Tunnel Boring Machine Research Articles

Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

In tunnel construction, efficiently predicting the energy usage of tunnel boring machines (TBMs) is critical for optimizing operations and reducing costs. This research proposes a novel method for predicting the specific energy of micro slurry tunnel boring machines (MSTBMs) using an explainable neural network (xNN) that leverages operator-monitored data. The xNN model provides transparency and interpretability by integrating the Shapley additive explanation (SHAP) technique, enabling tunneling engineers and operators to gain valuable insights into the prediction process. Extensive data from MSTBM umbrella pipe support excavation are the foundation for training, testing, and unseen data in the xNN model. The specific energy formula derived from the operational parameters of the MSTBM defines the dependent variable for the xNN model. The test dataset evaluates the model’s performance with an R² of 98.7%, an MSE of 2.40, and an MAE of 0.003, demonstrating its accuracy and reliability. Ten percent of the dataset was reserved as unseen data to assess the model’s generalization capabilities. Upon evaluation, the model achieved an R2 value of 89%, an MAE of 0.01, and a root mean squared error (RMSE) of 0.01. The xNN empowers operators to optimize operational parameters and promote more efficient and sustainable tunneling practices by identifying influential factors affecting energy consumption through its interpretable nature. This research has significant implications for the future of underground construction, paving the way for improved resource management.

Assessment for shallow and large tunnel construction in weak ground conditions: Application of tunnel boring machines

AbstractWith recent technological advancements, tunnel boring machines (TBM) have developed and exhibited high performance in large diameters and weak ground conditions. Tunnels are crucial structures that significantly influence the timelines of highway and railway projects. Therefore, the construction of tunnels with TBMs becomes a preferred option. In this study, a comparative analysis between TBM and the New Austrian Tunneling Method (NATM) for tunnel construction is performed in the construction of the T1 tunnel with a diameter of 13 m, which is the longest tunnel in the Eşme‐Salihli section of Ankara‐İzmir High‐Speed Railway Project (Türkiye). The selection of TBM type, measures taken in problematic sections, and application issues of TBM are discussed. The impact of correct description of geological and geotechnical conditions on both selection and performance of TBM is presented. An earth pressure balanced type TBM is chosen for the construction of the T1 tunnel. Because of the additional engineering measures taken before excavation in problematic areas, the tunnel was completed with great success within the initially planned timeframe. From this point of view, this study is an important case and may contribute to worldwide tunneling literature.

Effect of advance rate on excess pore water pressure induced by TBM tunnelling in saturated sand

The stability of tunnel face can be significantly influenced by the excess pore water pressure induced by tunnel boring machine (TBM) tunnelling in saturated sand. This article aims to investigate the effect of TBM advance rate on the variation of such excess pore water pressure. Numerical and analytical solutions are given for the situations of TBM advance rate faster and slower than the slurry infiltration velocity, respectively. It appears that the excess pore water pressure at the tunnel face maintains constant with time when the TBM advance rate exceeds the slurry infiltration velocity and the gradient close to the TBM decreases with distance. In the opposite situation, there is a temporal decrease in excess pore water pressure at the tunnel face, accompanied by a more gradual spatial drop in pressure. In addition, the gradient close to the TBM remains constant. Notably, a larger extent of excess pore water pressures is present in the latter situation, augmenting the risk of a blow-out during tunnelling. Comparative analysis of field measurements and theoretical calculations indicates that the conditions at Green Heart Tunnel align with the latter situation. Furthermore, for the latter situation, a slower TBM advance rate will result in a higher discharge and thus a higher pore water pressure.

A Test System for Microwave-Assisted Dual-Mode Mechanical Cutting of Hard Rock Under True Triaxial Compression

This paper introduces a test system for microwave-assisted dual-mode mechanical cutting of hard rock under true triaxial compression. The test system was developed by Northeastern University, China, to simulate the cutting and breaking of hard rock by a tunnel boring machine (TBM). The test system is mainly composed of a dual-mode mechanical cutting subsystem, a true triaxial loading subsystem and a high-power microwave presplitting subsystem. The system performs dual-mode cutting under true triaxial stress, applies a single sensor to monitor the three-dimensional force of the cutter head, and integrates the reduction in the vibration and resistance to torsion during the cutting process. Mechanical cutting tests of basalt under different amounts of microwave irradiation and true triaxial stress conditions were conducted in Chifeng, Inner Mongolia, China. The results showed that microwave radiation presplitting effectively improved the efficiency of mechanical rock breaking. However, after 180 s of microwave irradiation, true triaxial high stress inhibited the mechanical breaking of basalt in terms of the overall excavation depth. During the tests, each system performed stably. The test results verified the reliability of the test system.

