Ground Settlement need to be predicted well so that necessary precautionary measures could be adopted. Ground deformation behavior due to tunnel construction in inhomogeneous soil has been studied in the past few decades by many researchers. When tunnel-induced Ground, Settlement is predicted by considering average soil properties, it is likely to miss the true Settlement characteristics and failure mechanism due to the inherent heterogeneity of the Ground. In this paper, spatial variability of the Ground is considered in the numerical analysis to simulate the Ground Settlement. A numerical model is developed using the Finite-Difference based numerical code FLAC3D to simulate tunnel construction with earth pressure balance (EPB) TBMs for a case study. Both 2D and 3D random fields are simulated in the numerical model. Results are systematically compared with some of the empirical and analytical methods for predicting Ground Settlement. Spatial distribution is found to have a significant effect on surface Settlements and overall Ground behavior.

Nowadays, tunnel excavation plays a major role in the development of countries. Due to the complex and challenging Ground conditions, a comprehensive study and analysis must be done before, during, and after the excavation of tunnels. Hence, the importance of study and evaluation of Ground Settlement is dramatically increased since many tunnel projects are performed in urban areas, where there are plenty of constructions, buildings, and facilities. For this reason, the control and prediction of Ground Settlement is one of the complicated topics in the field of risk engineering. Therefore, in this paper, the proportional hazard model (PHM) is used to analyze and study the Ground Settlement induced by Tabriz Metro Line 2 (TML2) tunneling. The PHM method is a semi-parametric regression method that can enter environmental conditions or factors affecting Settlement probability. These influential factors are used as risk factors in the analysis. After establishing a database for a case study and using a proportional hazard model for surface Settlement analysis, and then by evaluating the effect of environmental conditions on the Ground surface Settlement, it has been found that the risk factors of grouting pressure behind the segment, the ratio of tunnel depth to Groundwater level, and drained cohesion strength at a significant level of 5% have a direct effect on the probability of Settlement. The results also showed that the effect of grout injection pressure on Ground subsidence is more than other parameters, and with increasing injection pressure, the probability of exceeding safe subsidence values decreases. In addition, it has been found that increasing the risk factor for the ratio of tunnel depth to Groundwater level reduces the probability of exceeding the safe Ground Settlement. Finally, increasing the number of risk factors for drained cohesion strength increases the probability of exceeding safe Settlement. 

Predicting the maximum Ground surface Settlement (MGS) beneath road embankments is crucial for safe operation, particularly on soft foundation soils. Despite having been explored to some extent, this problem still has not been solved due to its inherent complexity and many effective factors. This study applied support vector machines (SVM) and artificial neural networks (ANN) to predict MGS. A total of four kernel functions are used to develop the SVM model, which is linear, polynomial, sigmoid, and Radial Basis Function (RBF). MGS was analysed using the finite element method (FEM) with three dimensionless variables: embankment height, applied surcharge, and side slope. In comparison to the other kernel functions, the Gaussian produced the most accurate results (MARE = 0.048, RMSE = 0.007). The SVM-RBF testing results are compared to those of the ANN presented in this study. As a result, SVM-RBF proved to be better than ANN when predicting MGS.

In this article a brief review of the literature on the subject is cited. The method of ‎analysis by a finite element program is discussed in which the effect of different ‎influential parameters is examined. The results of these computations are then ‎compared to the corresponding empirical data and two other existing formulae for two ‎dimensional cases. The comparisons show quite reliable and acceptable agreement ‎between the results of the present computations and the results obtained by other ‎recently published formulae, and also with the existing observational data. Finally ‎some prepared design diagrams are presented for predicting the maximum values of ‎surface Settlement and the maximum Settlement of the tunnel crown‏.‏

Tunnel excavation on soil lands may leads to horizontal and vertical displacements around the tunnel. The displacements can reach the Ground surface and cause damages to existing structures on the Ground. Hence it is so important to estimate the Ground Settlement induced by excavation, particularly in urban environments. In this study, the effect of longitudinal distance between two tunnel faces on Ground surface Settlement is examined during the excavation of twin tunnels. Accordingly, the Ground Settlement is measured for the states where the distance between tunnel faces is 0D, 0. 5D, 1D, 1. 5D and 2D. The most important results suggest that creating a longitudinal distance (lagging) between the faces of twin tunnels during excavation operations causes changes in surface Ground Settlement. The maximum surface Ground Settlement along the width and length of tunnels decreases as the distance between two tunnel faces increases.

Near-fault seismic records strongly influenced by forward directivity are characterized by one or more large pulses in velocity time histories. The characteristics of these records are significantly different from ordinary records. This study evaluated the correlation between the intensity indices of pulse-like Ground motion and crest Settlement of embankment dams and their ranking according to quality was evaluated. The seismic behavior of five embankment dams with different heights was investigated under 105 pulse-like and 20 ordinary Ground motions with over 680 nonlinear time history analyses. The results showed that inelastic structural Ground motion intensity indices are more applicable than elastic indices to predict the Settlement of embankment dams. The proposed inelastic structural Ground motion intensity (IMPD) calculates the mean permanent displacement of a single degree of freedom (SDOF) system over a period of 0.2T1 to 1.5T1 (where T1 is the natural period of the dam) based on Newmark’s method using a decoupled approximation and deformable SDOF instead of a rigid block system. The non-structure Ground motion intensity index IVA (where IVA is the square root of the product of peak Ground velocity and plain integral of the squared acceleration) is proposed as a good predictor for the nonlinear response of embankment dams.