Tunneling-induced Ground Settlement Research Articles

An Optimized XGBoost Model for Predicting Tunneling-Induced Ground Settlement

An Optimized XGBoost Model for Predicting Tunneling-Induced Ground Settlement

Assessment of Impact to Buried Pipelines due to Tunneling-Induced Settlement

Tunneling in urban environments is challenging due to the number of pipelines to cross, which may adversely impact the pipelines due to tunneling-induced ground settlement. An adequate vertical separation distance between the bottom of the pipeline and tunnel crown is not always possible, and a minimum volume loss may be difficult to achieve due to subsurface conditions encountered and tunneling methods available. Therefore, the impact to the existing pipelines from tunneling needs to be assessed to mitigate the risk. Considering gap formation beneath the existing pipeline explicitly and a limiting overburden loading acting on the pipeline, this study proposes a design-oriented analytical approach to estimate the upper bound of the maximum bending moment of the pipeline induced by tunneling. A dimensionless quantity, namely, the overburden loading factor defined as the ratio of overburden loading and the minimum required loading to eliminate the gap, is introduced to evaluate the extent of gapping, by which gap formation can be predicted. The effect of pipe-soil interaction, which reduces the maximum bending moment in the pipeline, is considered by conducting nonlinear Winkler-based finite element analyses with varying stiffness factors. The design chart and table are presented to provide a more accurate estimation of the maximum bending moment, especially for greater stiffness factors. It is demonstrated for small overburden loading and stiffness factors, i.e., wider gaps, a perpendicular crossing may not always be the worst-case scenario. As illustrated by the partially supported beam model, the effect of the increase in the unsupported span is greater than the effect of the reduction of the curvature of the settlement trough for these cases. Design examples are provided to demonstrate the analytical procedure step-by-step, and a comparison with previous studies is made. It is demonstrated with reasonable conservatism for small to moderate stiffness factors, the proposed design-oriented analytical approach can straightforwardly predict the maximum bending moment in pipelines.

Vertical mechanical response of axial loaded pile to tunneling-induced pile exposure

Construction of tunnels in urban cities may inevitably pass frontally through existing piles, causing pile exposure. This tunneling-induced pile exposure will cause the loss of soil resistance and redistribution of ground stress, posing a risk to the safety of existing pile. However, few studies have been carried out on such cases. This study conducted a theoretical analysis based on the two-stage method to clarify the mechanical response of pile due to tunneling-induced exposure, which calculates the tunneling induced ground settlement and the ground settlement induced pile response in two separate stages. The displacement compatibility equation of the pile at the exposure part was adjusted to account for the loss of pile-soil interaction. The results show that the overall pile deformation behaves as a uniform settlement. The additional skin friction is negative above the exposed part and positive below, resulting in the whole pile being under pressure. The axial force increases with depth above the exposed part and decreases below, while remaining unchanged at the exposed part. The pile vertical response is strongly affected by both the pile top load and the ground loss. Limiting the pile’s exposed length can significantly reduce the adverse effects of tunnel excavation.

Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction

Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate ground settlement prediction has great significance in ensuring the surface building’s stability and human safety. To that end, 148 sets of data were collected from the Singapore Circle Line rail traffic project containing seven defining parameters to create a database for predicting ground settlement. These parameters are the tunnel depth (H), the tunnel advance rate (AR), the EPB earth pressure (EP), the mean SPTN value from the soil crown to the surface (Sm), the mean water content of the soil layer (MC), the mean modulus of elasticity of the soil layer (E), and the grout pressure used for injecting grout into the tail void (GP). Three hybrid models consisting of random forest (RF) and three types of meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA), were developed to predict ground settlement. Furthermore, the mean absolute error (MAE), the mean absolute percentage error (MAPE), the coefficient of determination (R2) and the root mean square error (RMSE) were used to assess predictive performance of the constructed models for predicting ground settlement. The evaluation results demonstrated that the GOA-RF with a population size of 10 has achieved the most outstanding predictive capability with the indices of MAE (Training set: 2.8224; Test set: 2.3507), MAPE (Training set: 40.5629; Test set: 38.5637), R2 (Training set: 0.9487; Test set: 0.9282), and RMSE (Training set: 4.93; Test set: 3.1576). Finally, the sensitivity analysis results indicated that MC, AR, Sm, and GP have a significant impact on ground settlement prediction based on the GOA-RF model.

