Surface Subsidence Prediction Research Articles

The Comparative Analysis of Probability Integration and Numerical Simulation in Surface Subsidence Prediction

This article based on Real measurement data of mine, then use Probability integration and Numerical Simulation that most commonly used methods in movement and deformation prediction to compute. By comparing the final sinking graph of the two methods and the actual data, to achieve the applicability and accuracy of two ways. The research have certain reference significance and revelatory effect on the selection of different methods of the surface subsidence prediction.

Study on Surface Subsidence Law Induced by Deep Mining of Large Steep Metal Deposit

To solve this difficult technological problem that predicts the surface subsidence that induced by deep mining of large steep deposit. Through understanding engineering geological characteristics and mining conditions for a certain mining area, the three-dimensional simulation model of deep mining with a high degree similarity in space and mechanics morphology have been established, and the exploitation of the mining area has been systematically simulated. After excavation of the third stage, the calculated values of surface subsidence coincided with the measured values of GPS. Then it has been more accurately predicted surface subsidence and displacement angle and the range of surface deformation which were induced by deep mining in fourth stage project. It provides the basis for the positioning of underground engineering and design of the range of ground movement in deep mining.

Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes

Increasing demand on infrastructures increases attention to shallow soft ground tunneling methods in urbanized areas. Especially in metro tunnel excavations, due to their large diameters, it is important to control the surface settlements observed before and after excavation, which may cause damage to surface structures. In order to solve this problem, earth pressure balance machines (EPBM) and slurry balance machines have been widely used throughout the world. There are numerous empirical, analytical, and numerical analysis methods that can be used to predict surface settlements. But substantially fewer approaches have been developed for artificial neural network-based prediction methods especially in EPBM tunneling. In this study, 18 different parameters have been collected by municipal authorities from field studies pertaining to EPBM operation factors, tunnel geometric properties, and ground properties. The data source has a preprocess phase for the selection of the most effective parameters for surface settlement prediction. This paper focuses on surface settlement prediction using three different methods: artificial neural network (ANN), support vector machines (SVM), and Gaussian processes (GP). The success of the study has decreased the error rate to 13, 12.8, and 9, respectively, which is relatively better than contemporary research.

Model for predicting ground surface settlement by field measuring and numerical analysis in shield TBM tunnel

In this study, more convenient model(S-model) for predicting ground surface settlement is developed through comparing field monitoring data of the domestic subway applied shield TBM method with conventional equation & numerical analysis. Sample stations are chosen from whole of excavation section and lateral & vertical ground surface settlement characteristic with excavation are analysed. Based on analysis result, through the comparison with actual monitoring data, the model that is possible to compute maximum surface settlement and settlement influence area is suggested with assumption that lateral surface settlement forms are composed relaxed zone and elastic zone. In addition, vertical ground surface settlement patterns with excavation are similar to cubic-function and S-model with assumption that coefficients are function of tunnel diameter and depth is suggested. Consequently, the ground surface settlement patterns are significantly similar to actual monitoring data and numerical method result. Thus, as a result, when tunnels are excavated using sheild TBM through rather soft weathered soil & rock layer, prediction of ground surface settlement with excavation using convenient S-model is practicable.

Surface subsidence prediction for the WUTONG mine using a 3-D finite difference method

WUTONG coal mine is adjacent to an auxiliary dam of the Yuecheng Reservoir. In this paper, mining-induced surface subsidence prediction is conducted by means of the finite difference method (FDM) to judge whether the extraction of the coal seam will have a negative impact on the dam. First, the initial values of the rock mass mechanical parameters are estimated using the available literature that relates intact rock and discontinuity properties to rock mass parameters. Then, based on available surface subsidence monitoring data on WUTONG’s mined areas, the main mechanical parameters of coal and rock masses are determined by a back analysis procedure that combines an experimental design technique with numerical simulations. Finally, the surface subsidence results in the mining area are numerically predicted for four different mining scenarios (S1 through S4). Scenario S3 emerged as the best choice of these four scenarios. The predictions are: (1) the maximum surface subsidence within the mining area is 2.14m, with the maximum settlement point located in the mid-west area of the coalfield, and (2) the nearest distance from the boundary of the surface movement area to the edge of the dam foundation is 35m. Therefore, mining the coal seam will not cause damage to the dam.

The Bearing Capacity of Excavated Pile under Load Test and Finite Element Analysis

This paper is used to study on the analysis and prediction of surface subsidence caused by pipe roof reinforcement method construction. Take one of the Shenyang's sewage disposal projects to be investigated, it monitored on field surface subsidence in the construction of under drain. During the measurement and monitoring, except for conventional measurement of surface subsidence according to code and peripheral convergence and so on, in order to analysis changes of stress and strain of the pipe roof in the whole construction of under drain. It also respectively arranges JMZX-212 intelligent string type of strain gauge in both internal and external sides of vault and hence. It used the Element Birth/Death of finite element software basing on the field data, using elastic-plastic and nonlinear finite element method. Material constitutive relationship used moor-coulomb model, simulating the process of tunnel of excavation and support, researching and stimulating the surface subsidence. The surface settlement of value was calculated applying finite theory via the numerical simulation, it based on prototype of engineering, which was approximately in accord with the measured value in field. The correctness of soil constitutive model chosen and boundary conditions used is verified, meanwhile, the numerical simulation shows the validity of the method .the results provide reference to similar construction method in the future.