Modeling and economic model predictive control of constrained cutterhead system with disturbance in tunnel boring machines

Tunnel boring machines (TBMs) are usually the first choice for tunneling construction with its advantages on high safety, time saving, and less operators. Cutterhead system is an important component for TBMs since it is used to excavate rocks and soil. It is difficult to guarantee both the boring efficiency and energy saving under the excavating rock disturbances and the constraints on the driving motors in TBMs by manual operation. To deal with this problem, it is necessary to develop advanced control algorithms for the cutterhead system. Thus, we investigate an economic model predictive control (EMPC) structure for cutterhead system in TBMs. A generalized nonlinear dynamic model of TBM cutterhead system is built based on the first principle method. An economic cost is constructed with the boring efficiency and energy cost to evaluate the tunnel construction quality. EMPC algorithms are designed to optimize the constructed economic cost for a cutterhead system to guarantee good economic performance. It is shown that EMPC can improve the economic performance of the cutterhead system.

Comparison of the specific energies of sinusoidal VCS cutter rings and CCS cutter rings in breaking rock-like materials based on the FEM.

Disc cutters are essential for full-section hard-rock tunnel boring machines. The performance of these devices directly affects tunnel engineering costs and duration. This paper proposes a sinusoidal variable cross-section (VCS) cutter ring and design method and establishes a digital model. Rock-like materials are simulated with a finite element model, and the model validity is verified via rock simulation mechanics tests. A disc cutter rolling rock simulation model for a linear cutting machine is also established, and simulation tests are performed for single- and three-cutter rolling using sinusoidal VCSs and constant cross-section (CCS) cutter models, respectively. The stress and energy changes for the cutters and rock-like material damage area were compared via simulation, confirming that some sinusoidal VCS cutter rings do less work on rock-like materials and cause larger crushing areas under the same engineering parameters; therefore, these cutter rings have smaller specific energies. The sinusoidal VCS cutter ring performance is 7% greater than that of CCS on average under single-cutter simulation, and the intermediate cutter performance of the intermediate cutter is 9% greater than that of CCS on average under three-cutter simulation. Thus, sinusoidal VCS cutter rings offer improved rock damage performance, and further research and application of this technology will improve the working efficiency of tunnel boring machines.

Enhancing ground classification models for TBM tunneling: Detecting label errors in datasets

Tunnel Boring Machine (TBM) construction, particularly with closed-face TBMs, faces uncertainties due to the inability of the operator to directly observe the ground ahead. These uncertainties can lead to time delays, cost overruns, and accidents. While supervised machine learning techniques have been used to predict geology from TBM sensor data, their performance drops significantly when applied to other projects, indicating poor generalization. To ensure accurate results and improved generalization to future data, supervised learning models require high-quality, well-labeled data which is not usually the case for TBM datasets. This paper addresses the issue of “noisy” labels in TBM datasets, which human operators and engineers often label with varying interpretations. A data-centric framework was adapted and applied to an Earth Pressure Balance Machines (EPBM) tunnel dataset to detect and identify these mislabeled datapoints. The framework's outputs were validated using two techniques and apply several methods to clean the dataset. The best-performing method was selected for the test set. The paper concludes by discussing the limitations of the proposed method, the challenges encountered, and future research directions in this area.

Innovative numerical modeling for predicting soil relaxation in the design of twin circular culverts

This study presents a finite difference model for analyzing ground stability and settlement of twin circular culverts in undrained clay. The model is verified through simulations of soil movement and relaxation around a tunnel-boring machine's shield. Stability numbers and ground settlement are evaluated across various culvert geometries and soil ratios and compared to rigorous solutions and previous models. The settlement data obtained is used to determine inflection point parameters for practical culvert design, considering dimensionless ratios. The findings highlight the importance of precise design methodologies that consider soil properties and geometry. The finite difference model proves to be a valuable tool in culvert design, providing accurate analysis of stability and settlement characteristics. The presented design figures and regression equations serve as practical tools for engineers in designing stable twin circular culverts in undrained clay. The study emphasizes the need to carefully consider soil properties and geometry for successful culvert design. In conclusion, the finite difference model offers insights into ground stability and settlement of twin circular culverts. The presented design figures and regression equations support engineers in making informed design decisions, ensuring the stability and long-term performance of culverts in undrained clay conditions.

