particularly effective at critical subsidence points, making it a valuable reference for safety in mining operations.

Prediction of surface settlement caused by synchronous grouting during shield tunneling in coarse-grained soils: A combined FEM and machine learning approach

This paper presents a surrogate modeling approach for predicting ground surface settlement caused by synchronous grouting during shield tunneling process. The proposed method combines finite element simulations with machine learning algorithms and introduces an intelligent optimization algorithm to invert geological parameters and synchronous grouting variables, thereby predicting ground surface settlement without conducting numerous finite element analyses. Two surrogate models based on the random forest algorithm are established. The first is a parameter inversion surrogate model that combines an artificial fish swarm algorithm with random forest, taking into account the actual number and distribution of complex soil layers. The second model predicts surface settlement during synchronous grouting by employing actual cover-diameter ratio, inverted soil parameters, and grouting variables. To avoid changes to input parameters caused by the number of overlying soil layers, the dataset of this model is generated by the finite element model of the homogeneous soil layer. The surrogate modeling approach is validated by the case history of a large-diameter shield tunnel in Beijing, providing an alternative to numerical computation that can efficiently predict surface settlement with acceptable accuracy.

Surface Subsidence Prediction Method for Backfill Mining in Shallow Coal Seams with Hard Roofs for Building Protection

The mining of shallow coal seams with hard roofs poses a threat to surface structures. In order to ensure the protection of these buildings, backfill mining is increasingly used in these types of coal seams. However, due to the lack of appropriate surface subsidence prediction methods, there are concerns about whether backfill mining can meet the requirements of building protection. In this study, through numerical simulation and physical experiments, the movement characteristics of the strata and surface were studied in the backfill mining of a shallow coal seam with a hard roof. Our results indicate that the backfilling ratio significantly influences strata movement and surface subsidence. As the backfilling ratio increases, the surface deformation in the backfill under the hard roof of the shallow coal seam transitions from discontinuous to continuous. When the backfilling ratio exceeds 60%, the deformation characteristics of the overburden and surface align with the probability integral method model. Consequently, a novel surface subsidence prediction method for backfill mining in shallow coal seams under hard roofs is proposed. This method was successfully applied at Yungang Mine, validating its effectiveness. These research findings have significant practical implications for the design of backfill mining in shallow coal seams under hard roofs.

Prediction of Surface Settlement in the Cross-passage by FIG-SVM Approach

Introduction: An optimized support vector machine prediction model (SVM) with fuzzy information granulation (FIG) is established for surface prediction in this paper. Based on the measured data, cubic spline interpolation was processed and FIG approach was applied to granulate the surface settlement parameters into fuzzy particles Low, R and Up, and the particles are used to represent the range of the measured data variation. Method: For each fuzzy particle, particle swarm optimization (PSO) was used to select the best penalty and kernel function parameters to minimize the K-fold cross-validation (K-CV) error. With the optimized parameters, the prediction model was trained for the nonlinear prediction of fuzzy particles. Finally, the surface settlement trend in terms of Low, R, Up for the next construction stage can be predicted effectively based on the previous test data. Taking the surface settlement monitoring point D5-5 as an example, the error of the predicted range of surface settlement at D5-5 is 5.82%, 5.42%, and 8.0%, respectively Result: The error of other predicted points is also less than 10%, indicating the effectiveness of the prediction model. Compared with the numerical simulation results, the accuracy of the prediction model was further verified. Conclusion: Combined with the simulation method, the "simulation - prediction" patent scheme for monitoring the surface settlement of the Cross Passage is proposed in this paper. The research results indicate that the model proposed in this paper can easily and effectively predict the range and trend of changes in surface settlement, and is suitable for practical engineering applications.

Application of the Time Function Model for Dynamic Deformation Prediction in Mining Areas under Characteristic Constraints

The fundamental model for dynamically predicting surface subsidence is the time influence function. However, current research and the application of time functions often neglect the comprehensive characteristics of the entire surface deformation process, leading to a less systematic representation of the actual deformation law. To rectify this, we explore ground point deformation along the strike line from two perspectives: dynamic subsidence and dynamic horizontal movement. Moreover, we develop prediction models for dynamic subsidence and dynamic horizontal movement at any point along the strike line, utilizing the probability integral method (PIM) and considering the surface deformation features. We then use characteristic constraints based on the prediction models to constrain the time influence function. For this purpose, we employ the Richards time function which has strong universality to establish the time functions for dynamic subsidence and horizontal movement under these constraints. We provide an illustrative example of its application in the 12,401 working face. Additionally, we explore the suitability of interferometric synthetic aperture radar (InSAR) technology for acquiring dynamic subsidence data on the surface. The experimental findings reveal the following key observations: the Richards model, when applied for dynamic subsidence prediction under constraints, exhibits high accuracy with an R-squared (R2) value of 0.997 and a root mean squared error (RMSE) of 94.6 mm, along with a relative mean square error of 1.9%. Meanwhile, the dynamic horizontal movement prediction model exhibits an accuracy in fully mined areas with an R2 of 0.986, an RMSE of 46.2 mm, and a relative mean square error of 2.6%.