Tunnel Settlement Research Articles

Research on prediction of surrounding rock deformation and optimization of construction parameters of high ground stress tunnel based on WOA-LSTM

To accurately understand the deformation behavior of surrounding rock in the jiugongshan No.2 high ground stress tunnel and optimize construction parameters for improved efficiency, on-site monitoring was used to gather data on rock deformation and initial support contact pressure. A WOA-LSTM regression prediction model was proposed, and excavation advance depth was optimized using numerical simulation. The WOA-LSTM regression prediction model demonstrated high accuracy in tunnel deformation monitoring. The absolute errors between predicted and actual values of tunnel settlement and convergence at various measurement points average 2.53− 04 mm and 1.96− 04 mm, with relative errors averaging 2.15% and 2.34%. These results meet the requirements for guiding construction. Additionally, based on the results of numerical simulation calculations, when the step length is 12 m during the construction of high-stress tunnels using the benching method, excavation advances of 2 m and 3 m result in settlement and convergence increases of 35.1% and 63.4%, and 25.5% and 55.2%, respectively, compared to an excavation advance of 1 m. However, no sudden jumps in these values were observed, indicating that with a 3 m excavation advance, the integrity of the surrounding rock remains in good condition, effectively guiding safe and rapid construction methods.

Longitudinal structural resilience of shield tunnel: Characterization and field application

The longitudinal structural resilience performance of shield tunnels is an important concern given the dramatic development of underground systems and the increasing demand for maintenance work. In this paper, a new model using longitudinal relative differential settlement as the index of tunnel structural resilience performance is proposed. The resilience metric (Re) is defined as the ratio of the area integrated by the residual performance during environmental disruptions to the area integrated by the normal performance for the corresponding duration. Then, the proposed resilience analysis model is applied to a well-documented case in Shanghai, where the existing metro tunnel is disrupted by a newly constructed large-diameter shield tunnel undercrossing and subsequently repaired by soil grouting. The variations of tunnel settlement concerning construction parameters and driving distance of the shield machine are analyzed. The performance degradation characteristics of the tunnel during disruption and recovery are effectively captured. The results show that 32.3 % of the performance loss is attributed to the new tunnel undercrossing in the first 38 days. After the completion of the grouting reinforcement, the tunnel performance is improved from 0.677 to 0.868, accounting for approximately 59.1 % of the performance loss during the crossing period. Moreover, the resilience metric (Re) is calculated as 0.764, indicating a high level of resilience for the existing metro tunnel in this case. In addition, other performance indexes based on tunnel longitudinal settlement are discussed, demonstrating the good rationality and applicability of the proposed index.

Thermo-hydro-mechanical behaviour of energy tunnel in unsaturated soils

Soils surrounding energy tunnels are often unsaturated, particularly in areas with a low water table. All studies of the mechanical behaviour of energy tunnels focused on saturated or dry soils. Through numerical simulations using a coupled thermo-hydro-mechanical model for unsaturated soils, this study aims to improve the understanding of the interactions between energy tunnels and unsaturated ground. The influences of water table position and soil type on the mechanical behaviour of energy tunnels were investigated. Results show that the thermally-induced lining convergence of energy tunnels does not change much with the water table position. Nevertheless, the other mechanical responses (e.g., settlements of ground and tunnel and lining stress changes) highly depend on the water table position. These mechanical responses are more significant when the water table is higher than the tunnel center, except for the larger settlements at a lower water table in clay. Furthermore, the above responses are underestimated if soils above the water table are assumed to be completely dry. The degree of underestimation is most significant for the clay, followed by silt and sand, e.g., a maximum of 54%, 23% and 10% in the change of circumferential stress, respectively. These results highlight that the unsaturated soil model should be used to evaluate the thermo-hydro-mechanical responses of energy tunnels in unsaturated soils, particularly for the clay ground.

Strain characteristics of reinforced soft clay around tunnel under metro loads

This study investigates the Strain characteristics of reinforced soft clay surrounding a tunnel subjected to cyclic loading, employing the Global Digital Systems cyclic triaxial test on the saturated reinforced clay near Shanghai Metro Line 4’s Hailun Road station. Initially, the study examines the impacts of frequency, dynamic stress amplitude (DSA), and loading cycles on cumulative plastic strain. Following this, the orthogonal design method was employed to organize tests, and an evaluation method for assessing soil strain was developed through mathematical statistical analysis. The results indicate at the constant vibration frequency, cumulative plastic strain increases with an increase in DSA and decreases with as load frequency increases. DSA is the primary factor influencing the axial deformation of subway tunnels, whereas the interaction between load frequency and vibration frequency is negligible. The findings suggest that measures to prevent and control subway tunnel settlement should concentrate on DSA during the early stages of subway operation.