Evaluation of the effect of TBM disc cutter wear on rock cutting efficiency

AbstractThe geometry of the disc cutter is one of the most important factors in the rock cutting process of mechanized tunnel boring machines (TBMs). However, during excavation, the optimal conditions of the disc cutter are not constant due to disc cutter wear. One of the most significant consequences of cutter wear is the increase in the contact area of the disc cutter with the rock so when the wear of the cutter exceeds its maximum limits, it should be replaced. Therefore, this study will examine the effect of disc cutter wear on TBM operational parameters such as thrust force, net penetration rate, and specific energy. In order to achieve this, six types of worn 17“ disc cutters have been studied graphically, and a method for calculating the surface of contact between the disc cutter and rock as a function of the cutter tip width has been developed. Moreover, a new equation has been proposed to predict normal and rolling cutting forces whose results are in good agreement with the reported laboratory values. As a final step, the hard rock TBM parameters of the Kani Sib water conveyance tunnel have been studied after replacement of worn disc cutters. The results show that replacing the worn disc cutters leads to an increase in penetration rate and a decrease in thrust force, resulting in an increase in specific penetration. On average, specific penetration increases by ≈35 %.

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Enhancing Urban Tunnelling Efficiency Through Real-Time Geotechnical Parameter Analysis, A Case Study at Kolkata East West Metro Project, KMRCL-1674

The exponential increase in urbanization has heightened the demand for a systematic and rapid mass transportation system in developing countries. One efficient solution has been the construction of underground networks of tunnels using Tunnel Boring Machines (TBMs). Urban tunnels, however, require careful attention to minimizing ground disturbances to avoid adverse impacts on existing infrastructure. The current study focuses on a parametric investigation based on observed trends from the Kolkata East West Metro UG1 project. It undertakes numerical regression to establish correlations, aiming to investigate geotechnical parameters derived from observed operational parameters during tunnel advancement. This correlation allows for the determination and validation of considered geotechnical parameters in real-time, thereby facilitating the optimization of operational parameters. By establishing correlations, the study examines and validates the effect of geotechnical parameters on observed settlement. In conclusion, the consideration of tunnel operational parameters and subsequent establishment of geotechnical parameters can potentially automate the functioning of mechanized tunnelling, leading to more efficient and effective tunnel construction processes.

АНАЛИТИЧЕСКИЙ ПОДХОД К ОПРЕДЕЛЕНИЮ ПРОДОЛЬНОГО СМЕЩЕНИЯ СУЩЕСТВУЮЩЕГО СБОРНОГО ТОННЕЛЯ ПРИ ВЫЕМКЕ КОТЛОВАНА ПО ВСЕЙ ДЛИНЕ

In the realm of urban construction employing excavation techniques, safeguarding existing underground structures from detrimental consequences arising from surface construction operations poses a formidable challenge. The reduction of loads due to excavation activities can induce unintended responses, potentially jeopardizing subterranean infrastructure, particularly high-safety-demanding structures like Tunnel Boring Machine (TBM) tunnels. This article introduces an uncomplicated method for ascertaining the axial displacement of TBM tunnels amidst concurrent surface excavation activities. Primarily, the approach entails the identification of stress variations encountered during soil excavation at the tunnel face. Subsequently, employing the solutions derived for the determination of tunnel deformation subjected to concentrated loads, the deformation incurred by the tunnel due to alterations in excavation-induced stress is quantified. The analytical outcomes are meticulously juxtaposed against results generated from a three-dimensional computational model. The comparative analysis demonstrates that the displacement values and axial deviations calculated using the proposed analytical method exhibit only marginal disparities of 4,3% and 1%, respectively, when compared to those obtained through finite element analysis. This study underscores the efficient predictive capabilities of the analytical method in assessing tunnel deformations, enabling a preliminary estimation of critical parameters associated with the excavation pit. These findings have significant implications for mitigating adverse impacts on existing subterranean infrastructure in densely populated urban areas.