The Analysis of Surface Subsidence Caused by Pipe Roof Reinforcement Method Construction in Hunhe Underdrain

This paper is used to study on analysis and prediction of surface subsidence caused by pipe roof reinforcement method construction. Take one of the Shenyang's sewage disposal projects to be investigated, it monitored on field surface subsidence in construction of underdrain. During the measurement and monitoring, except for conventional measurement of surface subsidence according to code and peripheral convergence and so on, in order to analysis changes of stress and strain of the pipe roof in whole construction of underdrain. it respectively arranges JMZX-212 intelligent string type of strain gauge in both internal and external sides of vault and hance. It used the Element Birth/Death of finite element software basing on field data, using elastic-plastic and nonlinear finite element method. Material constitutive relationship used mohr-coulomb model, simulating process of tunnel of excavation and support, researching and stimulating surface subsidence. Value of surface settlement was calculated via numerical simulation in applying finite element theory, it based on prototype of engineering, which was in accord with measurement value in field. The correctness of soil constitutive model chosen and boundary conditions used is verified, meanwhile, numerical simulation shows validity of method.

Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling

An alternative method of maximum ground surface settlement prediction, which is based on integration between wavelet theory and Artificial Neural Network (ANN), or wavelet network (wavenet), is presented. In order to minimize the risk of tunneling, a tunnel engineer needs to be able to make reliable prediction of ground deformations induced by tunneling. Any prediction from numerical analysis was highly dependent on the model adopted for modeling the soil behavior. However, setting up a realistic model that would be able to calculate tunneling-induced settlement profiles is rather difficult. Most of the researches show that the capability (i.e. pattern recognition and memorization) of an ANN is suitable for inherent uncertainties and imperfections found in geotechnical engineering problems considering its successful application without any restriction. Wavenet is a single hidden layer feedforward neural network, which uses wavelets as its activation functions. In this study different wavelets are applied as activation functions to predict the maximum surface settlement due to tunneling. Wavenet parameters such as dilation and translation are fixed and only the weights of the network are optimized during its learning process, which is performed by a back-propagation algorithm. The efficacy of this type of network in function learning and estimation is demonstrated through measurements extracted from EPB shield tunneling. The simulation results indicate decrease in estimation error values that depicts its ability to enhance the function approximation capability and consequently exhibits excellent learning ability compared to the conventional back-propagation neural network with sigmoid or other activation functions.

Modeling of Surface Subsidence Based on Time Function

Base on Knothe time function, a new time function was proposed by adding parameter which is approved more accurate in predicting surface subsidence than Knothe funciton. Combing this new time function with section function of surface subsidence basin, a dynamic model is proposed to predict surface subsidence induced by underground mining and a case study based on this model was carried out. The results illustrates this model is more accurate to reflect the dynamic process of surface subsidence

Water distribution extracted from mining subsidence area using Kriging interpolation algorithm

By comprehensively analyzing the data of geology and mining, Kriging algorithm was introduced to analyze the thematic information of geological data, to rapidly extract mining parameters for predicting mining subsidence, and to effectively integrate geomorphology and predict information. As a result, the change information of water body is successfully detected from the prediction of surface subsidence due to mining activity. Analysis shows that the elevation of farmland in the west side of water body will be lower than ever, and the west part farmland will be submerged. However, there is no evidence for impacting the villages. All the information provides a reference for efficiently assessing environmental impact due to mining activity, which can help to govern the subsidence of the area reasonably.

Numerical simulation of surface movement laws under different unconsolidated layers thickness

Based on specific geology and mining conditions of certain coal working face in China, a series of numerical models under different unconsolidated layers thickness were respectively established by employing FLAC 3D. The relationship between the unconsolidated layers thickness and surface movement laws was studied. Maximum surface subsidence, Maximum horizontal displacement and surface subsidence degree were obtained. Contours of surface subsidence/horizontal displacement and curves were drawn. Some laws of surface subsidence/horizontal displacement were analyzed. The role of the unconsolidated layers in surface subsidence was revealed. It is significant to predict surface subsidence of thick unconsolidated layers for coal mine and take effective measures to control surface subsidence.

