Surface Subsidence Prediction Research Articles

3D numerical simulations of tunneling induced soil deformations

The accurate prediction of the maximum surface subsidence caused by shallow tunneling in soil environments is a valuable criterion for safe design and operation, especially in urban areas. To investigate the surface subsidence due to tunneling, the simultaneous impact of depth and diameter of the tunnel in both saturated and dry conditions have been investigated using a FLAC3D Finite Difference Method. Six models having different diameters (7 m, 8 m, and 9 m), depths (12 m, 16 m, and 20 m), and face pressures (0.34 MPa, 0.36 MPa, and 0.38 MPa) were developed. A step-by-step excavation process of the tunnel advance was considered in the modeling to account for deformations ahead of the face and the shield’s effect. Results showed that depth and diameter induce a significant effect on the ground surface displacement values and crown displacement values. As face pressure increases, the effect of tunnel depth and diameter on surface and crown displacements decreases, and the effect of saturation did not change.

Maximum surface settlement prediction in EPB TBM tunneling using soft computing techniques

Earth pressure balance (EPB) TBMs are commonly used for soft ground tunneling in urban areas. In metro tunnels’ excavation, designing a comprehensive monitoring system to control surface settlement is essential to prevent damage to surface structures. The present study aims to develop new prediction models to estimate the ground surface settlement using two soft computing techniques, SVM and ANN-MLP, and a multiple variable regression model to develop the new empirical formulas. The TBM operational parameters collected from the Tehran metro line 6, South extension (TML6-SE) project have been applied to confirm the provided models. In the data analysis process, the relationships between various parameters (torque index, thrust index, and earth pressure) and the ground surface settlement are investigated. Moreover, several statistical evaluation criteria are implemented to evaluate the performance of the developed models. The results show that the predicted values are in good agreement with the real data. The results can be used for similar ground and TBM tunneling conditions.

Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China

Wuhan, one of China's megacities with rapid development, is facing serious surface subsidence. In this study, we combined MT-InSAR, geo-detector, and LSTM (Long Short-Term Memory) to achieve the monitoring, analysis, and prediction of surface subsidence in the main urban districts of Wuhan. The effectiveness of MT-InSAR in monitoring surface subsidence was validated against leveling results. During the monitoring period, the maximum subsidence velocity and uplift velocity were −53.3 mm/year and 18.0 mm/year, respectively. We identified six subsidence regions and explored their deformation characteristics. Further, we analyzed the relationship between the surface subsidence and influencing factors using the geo-detector in a quantitative manner. Our study revealed that the distance to soft soils had the greatest explanatory power on the subsidence. However, we also confirmed that subsidence was affected via coupling effects from multiple factors, suggesting a complex reinforcing relationship among influencing factors. The interaction between the distance to soft soils and the distance to karst collapse prone areas had the largest joint explanatory power on subsidence. Further, we constructed a data-driven LSTM model to predict and analyze the subsidence. The results showed that the LSTM model achieved great performance and presented strong universality, suggesting that it can be used for subsidence prediction in large geographic areas.

Subsurface settlements of shield tunneling predicted by 2D and 3D constitutive models considering non-coaxiality and soil anisotropy: a case study

Appropriate constitutive models and reliable excavation and support sequences are believed to be the major concern in using finite element (FE) analysis to simulate shield tunnel excavation. This paper presents systematic two-dimensional (2D) and three-dimensional (3D) FE analyses employing a number of constitutive models accounting for initial soil anisotropy and non-coaxial plasticity, as evidenced within site investigations from the Tsinghuayuan Tunnel of the Jing-Zhang high-speed railway in China. The aim is to assess the effects of both the initial soil anisotropy and non-coaxiality on longitudinal and transverse tunneling-induced surface settlements. It is shown that the excavation procedures combined with the degree of cross-anisotropy are key towards the accurate prediction of maximum vertical displacements from tunneling, matching field data. Knowledge of the initial soil strength anisotropy can further improve the shape prediction of the transverse tunneling-induced surface settlement troughs. When considering n = 0.6 and β = 0° in simulations, the transverse surface settlement trough obtained is almost coincident with monitored field data. Initial stiffness anisotropy used in the prediction of shield tunnel-induced surface settlements in sandy pebble soils does improve realism of results significantly. The maximum longitudinal settlement predicted by considering cross-anisotropy is larger than that predicted by its isotropic counterpart.