Research on Mechanical Characteristics of TBM Cutter Bearing Based on DEM-MBD Coupled Simulation

Introduction: The cutter is a crucial excavation tool employed by Tunnel Boring Machines (TBM) for tunneling underground passages. During operation, it undergoes intense impact loads, which are transmitted to the bearings, thus posing a risk of bearing failure. Method: This paper combines the Discrete Element Method (DEM) and Multibody Dynamics (MBD) by establishing a coupled simulation patent model of the cutter and surrounding rock, various methods of cutter load profiles are compared. The study investigates the variation patterns of multi-directional rock-breaking loads on the cutter and validates the findings through wire-cutting experiments. Result: The research findings indicate that the discrepancies between the simulated and experimental mean values of the cutter's normal force and rolling force are 6.26% and 35.20%, respectively. The efficiency of cutter load transmission to the outer ring of the bearing is 99.10%, leading to the characterization of the vibration characteristics curve of the bearing's outer ring. Simultaneously, the mean square error of the cutter load obtained through the coupled method and traditional method is 78.50 kN and 76.10 kN, respectively. In comparison, the experimental load's mean square error is 99.20 kN, indicating that the coupled method better aligns with actual conditions. Conclusion: This research approach serves as a reference for TBM cutter performance analysis and bearing fatigue analysis.

Transfer component analysis-driven domain adaptation approach for estimating the life of tunnel boring machine disc cutters

Estimating the life of disc cutters in tunnel boring machines is crucial for tunnel excavation efficiency. Accordingly, generating accurate predictions of the state of disc cutters can prevent unexpected downtimes while optimizing cutting performance. The literature presents investigations of the application of machine learning (ML) for estimating the disc cutter life. Nevertheless, diverse excavation conditions and data scarcity have stimulated research on transfer learning (TL) frameworks that tailor models for different domains, that is, domain adaptation (DA), by generating a common subspace in which the discrepancies among them are minimized. This study presents an end-to-end framework to estimate the disc cutter life through transfer component analysis, a DA-based method for projecting different domains by considering excavation project datasets in a common space; then, different ML models are trained, including linear, probabilistic, and neural methods. The results revealed that the Gaussian process regression model was superior to other methods, with R2 = 0.99. In conclusion, TL is a valuable tool for estimating disc cutter life across different domains, enabling accurate predictions and leveraging knowledge to mitigate data scarcity challenges. In addition, an explainable AI analysis is conducted to quantify and identify relevant features impacting negatively on the disc cutter life, evidencing great contribution from the cutter rotation speed, quartz content, screw rate and specific energy.

Utilizing Crushed Limestone as a Sustainable Alternative in Shotcrete Applications.

Solving the challenges facing the mining industry is crucial for shaping the global attitude towards clean energy technologies associated with critical minerals extracted from depth. One of these challenges is the well-known explosion-like fractures (rockbursts) or spalling failures associated with the initiation of internal cracks. To prevent such catastrophic failure, shotcrete, as a cement grout, is widely used in tunnel support applications. In areas where the tunnels are constructed within the limestone strata using tunnel boring machines (TBM), drilling, and/or blasting, millions of cubic meters of crushed limestone (CL) in powder form are extracted and landfilled as waste. Given the fact that natural sand consumption as a raw material in the construction industry exceeds previous records, recycling of such excavation material is now becoming increasingly needed. From this perspective, this study aims to utilize crushed limestone as a potentially sustainable alternative to natural sand in shotcrete applications in deep tunnels. Accordingly, several strength characterization and crack initiation determinations through various stress-strain-based models were carried out on cylindrical samples containing different proportions of crushed limestone. By increasing the crushed limestone content in the shotcrete mix, the crack initiation stress (as a measure of the in situ spalling strength) increased as well. The results suggest that the crushed limestone has good potential to replace the natural sand in the shotcrete mixture used in tunnel support applications.