A Study on Deformation Characteristics and Stability of Soft Surrounding Rock for a Shallow-Buried Tunnel

The Heimaguan Tunnel in China serves as a case study to exemplify the variation laws related to surface settlement, deformation, and stress characteristics in a shallow-buried soft-rock tunnel, while emphasizing in the tunnel support requirements. The first stage of this study begins with monitoring the time-varying characteristics of surface settlement, vault subsidence, and the horizontal convergence of Grade V rock. In the second stage, Peck theory is used to calculate the distribution characteristics of surface settlement. The results of both stages are compared to create a vault settlement model, thus establishing the horizontal convergence based on exponential function, logarithmic function and hyperbolic function, and determining the optimal time of secondary lining construction. On this basis, the time-dependent variation laws and characteristics of vertical and horizontal displacement and principal stress of surrounding rock are studied. After this, using simulation and analysis, the proper support is recommended. The study reveals that the surface settlement, vault subsidence, and horizontal convergence of the shallow-buried soft-rock tunnel stabilize within 25–30 days. Peck theory closely aligns with predictions based on exponential functions, with only a 0.72% difference. The recommended time for secondary lining application is 26–27 days.

Mechanical behavior of immersed tunnel under the influence of differential settlements: A case study of GZ tunnel

As a typical marine and underwater infrastructure, the joints stress state of immersed tunnels is complicated due to the differential settlement during operation. Based on the current attitude of the segmental elements, this study takes GZ Tunnel as an example to preliminarily discuss the influence of differential settlement on the mechanical behavior of immersed tunnels through field monitoring, on-site detection and numerical simulation. The result shows that: The differential settlements of immersed tunnels can be divided into longitudinal and transverse, specifically manifested as bending or torsion of elements. The maximum longitudinal settlement and maximum transverse settlement of immersed elements is 108.7 mm and 27.9 mm, respectively. Under the influence of differential settlement in different directions, and the shear keys of the central wall and the sidewall with larger settlements are subjected to greater forces. Numerical simulation shows that the maximum principal stress and minimum principal stress of GZ Tunnel can reach 20.87 MPa and 22.21 MPa, respectively, which is located at the joint between E2 element and the northern buried element. The failure mode of the shear keys is cracking near the inner corners and local concrete crushing. The failure reason can be summarized as the compressive or tensile shear caused by the misalignment and torsion of the elements. The reason of differential settlement of GZ tunnel is caused by construction and long-term operation. In studying underwater facilities such as immersed tunnels under the influence of differential settlement, it is suggested to consider the three-dimensional attitude change of the structure comprehensively.

Improved Machine Learning Model for Urban Tunnel Settlement Prediction Using Sparse Data

Improved Machine Learning Model for Urban Tunnel Settlement Prediction Using Sparse Data

Bidirectional and nested model for numerical simulation and machine learning: a case study of long-term settlement prediction of tunnel

Numerical simulation and machine learning are commonly adopted research methods in engineering. This paper proposes a bidirectional and nested model for numerical simulation and machine learning (BNNM). This model permits numerical simulation methods and machine learning methods to participate in each other’s calculation process. It helps overcome the obstruction of unclear mechanisms and inaccurate parameters in numerical simulation methods, and avoid overfitting problem caused by too many features in machine learning methods. Moreover, BNNM frees machine learning methods from the dependence on a specific set of labels. The BNNM helps train machine learning models using obtainable labels, and output results that cannot be easily obtained using field, experiment, and numerical simulations. To illustrate its construction method and performance, a representative BNNM model is constructed using BPNN, in addition to a simple numerical simulation model. This model predicts the long-term settlement of shield tunnel. The results show that the representative model effectively reduces the modelling difficulty associated with numerical simulation and improves prediction accuracy of BPNN model. The model also derives long-term constitutive models of various soils with only the tunnel settlement data set. Although a simplified constitutive model was used, the main advantages of the BNNM model have been highlighted.