Cylindrical Caved Space Stability Analysis for Extension Prediction of Mining-Induced Surface Subsidence

Subsequent extension of surface subsidence after vertical caving leads to large-scale surface destruction, as well as associated geological hazards. The extension prediction for cylindrical caved space, which appears circular surface subsidence, is still an intractable issue, due to the absence of robust models. To fill such a research gap, this paper provides an analytical model for the depth and orientation where the shear failure of isotropic rocks around the caved space is firstly observed. The anisotropy of surrounding rocks is further involved to enable this model to analyze the slip failure along discontinuities in anisotropic stress state. The prediction for the extension of the surface subsidence in Xiaowanggou iron mine is conducted, and the comparison between the prediction and the observation in satellite images demonstrates the validity of the proposed model. Even though this model cannot provide a definite boundary after extension, the prediction for the orientation surface subsidence extends to contribute to mitigating the effect of geological hazards. Another contribution of this work is to provide guidance to mitigate the impact of surface subsidence on safety and environment, such as filling the interspace between large-sized caved rocks by dumping small-sized waste rocks or backfilling the caved space with waste rocks.

Prediction of ground surface settlement induced by twin shield tunnellings in urban conditions

Tunneling in urban areas is growing in response to the increased needs for efficient transportation. Many urban tunnels are constructed in soft ground at shallow depths. Metro tunnels are usually constructed as twin-parallel tunnels and their adjacent constructions may lead to surface deformation, affecting the surface environment and the safety of the tunnels. Shield tunnelling is a commonly used as construction technique because it is very effective in reducing ground deformations and thus damage to urban infrastructure. The paper presents a 3D simulation of shield tunneling machines via the finite element code Abaqus and analysis model of ground surface settlements induced by a construction of twin-parallel tunnels. The results show that ground surface settlements induced by a construction of the left tunnel causes surface settlements of about 22÷24 mm and after the construction of both tunnels, it will cause ground subsidence has the greatest value of 33÷35 mm.

A New Theoretical Method to Predict Strata Movement and Surface Subsidence due to Inclined Coal Seam Mining

The mining-induced strata movement and surface subsidence are closely related to the dip angle of coal seam. However, most surface subsidence prediction methods are empirical, and only suitable for nearly flat coal seam mining. In this paper, a new theoretical method is proposed to predict the strata movement boundary and surface subsidence caused by inclined coal seam mining, which considers the influence of key strata, rock quality and coal seam dip angle. The strata movement caused by inclined coal seam mining is generalized and described by three models: analogous hyperbola model (AHM), analogous hyperbola-funnel model (AHFM), and analogous funnel model (AFM). Considering the rock quality of roof and floor strata, the rock mass rating system is adopted to calculate the surface maximum subsidence and its location. Additionally, the distinct element method was used to assess the performance of the theoretical models. The numerical simulation results match well with theoretical predictions of strata movement boundary and surface subsidence. It is discovered that the appearance of surface subsidence troughs is obviously asymmetric. As the dip angle increases, the surface maximum subsidence decreases and its location is laterally displaced. When the dip angle is greater than 50°, the double subsidence troughs can be visualized clearly. Furthermore, the theoretical predictions of surface subsidence are verified by field measurements of two cases. As a result, the theoretical predictions of surface subsidence are greatly improved by comparing with the empirical method.

Optimized Adaptive Neuro-Fuzzy Inference System Using Metaheuristic Algorithms: Application of Shield Tunnelling Ground Surface Settlement Prediction

Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including (i) preprocessed and normalized (PPN), (ii) preprocessed and nonnormalized (PPNN), (iii) non-preprocessed and normalized (NPN), and (iv) non-preprocessed and nonnormalized (NPNN) datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value (RMSE) of 7.98 and a correlation coefficient value (CC) of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance (EPB) shield tunnelling.