Mapping Tunneling-Induced Uneven Ground Subsidence Using Sentinel-1 SAR Interferometry: A Twin-Tunnel Case Study of Downtown Los Angeles, USA

Synthetic Aperture Radar (SAR) interferometry is a formidable technique to monitor surface deformation with a millimeter detection resolution. This study applies the Persistent Scatter-Interferometric Synthetic Aperture Radar (PSInSARTM) technique to measure ground subsidence related to a twin-tunnel excavation in downtown Los Angeles, USA. The PSInSARTM technique is suitable for urban settings because urban areas have strong reflectors. The twin tunnels in downtown Los Angeles were excavated beneath a densely urbanized area with variable overburden depths. In practice, tunneling-induced ground settlement is dominantly vertical. The vertical deformation rate in this study is derived by combining Line of Sight (LOS) deformation velocities obtained from SAR images from both ascending and descending satellite orbits. Local and uneven settlements up to approximately 12 mm/year along the tunnel alignment are observed within the allowable threshold. No severe damages to aboveground structures were reported. Furthermore, ground movements mapped one year before tunnel construction indicate that no concentrated ground settlements pre-existed. A Machine Learning (ML)-based permutation feature importance method is used for a parametric study to identify dominant factors associated with the twin-tunneling induced uneven ground subsidence. Six parameters are selected to conduct the parametric study, including overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, the distance between the two tunnel centerlines, the depth to the tunnel springline, building height, the distance to the tunnel, and groundwater level. Results of the parametric analysis indicate that overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, is the dominant contributing factor, followed by the distance between tunnel centerlines, depth to the tunnel springline, and building height. Two parameters, the distance to the tunnel, and the groundwater level, play lesser essential roles than others. In addition, the geological profile provides comprehension of unevenly distributed ground settlements, which are geologically sensitive and more concentrated in areas with thick artificial fill and alluvium soils, low tunnel depth, and high groundwater levels.

Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

Nowadays, the need for subway tunnels has increased considerably with urbanization and population growth in order to facilitate movements. In urban areas, subway tunnels are excavated in shallow depths under densely populated areas and soft ground. Its associated hazards include poor ground conditions and surface settlement induced by tunneling. Various sophisticated variables influence the settlement of the ground surface caused by tunneling. The shield machine's operational parameters are critical due to the complexity of shield-soil interactions, tunnel geometry, and local geological parameters. Since all elements appear to have some effect on tunneling-induced settlement, none stand out as particularly significant; it might be challenging to identify the most important ones. This paper presents a new model of an artificial neural network (ANN) based on the partial dependency approach (PDA) to optimize the lack of explainability of ANN models and evaluate the sensitivity of the model response to tunneling parameters for the prediction of ground surface and subsurface settlement. For this purpose, 239 and 104 points for monitoring surface and subsurface settlement, respectively, were obtained from line Y, the west bond of Crossrail tunnels in London. The parameters of the ground surface, the trough, and the tunnel boring machine (TBM) were used to categorize the 12 potential input parameters that could impact the maximum settlement induced by tunneling. An ANN model and a standard statistical model of multiple linear regression (MLR) were also used to show the capabilities of the ANN model based on PDA in displaying the parameter's interaction impact. Performance indicators such as the correlation coefficient (R2), root mean square error (RMSE), and t-test were generated to measure the prediction performance of the described models. According to the results, geotechnical engineers in general practice should attend closely to index properties to reduce the geotechnical risks related to tunneling-induced ground settlement. The results revealed that the interaction of two parameters that have different effects on the target parameter could change the overall impact of the entire model. Remarkably, the interaction between tunneling parameters was observed more precisely in the subsurface zone than in the surface zone. The comparison results also indicated that the proposed PDA-ANN model is more reliable than the ANN and MLR models in presenting the parameter interaction impact. It can be further applied to establish multivariate models that consider multiple parameters in a single model, better capturing the correlation among different parameters, leading to more realistic demand and reliable ground settlement assessments. This study will benefit underground excavation projects; the experts could make recommendations on the criteria for settlement control and controlling the tunneling parameters based on predicted results. Doi: 10.28991/CEJ-2022-08-11-05 Full Text: PDF

Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets

Prediction of tunneling-induced ground settlements is an essential task, particularly for tunneling in urban settings. Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures. Machine learning (ML) methods are becoming popular in many fields, including tunneling and underground excavations, as a powerful learning and predicting technique. However, the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods. Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small? In this study, seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation. These methods include multiple linear regression (MLR), decision tree (DT), random forest (RF), gradient boosting (GB), support vector regression (SVR), back-propagation neural network (BPNN), and permutation importance-based BPNN (PI-BPNN) models. All methods except BPNN and PI-BPNN are shallow-structure ML methods. The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability. The model accuracy is measured by the coefficient of determination ( R 2 ) of training and testing datasets, and the stability of a learning algorithm indicates robust predictive performance. Also, the quantile error (QE) criterion is introduced to assess model predictive performance considering underpredictions and overpredictions. Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy (0.9) and stability (3.02 × 10 −27 ). Deep-structure ML models do not perform well for small datasets with relatively low model accuracy (0.59) and stability (5.76). The PI-BPNN architecture is proposed and designed for small datasets, showing better performance than typical BPNN. Six important contributing factors of ground settlements are identified, including tunnel depth, the distance between tunnel face and surface monitoring points (DTM), weighted average soil compressibility modulus (ACM), grouting pressure, penetrating rate and thrust force.

Tunneling-induced settlement prediction using the hybrid feature selection method for feature optimization

Tunneling-induced ground settlement is influenced by a variety of features. In this article, a machine learning model is proposed to predict the ground settlement induced by shield tunneling. A hybrid feature selection method based on the importance degree of variables is first used to select the variables that are most significant to the settlement. Then, from the three perspectives of permutation importance, Sobol’s variance and Shapley additive explanations analysis, the influence of input features to output are quantified and the features are organically combined to construct the subsets for a random forest (RF) model. The monitoring data from a tunnel construction case across the Yellow River is used to evaluate this model. The variable importance measures (VIMs) based RF model with less variables than the original RF model shows a similar performance. Compared to the principal component analysis (PCA) based RF model, VIMs-based RF model shows a better performance while retaining the feature’s physical information, which is critical for future studies that continue to explore the explicit expression of settlement prediction.

Predicting earth pressure balance (EPB) shield tunneling-induced ground settlement in compound strata using random forest

The prediction and control of the ground settlement induced by shield tunneling in compound strata is a crucial challenge for tunnel excavation. In this regard, a finite element model for the analyses of ground surface settlement induced by earth pressure balance (EPB) tunneling was established and validated by the field test. The influence of single influence factors, such as rock proportion in the face, support pressure, and tunneling angle, on the surface settlement above the tunnel face was systematically studied. To investigate the joint influence of different factors on ground deformation mechanism, the multi-factor analysis based on field data and the multiple regression (MR) method was conducted. However, the prediction accuracy of the MR method was limited. Therefore, a prediction approach of shield tunneling-induced settlement in compound strata based on a random forest (RF) was developed. Tunnel geometry, geological features, and operation parameters were investigated as input variables in the RF model to achieve this goal. The applicability effect of the RF-based method was proved by utilizing two data sets (i.e., the settlement ahead of tunnel face and final settlement). The results indicated that the prediction accuracy of shield tunneling-induced settlement in compound strata by the RF-based model is higher than the MR method. The relative importance analysis of variables indicates that advance rate, the cover depth of the tunnel ring, and pressure in the chamber were the most influential features for the settlement ahead of the tunnel face, while the thrust of the cutter head and the grout quantity were the most significant variables for the settlement after the segment assembling is completed. Overall, the conducted study could provide a helpful reference for the efficient excavation of EPB tunnels across compound strata.