Dimension-Reduction Spectral Representation of Soil Spatial Variability and Its Application in the Efficient Reliability Analysis of Seismic Response in Tunnels

Reliability analysis of tunnels considering soil spatial variability was traditionally carried out using Monte Carlo simulations, which are computationally expensive. This study proposes a dimension-reduction spectral representation method (SRM) for simulating parameter stochastic fields to capture uncertainties in soil properties, which can be coupled with the probability density evolution method (PDEM). Additionally, the PDEM is employed to conduct seismic reliability assessments of tunnels in a more efficient manner. By integrating a non-intrusive dynamic random finite element method (FEM), this study explores the horizontal drift angle and vertical relative settlement of tunnel dynamic response under different coefficient of variations (COVs) and horizontal autocorrelation lengths of elastic modulus (E). Results show the changes in COVs have more pronounced effects on the mean and standard deviation of horizontal drift angle and vertical relative settlement of tunnel than alterations in horizontal autocorrelation lengths. A limit state pertaining to the horizontal drift angle and vertical relative settlement is established through the use of a probability density function (PDF) and cumulative distribution function (CDF). The results suggest that the combination of soil spatial variability and PDEM can serve as an effective approach for practical tunnel design.

Study on dynamic response and long-term settlement of silty soil around Shanghai metro tunnel

At present, the method for calculating long-term tunnel settlement predictions under metro loading considers only one working condition of passenger loading, which is inconsistent with actual working conditions. To establish a tunnel settlement model that accounts for variations in passenger flow, this study uses data mining methods to categorize metro operation into three working conditions: "peak period, secondary period, and low period." The impact of these passenger flow conditions on the dynamic response of the soil around the tunnel is analyzed. Then, based on the principles of calculus, a calculus-based prediction model is established to consider the changing patterns of metro passenger flow. The model is applied to analyze the long-term settlement characteristics of Shanghai Metro Line 10. The results indicate that, under identical conditions, soil displacement and dynamic deviatoric stress around the tunnel increase with passenger capacity. The calculus prediction model aligns more closely with actual working conditions than the conventional model. The predicted tunnel settlement of Shanghai Metro Line 10 after 20 years of operation is approximately 37.07 mm, with most settlement occurring in the early stages, primarily due to cumulative plastic deformation of the soil.

Probabilistic assessment of existing shield tunnel longitudinal responses to tunnelling

AbstractThis paper proposes a probabilistic‐based framework to assess the failure probability of the existing shield tunnel owing to undercrossing tunnelling. A novel deterministic model using the two‐phase analysis method is presented to evaluate the longitudinal behaviours of the in‐service shield tunnel. First, the tunnelling‐induced settlement is estimated using the Loganathan and Poulos’ method; second, the longitudinal beam‐spring model (LBSM), which can explicitly consider the circumferential joints, is applied to reflect the performances of the existing shield tunnel. Two well‐documented case histories are selected to validate the effectiveness of the deterministic analysis model. The predictions are also compared with previous analytical methods. Afterward, to improve the computational efficiency, a surrogate model is then established to replace deterministic model and perform the global sensitivity analysis (GSA). A probabilistic analysis is further performed to explore the tunnelling‐induced deformation characteristics of the shield tunnel by means of Monte Carlo simulation (MCS) method. Parametric analyses are further performed to explore the influences of key variables on the failure probabilities of the existing shield tunnel. The results show the proposed deterministic model gives a satisfactory prediction in tunnelling‐induced responses of the existing shield tunnel. The LBSM can reflect the segmental ring and joint deformations more realistically. The uncertainties in the ground elastic modulus, joint rotation stiffness and the joint shearing stiffness dominate the tunnel settlement, joint opening and dislocation, respectively. The failure probability of tunnel settlement is more sensitive to volume loss than that of the joint opening and dislocation. The whole system failure probability is identical to that of the tunnel settlement failure mode. By increasing the coefficient of variation (COV) of random variables, probability density function (PDF) curves for neutral and pessimistic conditions become lower and wider, and the maximum joint opening and dislocation positions corresponding to the peaks of PDF apparently deviate from that of the determinate results. Increasing the two tunnel clearances, the allowable volume loss for opening and dislocation increase linearly, but it has insignificant influence on the tunnel failure mode. However, enlarge the new tunnel diameter leads to rapid decrease in the allowable volume loss for all failure modes.