Enhanced Subsurface Subsidence Prediction Model Incorporating Key Strata Theory

Subsurface strata movements and deformations associated with longwall mining operations in underground coal mines could cause various disturbances to subsurface mine structures, ground water bodies, and coalbed methane reservoirs. In order to assess those disturbances correctly and to design effective and efficient mitigation measures, it is necessary to develop an accurate prediction model for subsurface strata movements and deformations. Decades of research have demonstrated that variations of stratification and lithology in the overburden have significant influence on the subsurface strata movements and deformations during ground subsidence process. The key strata theory states that the thick and hard key strata serve as the backbone of the overburden and control the movements of the thin and soft weak strata located above them. From an engineering standpoint, the overburden strata are subdivided into several individual groups by the key strata with each group consisting of a key layer at the bottom and weak layers overlying it. A new subsurface subsidence prediction model considering the key strata effects on subsurface strata movements and deformations has been proposed. In this method, elastic modulus, specific weight, tensile strength, and uniaxial compressive strength (UCS) of each layer are used to identify the key layers and to determine the needed subsurface subsidence parameters. The influence function method, proven to be accurate and versatile for surface subsidence prediction, was employed to predict the subsidence on each of the overburden layers from mining horizon progressively upward to ground surface with the assumption that the predicted subsidence on a given layer serves as the subsidence source for the layer immediately above. The enhanced subsurface subsidence prediction model has been demonstrated with an actual case to show its applicability and improvement.

Surface Deformations Caused by the Convergence of Large Underground Gas Storage Facilities

The article presents a method of forecasting the deformation of the land surface over large fields of underground gas storage facilities located in salt caverns. The solution allows for taking into account many parameters characterising the operation of underground gas storage facilities, such as cavern processes (leaching, enlargement, operational, etc.), their depth, distribution, diameter, shape, and many others. The advantage of the applied method over other available options is the possibility of using it for large fields of caverns while keeping the calculations simple. The effectiveness of the method has been proven for predicted surface subsidence for the EPE field with 114 underground caverns. The hypothesis was compared with the measurement outcomes.

Modification of Peck Formula to Predict Ground Surface Settlement of Twin Tunnels in Low Permeability Soil

Accurate prediction of surface settlement induced by tunnel excavation is significant for preventing damage to existing structures under complex geological conditions. The Peck formula is currently considered as an efficient solution for surface settlement prediction. This paper proposes a modified Peck formula considering geological conditions to improve the accuracy of surface settlement prediction of twin tunnels. The asynchronization of the sinking rate and stability of the vault settlement and surface settlement within the river‐affected area may attribute to the groundwater drawdown caused by cofferdam construction on the river. A modified Peck formula is put forward with soil permeability and width‐controlling parameters involved. There is a small settlement at the center of the twin tunnels, making the settlement trough upward buckling, which is like a “W” shape. This situation can be accurately predicted by the modified formula with a significantly increased adjusted R‐square. The modified formula can accurately predict the surface settlement of tunnels excavated in low permeability soil layers with a permeability coefficient between 10−4 cm/s and 10−7 cm/s, especially in the groundwater drawdown environment. The reliability of the modified Peck formula is verified by other cases in Nanjing and Singapore.

Mine subsidence prediction using gene expression programming based on multivariable symbolic regression

Accurate prediction of surface subsidence becomes a significant challenge for active industrial companies in coal mining fields due to the importance of the economic impacts of longwall mining-induced subsidence. This article explores a new variant of genetic programming, namely gene expression programming (GEP). The GEP-based method is utilized to present a new mathematical formula for subsidence prediction in longwall coal mining. The derived model includes both geometrical and geological variables. The data set consists of field measurements obtained through 37 longwall panels of Ulan coal mine, NSW, Australia. The GEP-based model concluded satisfactory subsidence prediction outcomes compared to other empirical methods such as NCB, DMR, ACARP, and IPM. The predictive ability of the GEP-based models, which captures the complex nonlinear effects of the critical factors on the magnitude of subsidence, resulted in a statistically significant improvement in predictive capacity compared to the aforementioned empirical methods. The sensitivity analysis results indicated that Panel width and cover thickness with 31% and 23% were the most influential parameters in the proposed model. Also, the extracted seam thickness, thick layer location, and thick layer thickness had 19%, 16%, and 11% impact on the GEP proposed model, respectively.

