Prediction Of Ground Surface Settlement Research Articles

Predictions of ground surface settlement for shield tunnels in sandy cobble stratum based on stochastic medium theory and empirical formulas

This paper focuses on the prediction of ground surface settlement induced by shield tunnelling in sandy cobble stratum. Based on the stochastic medium theory, an analytical solution to predict the surface settlement is developed considering the difference between soil and tunnel volume loss. Then, the effects of tunnel geometries, influence angle and volume loss on the characteristics of surface settlement are discussed. Through back analysis, a total of 103 groups of field monitoring data of surface settlement induced by shield tunnelling in sandy cobble stratum are examined to investigate the statistical characteristics of the maximum settlement, settlement trough width and volume loss. An empirical prediction is presented based on the results of back analysis. Finally, the analytical solution and empirical expression are validated by the comparisons with the results of model tests and field monitoring. Results show that the soil at ground surface has an overall dilative response for most of the shield tunnelling in sandy cobble stratum. In addition, the developed analytical solution is applicable and reasonable for surface settlement prediction. Meanwhile, the proposed empirical formula also shows good performance in some cases, providing an approach or a reference for engineering designers to preliminarily evaluate the surface settlement.

Prediction of ground surface settlement induced by twin tunnelling in urban areas

Tunnelling in urban areas is growing in response to efficient transportation, many urban tunnels are constructed in soft ground at shallow depths. Urban tunnels are usually constructed as twin-parallel tunnels and their adjacent constructions may cause ground settlements that distort and damage the existing structures and utilities above the tunnel. In the past few decades, tunnel boring machines have been used to drill in increasingly difficult geotechnical conditions such as soft ground like soft clay. Metro Line 3 of the Hanoi metro system is designed of twin tunnels horizontally aligned in soft ground. The prediction of ground movements is an important part of the planning stage of any urban tunnelling project. This paper presents the results of numerical simulation by using ABAQUS finite element software to predict the vertical displacement at the surface caused by twin tunnelling of Hanoi pilot light metro line 03. According to numerical simulation results, the maximum vertical displacement at the surface caused by the left line tunnel and twin tunnels bore excavations is values of 12.8 and 21.3 mm, respectively. The maximum vertical displacement can be reached after the shield passes by a distance ranging from 30÷40 m. Twin tunnelling only affects the maximum vertical displacement at approximately 20÷30 m before excavation face tunnel. After the left line tunnel bore excavations, the magnitudes of the vertical displacement directly above the face tunnel (x = 0 m) is 7.9 mm coinciding with 61.7% of the maximum vertical displacement. After the twin tunnels bore excavations, The maximum vertical displacement directly above the face tunnel (x = 0 m) is 13.1 mm coinciding with 61.5% of the maximum vertical displacement.

Prediction of ground surface settlement by shield tunneling using XGBoost and Bayesian Optimization

Ground surface settlement caused by shield tunneling is a complex problem caused by multiple factors. Machine learning models can help in the nonlinear intelligent prediction of ground surface settlement caused by shield tunneling. At present, the Artificial Neural Network model and the Support Vector Machine model are the most widely used models for settlement prediction. Due to the black-box characteristics of these two models, they are inherently deficient in interpretability, which means it is difficult to provide guidance for engineering. To solve the problem of poor interpretability in the prediction of ground surface settlement using these two models, an ensemble learning algorithm called the XGBoost model is introduced. In order to select hyperparameters in XGBoost more efficiently, the Bayesian optimization is used for parameter search. In this study, 533 cases of ground surface settlement monitoring data from a shield tunnel construction project in a city were used. Compared with the prediction results of the ANN model and the SVM model, the XGBoost model has the advantages of prediction accuracy and interpretability, especially for the prediction of out-of-limit settlement points.

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.

Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil

The main purpose of this research is to predict the ground surface settlement in tunneling of a single circular tunnel with simultaneous changes in the mechanical properties of soil and geometrical properties of the tunnel section. In this research, numerical and parametric analysis of circular tunneling in frictional–cohesive saturated soil has been investigated using 2D finite element method by ABAQUS. In other words, the behavior of ground surface, considering to change the different values of depth-to-diameter ratio (H/D), soil cohesion, internal friction angle, permeability coefficient, and the influence of these variables on settlement of surface in each model, has been separately evaluated. Then, a multilayer perceptron (MLP) artificial neural network is designed to predict the ground surface settlement. MLP is a type of feedforward artificial neural network utilizing backpropagation technique for training phase, and the Levenberg–Marquardt method is used to reduce the errors and the distance between the network outputs and finite element method results. There are some independent variables in the input layer and a dependent variable in the output layer. The middle layer consists of seven neurons. Finally, the high potential of the artificial neural network with a correlation coefficient of 0.98 is shown in the prediction of ground surface settlement.

