Settlement Prediction Research Articles

Research on the establishment of a mining subsidence prediction model under thick loose layer and its parameter inversion method

Most of the coal mining in China is underground, which will inevitably cause surface deformation and trigger a series of geological disasters. Therefore, it is essential to find a suitable method to forecast the ground sinking caused by underground mining. The most commonly used prediction model in China is the probability integral model (PIM). But when this model is used in the geological condition of mining under thick loose layers, the predicted edge of the sinking basin will converge faster than the actual measured sinking situation. A geometric model (GM) with a similar model shape as the PIM but with a larger boundary value was established in this paper to solve this problem. Then an improved cuckoo search algorithm (ICSA) was proposed in this paper to calculate the GM parameters. The stability and reliability of the ICSA were verified through a simulated working face. At last, the ICSA, in combination with the GM and the PIM, was used to fit 6 working faces with the geological mining condition of thick loose layers in the Huainan mining area. The results prove that GM can solve the above-mentioned PIM problem when it is used in geological mining conditions of thick loose layers. And it was obtained through comparative analysis that the GM and the PIM parameters can take the same value except for the main influence radius.

Thermo-fluid–mechanical numerical simulations of surface subsidence at the site of underground coal gasification

This work deals with modelling surface subsidence that aims to help industrialize underground coal gasification (UCG). UCG is a long-known, but poorly industrialized, method of energy extraction from coal. Risks of surface subsidence and groundwater pollution are two main hurdles that are affecting the potential industrialization of UCG. The particular challenge is the existence of groundwater because of implications to both its pollution and its influence on surface subsidence. Additionally, the coal combustion and the complex geometry of the UCG reactors impacts surface subsidence. To meet these challenges, the thermal and fluid analyses should be included in the model, and surface subsidence should be modelled in three dimensions to capture the collapsed shape of the UCG reactor. Based on the nature of these challenges and an earlier successful implementation, Itasca's commercial FLAC3D software with intrinsic thermal and fluid models was chosen to model surface subsidence. This study determines that the inclusion of fluid analysis improves the predictions of surface subsidence when compared with measurements at the highly watered Shatsk UCG site. In turn, thermal analysis mildly influences the modelled surface subsidence. The fluid analysis shows that the flow in the upper aquifer influences surface subsidence to a greater extent than the flow in the lower aquifers. High temperatures cause an upward flow in the lower aquifer located above the UCG reactor but does not change the flow pattern in the upper aquifer. The fluid analysis also reveals that if the UCG reactor is filled with water, surface subsidence does not occur.

Determination of the ultimate consolidation settlement of jack-up spudcan footings embedded in clays

When jack-up platforms are deployed for long-term services such as those functioning as production units, the consolidation settlement of spudcan footings is an issue of particular concern. Unpredicted and thus unprepared settlement may do harm to the serviceability and safety of jack-up rigs. Through centrifuge experiment and large deformation finite element calculation, this paper first illustrates the inherent correlation between the excess pore pressure dissipation inside soil and spudcan settlement development. With the measured and calculated settlement time histories, subsequent efforts are directed towards exploring how to predict the ultimate consolidation settlement with accuracy. Particular attentions are placed on investigating the applicability and advantages of two observational methods: Asaoka's method and Hyperbolic approach. Their prediction performances for spudcan with various soil, loading, embedment and geometrical conditions are symmetrically evaluated through numerical parametric investigation. The upper and lower limits of their prediction errors are established. Explicit recommendations are given on the selection of appropriate prediction approaches for different consolidation conditions. The practical significance of these research outcomes lies in 1) providing guidance on the selection of settlement prediction approaches; 2) establishing the limits of prediction errors involved. These are expected to be particularly useful for the field monitoring of spudcan settlement.

A novel soil-pile interaction model for vertical pile settlement prediction

In the past decades, many continuum-mechanics-based soil-pile interaction models have been proposed for static or dynamic loading problems. However, considering the difficulty to solve the nonlinear continuum mechanics problem analytically, most of them are applied to study the linear elastic problems. Therefore, Load Transfer Functions whose coefficients are generally concluded from the experimental results are alternatively used in the nonlinear behavior studies. In this paper, a coupled soil-pile interaction model is proposed to predict the pile head settlement. The model is established under ideal elastoplastic assumption and unifies the computation of end bearing and floating piles without involving any parameters with ambiguous physical meanings. The reliability of the present solution is verified through the comparisons with in-field results. At last, a detailed parametric study is given to exhibit the development of skin friction around the pile shaft. And, the potential penetration failure is alerted for end-bearing piles with large Young's modulus.