Ground surface settlement response to subway station construction activities using pile–beam–arch method

A pile–beam–arch method is a shallow tunneling method suitable for constructing urban subway stations with dense surface traffic. The main construction process can be simplified into four stages: excavation of pilot tunnels, installation of piles and beams, installation of arches, and excavation of soil inside the subway station. This study uses numerical calculations to investigate the development law of ground surface settlement during the construction of subway stations. The results indicate that surface settlement gradually increases in the first three stages and rebounds after the fourth stage. Further, pilot tunnel excavation and arch installation are identified as key stages for controlling the surface settlement. Based on these facts, the effects of the sequence of pilot tunnel excavation and the spacing and cross-sectional area of the columns on the surface settlement are studied. The results show that the sequence of pilot tunnel excavation has a significant effect on the development of the surface settlement trough. Further, column spacing and surface settlement exhibit a linear relationship, wherein a shorter spacing leading to low surface settlement. The cross-sectional area of the column exhibits a nonlinear relationship with the surface settlement; the surface settlement is stable when the cross-sectional dimensions are 1.5. × 1.5 m. The Peck formula and stochastic medium theory are used to predict the surface settlement in the pilot tunnel excavation stage. The results are 30%–50% higher than that in the numerical calculation, which indicates their safety and conservatism. The results of this study provide a reference for the selection of construction parameters and the prediction of surface settlement in similar projects.

Improving Reliability of Prediction Results of Mine Surface Subsidence of Northern Pei County for Reusing Land Resources

The accurate prediction of mine surface subsidence is directly related to the reuse area of land resources. Currently, the probability integral method is the most extensive method of surface subsidence prediction in China. However, its prediction precision largely depends on the accuracy of the selected parameters. When the mining area lacks measured data, or the geological and mining conditions change, particularly for large-scale surface subsidence prediction, the reliability of the prediction of surface subsidence is poor. Moreover, there is a lack of a systematic summary of the correct selection of prediction parameters. Based on this, the paper systematically investigated the influence of geological and mining conditions on the prediction parameters of the probability integral method. The research findings were obtained via theoretical analysis. The research outcomes can provide a scientific basis for properly selecting the prediction parameters of the probability integral method. Last, the results of this paper can be applied to predict the surface subsidence of Pei County in the north, laying the foundation for the integration of Pei County.

Assessment of models to predict surface subsidence in the Czech part of the Upper Silesian Coal Basin - Case study

Assessment of models to predict surface subsidence in the Czech part of the Upper Silesian Coal Basin - Case study

Spatial estimate of ecological and environmental damage in an underground coal mining area on the Loess Plateau: Implications for planning restoration interventions

Estimation of the ecological and environmental damage is viewed as an essential tool for effective decision-making in ecological restoration, especially involving high-intensity coal mining and high ecological vulnerability on the Loess Plateau, China. Using the Datong mining area on the Loess Plateau as a case study, we propose an integrated spatial estimation based on surface subsidence prediction, geographic information system, and field survey. The implications for regional ecological restoration are analyzed. Based on this inspection, the magnitude of ecological and environmental damage depended on a combination of risk factors, other than a single factor or evaluation target. Still, the geological hazard was the primary impact factor, especially surface subsidence as a result of underground mining. The results suggest that areas of severe, moderate, and slight damage accounted for 9.87%, 80.09%, and 10.04%, respectively. The methodology used was effective in determining the ecological and environmental damage that is consistent with the spatial distribution of subsidence. The proposed integrated estimation provides targeted measures for active, progressive, and passive interventions to improve and renovate the post-reclamation mining ecosystem and ensure sustainable planning in underground mining areas.