Multi-influencing Factor Weighted WPSO–SVM Prediction of Subway Tunnel Settlement under GRA Supports

Multi-influencing Factor Weighted WPSO–SVM Prediction of Subway Tunnel Settlement under GRA Supports

Intelligent Early Warning and Decision Platform for Long-Term Ground Subsidence in High-Density Areas for Sustainable Urban Development

Long-term ground subsidence (LTGS) is a relatively slow process. However, the accumulation of long-term subsidence has an adverse impact on the normal operation and safety of a subway, hindering sustainable urban development. A wide gap exists between early warning theory and its application in the control of LTGS during subway operation due to time span limitation. Providing decision support for LTGS in high-density urban areas during subway operation is difficult, and a collaborative decision system for real-time early warning and intelligent control is currently lacking. This study establishes the functional components of an intelligent early warning and decision platform, proposes a software system module, constructs an overall software framework structure, and develops a mobile intelligent early warning and decision platform. Moreover, this study introduces an early warning method for LTGS in high-density urban areas during subway operation. This method integrates an intelligent early warning decision-making platform, namely Differential Synthetic Aperture Radar Interferometry (DInSAR), land subsidence monitoring, operation tunnel subsidence monitoring, and other multisource data coupling. The method is applied to sections of the Hangzhou Metro Line 4 Phase I Project (Chengxing Road Station (CRS)–Civic Center Station (CCS)–Jiangjin Road Station (JRS) and Xinfeng Station (XS)–East Railway Station (ERS)–Pengbu Station (PS)). This work can serve as a reference for ensuring urban safety and promoting sustainable development.

A method for monitoring the uneven settlement of shield tunnels considering the flattening effect using distributed strain data measured from BOTDA sensors

When investigating the uneven settlement monitoring of shield tunnels, the influence of the flattening effect under longitudinal bending is rarely considered, which leads to inaccurate and incomplete settlement monitoring. To address this issue, a method for monitoring the uneven settlement of shield tunnels that considers the flattening effect is proposed, which is achieved using high-density strain data from distributed Brillouin optical time domain analysis (BOTDA) sensors. The high-density measured strain data is used to combine global settlement with the additional influence of the flattening effect, which includes extrusion and shear effects. This approach allows the trend of uneven settlement to be effectively monitored. Furthermore, a finite element model updating process is implemented in advance to improve the accuracy of soil parameters and minimize calculation errors in practical engineering. To demonstrate the effectiveness of the proposed method, an application example from the Jinan Rail Transit Line 3 was utilized for numerical simulations and practical tests. The results of the application example reveal that the proposed method has a high accuracy, with measurements comparable to the total station-based method. This indicates that the proposed method is an effective and robust settlement monitoring approach and provides a referential basis for further tunnel operations and maintenance.

Deep Learning Network for Remote Monitoring of Thermal Exchange Tunnels

Monitoring and predicting ground settlement throughout tunnel construction is critical to ensuring the safe and accurate use of urban tunnel systems. The accurate and efficient diagnosis of such settlement can decrease hazards while improving the safety and dependability of these initiatives. However, typical tunnel inspection procedures are time-consuming, costly, and heavily reliant on human subjectivity. The trained model's accuracy was evaluated by comparing its findings across extended operating durations using the same and different thermal operational patterns as those utilized for training. Deep learning, one of the most powerful Artificial Intelligence approaches, is required for the tunnel's settlement predicting challenge. Nevertheless, deep neural networks frequently want huge quantities of training data. In the method we used, CNN-LSTM models were trained on datasets of various sizes and attributes. The results suggest that both of the proposed models may achieve a little inaccuracy under specific situations.

Analysis and Warning Prediction of Tunnel Deformation Based on Multifractal Theory

To better analyze the fluctuation characteristics and development law of tunnel deformation data, multifractal theory is applied to tunnel deformation analysis. That is, the multifractal detrended fluctuation analysis (MF-DFA) model is first utilized to carry out the multifractal characterization of tunnel deformation data. Further, Mann–Kendall (M–K) analysis is utilized to construct the dual criterion (∆α indicator criterion and ∆f(α) indicator criterion) for the tunnel deformation early warning study. In addition, the particle swarm optimization long-short-term memory (PSO-LSTM) prediction model is used for predicting tunnel settlement. The results show that, in reference to the tunnel warning level criteria and based on the Z-value results of the indicator criterion, the warning level of all four sections is class II. At the same time, through the analysis of tunnel settlement predictions, the PSO-LSTM model has a better prediction effect and stability for tunnel settlement. The predicted results show a slow increase in tunnel settlement over the next 5 days. Finally, the tunnel warning level and the predicted results of tunnel settlement are analyzed in a comprehensive manner. The deformation will increase slowly in the future. Therefore, monitoring and measurement should be strengthened, and disaster preparedness plans should be prepared.