A theoretical study on ground surface settlement induced by a braced deep excavation

Ground surface settlement (GSS) is one of the major concerns in design and construction of a deep excavation. This paper proposes an analytical approach for the prediction of GSS induced by a braced deep excavation. Considering wall deformation and stress release, the problem investigated is formulated as a system of two second-order partial differential equations (Lame equations) with mixed boundary conditions based on the elastic theory. Taking advantage of the superposition principle, the mixed boundary conditions are decomposed into displacement and stress boundary. The separation of variables method is applied to solve the governing equations with displacement boundary, while the Fourier Transform Method is employed to derive the solution for the governing equations with stress boundary. A novel least-squares based method is proposed to transform the scatter data of wall deflection into a continuous function, which is used to determine the unknown coefficients in the solution. The validity of the proposed solution is checked by predicting the GSS of two well-documented cases and by comparing with some empirical approaches. Parametric studies are conducted to demonstrate the impact of the modulus ratio on the excavation responses.

Analysis of the Additional Stress and Ground Settlement Induced by the Construction of Double-O-Tube Shield Tunnels in Sandy Soils

Double-O-tube shield tunneling has attracted increasing attention because it offers cost-efficiency in underground construction. Prediction of ground surface settlement and the variety of additional stresses induced by shield construction is crucial to underground construction in metropolises since excessive settlement could trigger potential damage to the surrounding environment. The additional stresses induced by the propulsion of double-O-tube shields are calculated by means of the Mindlin’s equations of elasticity. The characteristics of additional stresses are analyzed with compound Gauss-Legendre integral arithmetic, and the frontal additional thrust, the lateral friction, and the ground loss are taken into account. Subsequently, based on field measurements, the maximum settlement coefficient and width of the settlement trough coefficient of the typical Peck formula are modified. The predictive curve of the Peck formula is closer to the engineering measured data than that of the typical formula. The cut-off functions of ground surface settlement caused by double-O-tube tunnel shield construction are proposed and can predict the shape of ground surface settlement, such as single peak or double peak. The correctness of the proposed functions is verified based on an engineering project.

Prediction methods on tunnel-excavation induced surface settlement around adjacent building

With the rapid development of urban underground traffic, the study of soil deformation induced by subway tunnel construction and its settlement prediction are gradually of general concern in engineering circles. The law of soil displacement caused by shield tunnel construction of adjacent buildings is analyzed in this paper. The author holds that ground surface settlement based on the Gauss curve or Peck formula induced by tunnel excavation of adjacent buildings is not reasonable. Integrating existing research accomplishments, the paper proposed that surface settlement presents cork distribution curve characters, skewed distribution curve characteristics and normal distribution curve characteristics when the tunnel is respectively under buildings, within the scope of the disturbance and outside the scope of the disturbance. Calculation formulas and parameters on cork distribution curve and skewed distribution curve were put forward. The numerical simulation, experimental comparison and model test analysis show that it is reasonable for surface settlement to present cork distribution curve characters, skewed distribution curve characteristics and normal distribution curve characteristics within a certain range. The research findings can be used to make effective prediction of ground surface settlement caused by tunnel construction of adjacent buildings, and to provide theoretical guidance for the design and shield tunnelling.

Neural Network Prediction of Ground Surface Settlement under the Influence of Open Pit Mining

In the process of open-pit mine stripping,the surface and slop that influenced by open-pit mining stripping, rock mass structure and lithology, blasting vibration, slope angle, groundwater and precipitation start to move and deform,which threatens the safe use of industrial and civil buildings adjacent to the open pit mining .In this paper, through the establishment of increasing the momentum of adaptive BP neural network,with the aid of existing sample data,we carried out on the network training, testing, and compared with the target, its precision is higher.The result showed that network is effective and reliable,and the trained network is applied to the observation point prediction,so we get the value of its settlement deformation in 2013.The method is expected to have the important meaning on the multi-factor coupling surface subsidence .

Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine

Accurate prediction of ground surface settlement is necessary for effectively controlling the settlement that develops during tunneling. Many models have been established for this purpose by extracting the relationship between the settlement and the factors that influence it. However, most of the models focused on the maximum ground surface settlement and do not involve dynamic and real-time predictions. This paper investigated how tunneling-induced ground surface settlement developed using a smooth relevance vector machine with a wavelet kernel (wsRVM). Various factors that affect this settlement, including geometrical, geological and shield operational parameters were considered. The model was applied to earth pressure balance (EPB) shield-driven tunnels. The results indicate that the prediction model performs well and that the distribution of the predictions can provide a measure of the prediction uncertainty. Unlike conventional methods that requireadditional efforts to determine relevant model parameters, the proposed method can optimize the parameters in the training process. The results of the parametric study conducted show that the model performance can be improved by the optimization and that the method can serve as a simple tool for practitioners to use in estimating ground surface settlement development during tunneling.

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.

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.

Shape of ground surface settlement profiles caused by excavation

There are two general types of settlement profiles induced by excavation, the spandrel type and the concave type. Based on a regression analysis of the field observations of settlement curves with predominantly cohesive subsurface conditions, an empirical method for predicting the spandrel and concave settlement profiles is proposed. The proposed method is verified through several case histories. For comparison, the results obtained from other empirical methods are also presented. The proposed method provides a fairly good prediction of ground surface settlement and yields better prediction of angular distortion than other empirical methods for the concave- and spandrel-type settlement profiles. Furthermore, the relationship of cantilever area and deep inward area of wall deflection is established which can be used as the first approximation to predict the type of settlement profile.Key words: prediction, excavation, settlement profile, spandrel, concave.