Prediction of the tunnelling advance speed of a super-large-diameter shield machine based on a KF-CNN-BiGRU hybrid neural network

The tunnelling advance speed (TAS) of a super-large-diameter tunnel boring machine (TBM) significantly affects project progress and safety. Effective prediction of the TAS of a TBM is crucial for early warning and evaluation of its excavation performance. This paper proposes a model based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) to predict TAS. This model can more effectively extract and retain many features; additionally, it considers the temporal relationships among the operating parameters of the TBM. Additionally, a Kalman filter (KF) is incorporated into the model to effectively remove input parameter noise, leading to a more accurate prediction of TBM excavation speed. A case study indicated that the pressure of the excavation chamber and the pitch angle of the shield tunnelling machine were correlated with the advance speed. The research results show that the proposed model can accurately and effectively predict TAS.

Coupled DEM-FDM numerical model for EPB shield tunnelling simulation with foam conditioning

It is widely recognized that an appropriate application of soil conditioning significantly improves the tunnelling performance of earth pressure balance (EPB) shield tunnel boring machines (TBMs). This study presents a numerical approach that simulates an EPB shield tunnelling, considering the injection of foam into the soil. To enhance computational efficiency and reduce the number of particles simulated in the ground formation, the numerical study coupled the discrete element method (DEM) and the finite difference method (FDM). The properties of in-situ soil and foam-conditioned soil were incorporated into the DEM domain by determining the contact parameters of particles using two calibration models: the triaxial compression test and the pressurized vane shear test models. With the established model, tunnelling analyses were conducted under specific TBM operation conditions, such as the penetration rate, and rotational speed of the cutter head and the screw conveyor. The effect of foam injection on EPB shield TBM operation was evaluated by adjusting the contact parameters of DEM elements. The numerical simulations revealed that foam conditioning reduced the torque, thrust force, and forces on cutting tools. These simulations provided valuable insights into the applicability of the numerical approach in assessing foam conditioning for EPB shield TBMs.

Energy-driven TBM health status estimation with a hybrid deep learning approach

This study provides a hybrid deep learning approach that enables the researchers to estimate the Tunnel Boring Machine (TBM) health status during excavation process using energy consumption data. By analyzing the energy forms and paths in the tunneling process, the key energy factors' consumption performance behavior affecting TBM safety is identified. The Ensemble Empirical Mode Decomposition (EEMD) method and a Convolution neural network-Long short term memory-Depth Neural Network (CNN-LSTM-DNN, CLDNN) model are integrated for Health Performance Parameter (HPP) prediction. A classification method is constructed by the Generative Adversarial Imputation Networks (GAIN) model for health status estimation. The SHapley Additive exPlanations (SHAP) method is performed to quantify the relationships between energy consumption and system health status. A tunneling case in Singapore is used to test the effectiveness of the developed methodology. It is found that (1) the proposed method provides TBM cutter wear and cooling oil prediction with a R-square (R2) of 0.8591 and 0.8727, respectively, and (2) classification results indicate that the estimation F1-score of 0.9863 can be achieved based on health status evaluation indicator (HSEI). The novelty of the proposed approach lies in its capabilities of (1) fully extracting and utilizing the energy consumption information in every regard to predict TBM health performance; (2) providing a good one-step-ahead health status estimation for the TBM excavation process. Therefore, this study proposes a feasible direction for the application of energy consumption data in TBM health status estimation.

Quantifying tunneling risks ahead of TBM using Bayesian inference on continuous seismic data

Seismic tunnel look-ahead methods, used during Tunnel Boring Machine (TBM) excavation, provide key advantages in forecasting potential tunneling risks and improving operational reliability and efficiency. However, these methods often grapple with data uncertainty due to the complex subsurface medium and incomplete data, causing tunnel engineers to question seismic prediction reliability. The analysis of uncertainty for seismic tunnel look-ahead methods remains largely unexplored. In this paper, we delve into these uncertainties using field tunnel look-ahead seismic data. We propose a Bayesian update workflow, grounded in a novel real-time seismic tunnel look-ahead method, to quantify tunneling risks and mitigate seismic uncertainty using continuous data measurement. We construct probability density functions of potential risks from seismic prediction outcomes and introduce Bayesian inference to update the probability with multiple observations. We demonstrate this proposed method’s application in various geological conditions in an urban Singapore area through several field experiments. Results indicate that our method enhances geological interface prediction reliability, minimizes interference from abnormal bodies adjacent to the tunnel, and demonstrates the ability to integrates interpreted geological information to refine tunnel look-ahead predictions.