Intelligent Prediction of Multi-Factor-Oriented Ground Settlement During TBM Tunneling in Soft Soil

Tunneling-induced ground surface settlement is associated with many complex influencing factors. Beyond factors related to tunnel geometry and surrounding geological conditions, operational factors related to the shield machine are highly significant because of the complexity of shield-soil interactions. Distinguishing the most relevant factors can be very difficult, for all factors seem to affect tunneling-induced settlement to some degree, with none clearly the most influential. In this research, a machine learning method is adopted to intelligently select features related to tunneling-induced ground settlement based on measured data and form a robust non-parametric model with which to make a prediction. The recorded data from a real construction site were compiled and 12 features related to the operational factors were summarized. Using the intelligent method, two other features in addition to cover depth–pitching angle and rolling angle–were distinguished from among the 12 feature candidates as those most influencing the settlement trough. Another new finding is that advance rate does not emerge in the top 10 selected models from the observational data used. The generated non-parametric model was validated by comparing the measured data from the testing dataset and performance on a new dataset. Sensitivity analysis was conducted to evaluate the contribution of each factor. According to the results, engineers in general practice should attend closely to pitching angle during tunnel excavation in soft soil conditions.

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the (R2) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.

Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement

Tunnel excavations can cause ground surface settlement which in turn affects the structural integrity of adjacent pipelines. This paper explores the probabilistic risk assessment of gray iron pipes subjected to tunneling. In the beam-on-spring analysis, a modified Gaussian distribution profile was incorporated as displacement-controlled boundary conditions on spring elements. The modeling strategy was evaluated by comparing calculated pipe strains with field and centrifuge experimental measurements, as well as empirical solutions. The calibrated numerical model was then used to perform parametric fragility analyses using a machine learning technique called Lasso Regression to demonstrate the relative importance of various parameters. It has been found that existing pipelines with a smaller pipe diameter and a larger pipe wall thickness buried at a shallower depth in loosely compacted soils with a smaller soil friction angle are less prone to failure due to tunneling induced ground settlement.

Numerical Investigation of Pipeline Responses to Tunneling-Induced Ground Settlements in Clay

Tunnel excavation causes stress changes in the ground, leading to soil movements that may affect the safety and serviceability of existing pipelines. Although the tunnel-soil-pipe interaction has attracted increasing research attention, previous studies primarily focused on the responses of existing pipelines due to tunneling in sand. In this study, an extensive numerical parametric study with 540 numerical runs is conducted to investigate the effects on existing pipelines due to tunneling in clay. Soil responses to uplift and downward pipe-soil relative movements are explicitly simulated. Parameters considered in the tunnel-soil-pipe interaction include ground settlement profile, pipe dimension, material properties, pipe burial depth, and undrained shear strength. A dimensionless plot of relative pipe-soil stiffness and the ratio of maximum pipe curvature to ground curvature are developed to directly estimate pipeline curvature due to tunnel excavation in clays. When the relative pipe-soil stiffness is less than 1×10-4, the existing pipeline deforms with tunneling-induced ground settlement. The existing pipeline is relatively rigid when the pipe-soil stiffness is larger than 10.

Semianalytical Solution and Parameters Sensitivity Analysis of Shallow Shield Tunneling-Induced Ground Settlement

The influence of boundary soil properties on tunneling-induced ground settlement is generally not considered in current analytic solutions, and the hypothesis of equal initial stress in vertical and horizontal makes the application of the above solutions limited. Based on the homogeneous half-plane hypothesis, by defining the boundary condition according to the ground loss pattern in shallow tunnel, and with the use of Mohr-Coulomb plastic yielding criteria and classic Lame and Kiersch elastic equations by separating the nonuniform stress field to uniform and single-direction stress field, a semiempirical solution for ground settlement induced by single shallow circular tunnel is presented and sensitivity to the ground parameters is analyzed. The methods of settlement control are offered by influence factors analysis of semiempirical solution. A case study in Beijing Metro tunnel shows that the semiempirical solution agrees well with the in situ measured results.