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.

Prediction of InSAR deformation time-series using a long short-term memory neural network

ABSTRACT The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.

A novel methodological approach for land subsidence prediction through data assimilation techniques

Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and approximations are present, as in any other modeling activity of real-world phenomena. This study aims at combining data assimilation techniques with a physically-based numerical model of anthropogenic land subsidence in a novel and comprehensive workflow, to overcome the main limitations concerning the way traditional deterministic analyses use the available measurements. The proposed methodology allows to reduce uncertainties affecting the model, identify the most appropriate rock constitutive behavior and characterize the most significant governing geomechanical parameters. The proposed methodological approach has been applied in a synthetic test case representative of the Upper Adriatic basin, Italy. The integration of data assimilation techniques into geomechanical modeling appears to be a useful and effective tool for a more reliable study of anthropogenic land subsidence.

Application of a superposition model to evaluate surface asymmetric settlement in a mining area with thick bedrock and thin loose layer

Surface subsidence caused by underground mining is a complicated spatiotemporal process. This study establishes a novel mining subsidence superposition model using variable mining thickness of coal seam by combining two models based on rock rheology theory and the Weibull subsidence method. This model better characterises the surface subsidence mechanism under geological conditions of thick bedrock and thin loose layer, and the prediction results are close to actual measurements. The bedrock and loose layer are considered viscoelastic beam and homogeneous body, respectively. The bedrock and loose layer are analysed using the theories of mechanical movement and mathematical models, respectively. Based on actual geological conditions of a mine in East China, the mechanical parameters were substituted into the Weibull composite subsidence prediction model (WCSPM) and the probability integral composite subsidence prediction model (PICSPM), and the predictions of surface subsidence movement parameters were obtained. The prediction results of the WCSPM are closer to the measured surface subsidence values, thereby verifying the applicability of the theoretical model in engineering practice. This model enables the construction of structures in mining-induced subsidence areas with confidence.

Measurement and prediction of tunnelling-induced ground settlement in karst region by using expanding deep learning method

This paper presents the measurement and prediction of the tunnelling-induced surface response in karst ground, Guangzhou, China. A predictive method of ground settlement is proposed named as the expanding deep learning method. This method kinetically uses the expanding tunnelling data to predict ground settlement in real time. Four types of deep learning methods are compared, including artificial neural network (ANN), long short-term memory neural networks (LSTM), gated recurrent unit neural networks (GRU), and 1d convolutional neural networks (Conv1d). Based on static Pearson correlation coefficient, a kinetic correlation analysis method is proposed to evaluate the variable significance of input data on the ground settlement. The effect of cemented karst caves and variable geological conditions are then analysed. The results indicate that the expanding Conv1d model precisely predict the tunnelling-induced ground settlement. The kinetic correlation analysis can reflect the variable influence of geological condition and tunnelling operation parameters on the ground settlement.

Mining subsidence prediction for multi-seam and non-rectangular goafs based on probability integral model: a case study from China

Mining subsidence prediction for multi-seam and non-rectangular goafs based on probability integral model: a case study from China

Proposal for considering the group effect in the prediction of settlements in pile groups through load transfer methods

When designing a foundation project, it is necessary to ensure that all the elements meet both ultimate and serviceability limit states, which call for predictions of settlement and load capacity. The load transfer methods are a widely used alternative to estimate the load-settlement ratio of piles in the design of foundation projects. However, traditional load transfer methods do not consider the interactive effects between the elements in pile groups. This study proposes changes to the load transfer curves developed by Bohn et al. (2016), aiming to incorporate the group effect in the analysis of load-settlement relationships in pile groups. Comparisons between the predicted settlements obtained using the proposed method and the results of load tests performed by Dai et al. (2012) in Jiangsu, China, showed that the modifications proposed in this study agreed well with the experimental results for most of the analyzed groups.