Subsidence and heat propagation modeling on the underground coal gasification (Case study at Muara Enim Formation, South Sumatera)

One of the important issues to study underground coal gasification (UCG) is the prediction of surface subsidence. Several parameters that influence these conditions are the thickness of cap rock, the physical and mechanical characteristics, the structure condition, the minerals composition of the rock, and external conditions. This study had been carried out simulation and modeling to determine the level of surface subsidence risk and the effect of high temperatures due to the activities. The modeling results show that the thickness of the rock above the UCG coal seam greatly affects the surface subsidence. The depth is more than 200 m and found that the SF value is 1.59 which indicates UCG reactor depth of ≥ 200 m is safe from the risk of subsidence. From the characteristic aspect of the cap rock, the claystone types which not contain kaolinite minerals are more prone to collapse than those of contain kaolinite minerals. From this models, the gasifier at 150 m depth was estimated that there will be a decline of -7.23 m, and the minimum subsidence is at 275 m about 0.1 m. The heat propagation modeling results show that at 50 m the temperature is estimated to be 213- 289°C, but if the thickness of the cap rock is > 200 m depth, the temperature is around 29-28°C.

Strata movement and surface subsidence prediction model of deep backfilling mining

ABSTRACT Surface subsidence prediction is an important approach to evaluate the surface damages caused by backfilling mining activities, and is applied to guide the recovery of coal resources under buildings, railways, and water bodies. With the mining depth increasing, the mining-induced stress and bedrock ratio increase, which lead to a different strata movement rule from shallow mining. Therefore, the appropriate surface subsidence prediction method for deep backfilling mining needs to be developed. To solve this issue, the coupled simulation method is employed to investigate the features of strata structure, strata movements, and internal strata movement boundary of deep backfilling mining. The simulation results show that the overburden is relatively integrated, and the subsidence of overburden is continuous. Compared with shallow mining, the curve of internal strata movement boundary of deep backfilling is more moderate, which is in accordance with an exponential function. Based on the simulation results, the strata movement prediction model of deep backfilling mining is established with the combination of elastic foundation theory and subsidence space conservation principle, and the prediction subsidence curve basically coincides with the simulation result. This model is highly instrumental in ground environmental protection.

Subsidence prediction versus observation in Australia: A short comment

Over-prediction of subsidence levels of mined land may be an issue for getting mining approval and tighter conditions in some places can be imposed to better inform mining operations and planning. Under-prediction of subsidence, present safety, social and environmental consequences during or after longwall extractions have occurred. In this study we collected subsidence data from reports related to underground coal mining environmental impact statements (EIS) and end of panel reports, and compared predicted subsidence data with the observed data to compare the accuracy of prediction. From the limited data, while it was difficult to substantiate a strong relationship between the predicted and observed subsidence data and mining characteristics, we found that where mining is less than 220 m below the surface subsidence prediction generally matches well with the observed subsidence and the ratio is closer to 1 than those mined at greater depths. Subsidence prediction analysed for mines in the Gunnedah Basin are found to be generally better than those in the Sydney Basin.The results presented here are based on limited data available in the public domain and while more data is needed to prove or disprove the discussed hypotheses, we consider there to be value in extending EIS requirements. Importantly, publication of both prediction and observed subsidence measurements would provide suitable transparency for improved, future predictions, and to provide social and stakeholder assurances that would meet growing legislative requirements.

Ground Movements Prediction in Shield-Driven Tunnels using Gene Expression Programming

Introduction: The increase in population and traffic in metropolitan areas has led to the development of underground transportation spaces. Therefore, the estimation of the surface settlement caused by the construction of underground structures should be accurately considered. Several methods have been developed to predict tunneling-induced surface settlement. Among these methods, artificial intelligence-based methods have received much attention in recent years. This paper is aimed to develop a model based on Gene Expression Programming (GEP) algorithm to predict surface settlement induced by mechanized tunneling. Methods: For this purpose, Tehran Metro Line 6 was simulated numerically to investigate the effects of different parameters on the surface settlement, and 85 datasets were prepared from numerical simulations. Subsequently, several GEP models were implemented using the obtained datasets from numerical simulations and finally, a model with 30 chromosomes and 3 genes was selected as the optimum model. Results: A comparison was made between obtained maximum surface settlements by the proposed GEP model and numerical simulation. The results demonstrated that the proposed model could predict surface settlement induced by mechanized tunneling with a high degree of accuracy. Conclusion: Finally, a mathematical equation was derived from the proposed GEP model, which can be easily used for surface settlement prediction.