Applicability of the principle of superposition in estimating ground surface settlement of twin- and quadruple-tube tunnels

Prediction of tunneling-induced ground surface settlement is important to underground constructions in the densely populated city because excessive settlement could trigger potential damage to existing structures. This paper examined the interaction of multi-tube tunnels on ground surface settlement and the methods of prediction for the transverse ground surface settlement profile induced by the closure and grouting of tail void of shield machine of multi-tube tunnels in the CH221 Contract of the Taipei Rapid Transit System. The transverse ground surface settlements of a twin- and a quadruple-tube tunnels at two sections of this contract were back analyzed using the empirical equation that assumed the settlement profile over a tunnel to be a Gaussian distribution curve, and the finite element analysis. The applicability of the principle of superposition in the analysis and the characteristics of the ground surface settlement for a green-field land (previously undeveloped land) and a brown-field site (previously developed site) was also studied using the finite element analysis. The results showed that the ground surface settlement profile of multi-tube tunnel could be conveniently and reliably estimated using the empirical equation together with the principle of superposition. Extra effort required in the model set-up and result interpretation caused the numerical method to be outperformed by the empirical method.

Dynamic Modeling of Surface Subsidence Induced by Underground Mining

A new time function was proposed to cover the disadvantage of knothe time function in predicting surface subsidence by adding parameter k. The velocity and acceleration of the progressive surface subsidence obtained from this new time function were compared with actual subsidence data from an iron mine in China. Combining this new time function with section function of surface subsidence basin, a dynamic mode was proposed to predict surface subsidence induced by underground mining and a case study based on this model was carried out. It shows that this model accurately represents the actual subsidence pattern observed. Instruction

Prediction of Shield Tunnelling-Induced Ground Settlement Considering Soil-Water Coupled and around Soil Disturbance Damage

to evaluate the influence of soil-water coupled and shield tunnel construction induced around soil disturbance damage on ground surface settlement, the process of shield tunnel construction induced around soil disturbance is analyzed, the FLAC3D numerical simulation are carried out, and a newly-modified tunnelling-induced ground settlement calculation method based on disturbance degree of around soil and soil-water coupled is presented, and these methods are applied in case of Beijing Metro 10thLine. It is indicated that considering the influence of the shield tunnelling-induced around soil disturbance damage, and soil-water coupled induced soil properties weakening and the excess pore water pressure dissipating induced soil consolidation to the ground surface settlement are necessary, the calculating result of the newly-modified surface settlement prediction method, and the result FLAC3D numerical simulation all agree well with in-site observed data of Beijing Metro 10th Line.

Fuzzy probability measures (FPM) based non-symmetric membership function: Engineering examples of ground subsidence due to underground mining

This paper introduces a new FPM method for prediction of surface subsidence due to inclined coal seam mining. Based on the non-symmetric membership function and the definition of the fuzzy probability measure, the mathematical model for the two-dimensional problem is developed and applied to the analysis of ground subsidence due to underground mining of inclined coal seam. The results of calculation with the new model indicate that the predicted value has good agreement with the measured data.

Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining

ABSTRACT The surface subsidence induced by mining is a complex problem, which is related with many complex and uncertain factors. Genetic programming (GP) has a good ability to deal with complex and nonlinear problems, therefore genetic programming approach is proposed to predict mining induced surface subsidence in this article. First genetic programming technique is introduced, second, surface subsidence genetic programming model is set up by selecting its main affective factors and training relating to practical engineering data, and finally, predictions are made by the testing of data, whose results show that the relative error is approximately less than 10%, which can meet the engineering needs, and therefore, this proposed approach is valid and applicable in predicting mining induced surface subsidence. The model offers a novel method to predict surface subsidence in mining.

Profiling Function for Surface Subsidence Prediction in Mining Inclined Coal Seams

A new profiling function is proposed to predict the value of surface subsidence over longwall panel in the inclined seams. This function was used at the Parvada-2 mine in Tabass coal region, Iran. The calculated results are compared with the real movements obtained by field measurements.

Neural-network-based regression model of ground surface settlement induced by deep excavation

Ground surface settlement is an important field measurement in deep excavation. The monitoring data are adopted to evaluate construction performance and to avoid large surface settlements incurred to adjacent structures. Due to the complicated geotechnical and construction factors affecting ground surface settlement, no single analytical method can accurately forecast ground surface settlement induced by deep excavation. This paper presents an artificial-neural-network-based (ANN-based) regression approach to the prediction of ground surface settlement induced by deep excavation. Case data of deep excavation projects recently finished in Taiwan were used to establish the model. Soil and construction-related parameters having significant influences on surface settlement were filtered to train and test the ANN. Validation was also performed to show that the ANN outperformed the multiple linear regression method in predicting ground surface settlement. The ANN-based forecast model can reasonably predict the magnitude, as well as the location, of maximum ground surface settlement induced by deep excavation.

Prediction of subsidence due to underground mining by artificial neural networks

Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network was used. The training and testing of neural network is based on the available data. Input variables represent extraction parameters and coordinates of the points of interest, while the output variable represents surface subsidence data. After the neural network has been successfully trained, its performance is tested on a separate testing set. Finally, the surface subsidence trough above the projected excavation is predicted by the trained neural network. The applicability of ANN for the prediction of surface subsidence was verified in different subsidence models and proved on actual excavated levels and in levelled data on surface profile points in the Velenje Coal Mine.

Prediction of progressive surface subsidence above longwall coal mining using a time function

Prediction of progressive surface subsidence above longwall coal mining using a time function