Modeling of shield-ground interaction using an adaptive relevance vector machine

Shield tunneling method is widely adopted in tunneling projects. Analysis of ground settlement is required as an effective way for minimizing the potential damage caused by tunneling. Many efforts have been devoted for this purpose using various methods such as empirical approaches and numerical modeling. However, there are multiple factors that may influence the ground settlement, and the shield-ground relationship is highly non-linear and complex. To understand the complex soil behavior in response to shield penetration, a model that can establish the relationship and make accurate predictions for tunneling-induced ground settlement is needed. This paper proposed a model based on relevance vector machines (RVMs) to develop the predictive relations. Adaptive feature scaling factors were introduced as an inherent mechanism that enables RVMs to identify the relative importance of each input factor, and an optimization method for obtaining the appropriate values of feature scaling factors is proposed. The potential of the proposed adaptive model was investigated by applying it to tunnels that bored by an earth pressure balance (EPB) shield machine. Three categories of factors, namely tunnel geometry, geological conditions and shield operational parameters were considered in the model. The results demonstrate that the proposed model has competitive predictive capacities and that the adoption of adaptive feature scaling factors can enhance the prediction accuracy and provide a measure of the relative importance of each input factor. Moreover, the implementation of the adaptive RVM model is relatively simple. There is no need to set model parameters because they can be automatically optimized during model training, which makes the method a practical tool for geotechnical engineers to evaluate ground reactions during tunnel excavation.

Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays

Excessive settlement caused by tunneling during construction often damages adjacent infrastructures and utilities. Such excessive settlement can also present a challenge in the maintenance of subways during their operation. Thus, it is important to be able to accurately predict tunneling-induced settlement. The uncertainties in geotechnical parameters, however, can lead to either an overestimation or an underestimation of the tunneling-induced settlement. Such uncertainties can arise from many sources such as spatial variability, measurement error, and model error; in this paper, the focus is on the geotechnical parameters characterization from site exploration. The goal here is to determine an optimal level of site exploration effort so that effective predictions of the tunneling-induced settlement in clays can be achieved. To this end, a Monte Carlo simulation-based numerical model of site exploration is first established to generate artificial test data. Then, a series of parametric analyses are performed to investigate the relationship between the level of site exploration effort and the accuracy of the tunneling-induced ground settlement prediction. Through the assumed different levels of site exploration effort, statistics of soil parameters are estimated using the maximum likelihood method and the tunneling-induced ground settlement is then analyzed using the probabilistic method, and finally the effect of site exploration effort is assessed. The knowledge generated from this series of analyses is then used to develop the proposed framework for selecting an optimal site exploration program for improved prediction of the tunneling-induced ground settlement in clays. Examples are presented to illustrate the proposed framework and demonstrate its effectiveness and significance.

Effects of building three-dimensional modeling type on twin tunneling-induced ground settlement

Buildings are usually modeled as elastic beams in plane strain finite element (FE) simulation of tunnel-building interaction. However, neither tunnel nor surface buildings behave in a plane strain manner. In order to assess the effect of building modeling type on twin tunneling-induced ground settlement, some full three-dimensional FE models were analyzed and the effect of building modeling type was reviewed. Two dimensional (2D) plane strain interaction and green-field situation models were also analyzed to compare the results with the three dimensional (3D) models and assess the influence of modeling type. Outcomes of the studies showed that three-dimensional modeling has great influence on the results and equivalent surface beam, which is used in common practice of two-dimensional plane strain models, over-estimates the stiffness of building and leads to less settlement prediction in comparison with 3D simulation.

Wavelet Analysis for tunneling-induced ground settlement based on a stochastic model

Current practice in predicting tunneling-induced ground settlement has some limitations in describing the time-dependent settlement process due to the existence of measurement error. In this study, settlement data was considered as time series by establishing a stochastic model, while measurement error was regarded as a stationary and normally distributed stochastic process. Furthermore, Wavelet Analysis was introduced to filter the measurement error and extract the actual settlement value, which is similar to denoising in signal processing. In addition, methods such as the unit root test, normality test and ANOVA, were used to testify whether the characteristics of the filtered part of settlement data were consistent with those of measurement error. As a result, an optimal selection of wavelet basis and decomposition level could be made when using Discrete Wavelet Transform. Finally, extensive instrumentation data obtained from a real tunnel project supported our model hypothesis and proved the feasibility of this approach, and decomposing at level 4 with wavelet D16 was proved to achieve the best performance.