Subway station construction parallelly below existing double-cell tunnel without clearance

ABSTRACT A case of the Olympic Park Station of Beijing Subway Line 15 constructed parallelly below the Datun Road Tunnel without clearance using the pile-beam-arch (PBA) method was presented. Cross section, geological condition, and construction process of the subway station are illustrated. The strip foundation PBA approach was first adopted and the settlements of the existing tunnel due to new subway station construction are systematically monitored. Whereas this method could not satisfy the settlement requirements of the existing Datun Road Tunnel. The maximum settlement of the existing tunnel reaches 32.0 mm. Meanwhile, the maximum transverse differential settlement of the existing tunnel reaches 10.1 mm. The improved pile foundation PBA approach was proposed. The numerical simulation was performed to verify the feasibility. The results indicate that the pile foundation PBA approach is more efficient in existing tunnel settlement control than the strip foundation PBA approach. About 80% of the total settlement of the existing tunnel caused by subway station construction using the pile foundation PBA method is produced during the excavation of the drifts and the arches. The settlements of the original tunnel induced by the construction of the concourse and the platform account for about 20% of the total settlement. The increments are negligible after the completion of the concourse construction.

Time-series prediction of settlement deformation in shallow buried tunnels based on EMD-SSA-GRNN model

Tunnel settlement deformation monitoring is a complex task and can result in nonlinear dynamic changes. To overcome the disturbances caused by historical data and the difficulty in selecting input parameters during deformation prediction, a decomposition, reconstruction and optimization method for tunnel settlement deformation prediction is proposed. First, empirical mode decomposition (EMD) is used to decompose the in-situ monitoring data and reduce the interactions among information at different scales in sequences. Then, the monitoring data are decomposed into intrinsic mode functions (IMFs). Secondly, the smoothing factor of the generalized regression neural network (GRNN) is optimized by using the sparse search algorithm (SSA). An EMD-SSA-GRNN deformation prediction model is developed using the optimized GRNN algorithm and is used to predict the changes in the decomposed IMFs. Finally, using the measured deformation data from a shallowly buried tunnel along the Kaizhou-Yunyang Highway in Chongqing, China, the reliability and accuracy of different models are analysed. The results show that tunnel settlement deformation exhibited a trend and a slow change in the early stage, a rapid change in the middle stage and a slow change in the late stage, and the rate of change was significantly influenced by the excavation time and the upper and lower geological layers. The prediction accuracy of the EMD-SSA-GRNN model after EMD improved from 19.2 to 59.4% relative to that of the SSA-GRNN and single GRNN models. Moreover, we find that the three error evaluation indicators of the EMD-SSA-GRNN model are lower than those of the other models and that the results of the proposed model and are more strongly correlated with measured data.

Modeling of the accurate application of the Kernel Efficiency Method in the proportioning process of shield tunnel construction

Abstract The existing control strategies for subway construction struggle to address the challenges posed by the impact of large-diameter shield-driven underpass subway tunnels on soil settlement. In this paper, based on the mechanism of ground settlement of the shield tunnel under the KNX method, the influence of the underpass tunnel on the existing tunnel and its surrounding soil stress-strain characteristics is calculated by the Peck superposition method. The KNX method is optimized by optimizing the ratio of KNX slurry and the grouting pressure to control the existing tunnel and the ground settlement. The geotechnical general finite element analysis software is being introduced to conduct numerical simulation and engineering application experiments to verify its effectiveness. The results show that when the bonding force of the grouting material is 350Pa-s, the maximum value of the variation is 3.91mm, and the mobility is more suitable, which can effectively fill the void of the shield machine and reduce the settlement of the foundation. When the grouting pressure is 280kN/m2, the maximum contraction of the tunnel is 6.25mm, which can ensure the safety of the existing tunnel on the basis of the tunnel so that the actual settlement of the soil in the tunnel is minimized. Comprehensively, the optimization of the ratio of the KNP method in the control of the deformation characteristics of the soil and structures under the shield tunnel has effectiveness and can effectively provide theoretical and technical guidance for the construction of subway tunnels under the large-diameter shield under the subway.

Assessment of the Influence of Tunnel Settlement on Operational Performance of Subway Vehicles

Assessment of the Influence of Tunnel Settlement on Operational Performance of Subway Vehicles