Prediction of the failure point settlement in rockfill dams based on spatial-temporal data and multiple-monitoring-point models

The integrity of monitoring data is important in the study of the deformation law of rockfill dams. Environment, construction, aging, and other factors result in dam monitoring sensors malfunction at the initial stage of operation. Data collection discontinue leads to insufficient monitoring data. The huge amount of missing data is lager than traditional model training samples and increases the difficulty of data recovery in failure points. In this study, the multiple-monitoring-point (MMP) model is established to extend the number of training set samples. MMP model integrates spatiotemporal information to make predictions of long-term missing data in malfunctioning settlement sensors according to the corresponding relationship among the coordinate position, environment values, and settlement. Additionally, this paper presents the study of monitoring point selection and the clustering time period division in the MMP model. The spatiotemporal data clustering analysis is used as the measurement method to determine the settlement similarity to screen the appropriate data. Experiments on large-scale real dam deformation data demonstrate that the MMP model is suitable for the long-term data prediction of failures in rockfill dam settlement monitoring. After the spatiotemporal panel data clustering analysis, the model prediction accuracy is significantly improved. This model provides a new method for dam settlement prediction and analysis.

Nonlinear consolidation of soft foundation improved by prefabricated vertical drains based on elliptical cylindrical equivalent model

AbstractThis study builds up an elliptical cylindrical equivalent model (ECEM) of prefabricated vertical drains (PVD)‐improved foundation which incorporates the nonlinear variation of compressibility and permeability and the preconsolidate state of soil. An exact solution and a simplified solution are derived for the nonlinear consolidation model under a combined vacuum and surcharge preloading. Considering the stratification of improved soil and the reduction of vacuum pressure along PVD, a precise settlement prediction method is proposed. The rightness and applicability of present solutions are verified by carrying out a series of comparative analyses. Also, a parametric study is conducted to investigate the characteristics of nonlinear consolidation under different , , over‐consolidation ratios (OCRs). The results show that, when the applied load has no impact on the dissipation of excess pore‐water pressure, whereas a larger applied load would induce a quicker settlement rate; the consolidation rate would be underestimated if the over‐consolidated stress history is neglected, and greater OCR produces more obvious underestimation.

Experimental Study on Consolidation Characteristics of Compacted Loess

The prediction of foundation settlement is an important topic in loess filling engineering. Based on a filled foundation in Yan’an, China, this study explores the consolidation characteristics of compacted loess with different compaction energy and consolidation pressure through consolidation tests, analyzes the strain-time curve and refines the curve within 2 h, separates the primary and secondary consolidations, and obtains the critical time point between the primary and secondary consolidations. Deformation rate S t ′ and cumulative deformation St were introduced to analyze the S t ′ − St curve at the secondary consolidation stage; the secondary consolidation coefficient was employed to describe the secondary consolidation characteristics of compacted loess. According to the secondary consolidation characteristics, a prediction model of loess settlement considering different compaction energy and fill thickness was proposed, and the applicability of the model was further analyzed. The model will facilitate in guiding the design and construction of loess filling engineering.

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.

Modeling and prediction of earthquake-related settlement in embankment dams using non-linear tools

Modeling and prediction of earthquake-related settlement in embankment dams using non-linear tools

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.

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

Analysis and Prediction of Long-Term Settlement of Metro Shield Tunnel in Saturated Sand

Based on the existing monitoring data, this paper uses the matured data method to analyze, predict the long-term settlement of the subway shield tunnel, and comprehensively analyzes the long-term settlement of the subway shield tunnel in the saturated sand layer of Zhengzhou. Logical curve model, etc. is used to separately predict the long-term settlement of the Zhengzhou metro shield tunnel. Combining the prediction results to summarize the characteristics of the prediction model, a prediction model suitable for the long-term settlement of the subway shield tunnel in the sandy soil area represented by Zhengzhou is obtained, which can be used for the operation of the Zhengzhou subway tunnel. Management and similar engineering projects provide a certain reference basis.

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. In this paper, a new hybrid technique for predicting the settlement of GRSF has been proposed based on the combination of evolutionary algorithm, that is, grey-wolf optimisation (GWO) and artificial neural network (ANN), abbreviated as ANN-GWO model. For this purpose, the reliable pertinent data were generated through numerical simulations conducted on validated large-scale 3-D finite element model. The predictive power of the model was assessed using various well-established statistical indices, and also validated against several independent scientific studies as reported in literature. Furthermore, the sensitivity analysis was conducted to examine the robustness and reliability of the model. The results as obtained have indicated that the developed hybrid ANN-GWO model can estimate the maximum settlement of GRSF under service loads in a reliable and intelligent way, and thus, can be deployed as a predictive tool for the preliminary design of GRSF. Finally, the model was translated into functional relationship which can be executed without the need of any expensive computer-based program.