The extremely high wear rate of disc cutter rings of tunnel boring machines (TBMs) limits their tunneling efficiency and increases their cost. The surface properties of the cutter ring significantly ...affect the wear rate. In this study, the effect of coatings on the cutter wear rate and wear mechanism under actual working conditions was investigated by comparing the wear test results of three different surface properties of reduced scale cutter rings with granite. The three cutter rings' vertical load, vertical vibration, mass loss, microhardness, temperature, surface morphology, and wear debris were compared. The results demonstrated that the coating had a profound effect on the wear rate of the cutter ring. The wear rate of the Fe-based amorphous-coated cutter ring was 20.3% of that of a conventional cutter ring. The wear mechanisms of the conventional cutting ring were ploughing and microcutting, whereas those of the coated cutting ring were microcutting and microcracking. Surface cracks in the Fe-based amorphous coated cutter ring have a negligible effect on its mass loss. The coatings peeled off more quickly with more hole defects. These results have guiding significance for increasing the wear resistance of disc cutters and improving their service life.
•Fe-based coatings were prepared on the surface of cutter ring material.•The wear tests of cutter rings under Fe-based coating conditions were conducted.•Wear behavior of cutter rings under coating conditions were revealed preliminarily.
The Ylvie model is a novel method towards transparent Tunnel Boring Machine (TBM) data analysis for tunnel construction. The model innovatively applies machine learning to automate friction loss ...computation per stroke, enhancing TBM performance prediction in varying geomechanical environments. This research considers the complexities of TBM mechanics, focusing on the Thrust Penetration Gradient (TPG) and shield friction influenced by geological conditions. By integrating operational data analysis with geological exploration, the Ylvie model transcends traditional methodologies, allowing for a comprehensible and specific determination of the friction loss towards more precise penetration rate prediction. The model’s capability is validated through comparative analysis with established methods, demonstrating its effectiveness even in challenging hard rock tunneling scenarios. This study marks a significant advancement in TBM performance analysis, suggesting potential for the expanded application and future integration of additional data sources for comprehensive rock mass characterization.
Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM ...excavation efficiency. Conventionally, skilled operators manually adjust numerous tunnelling parameters to regulate the machine's progress. In contrast, machine learning (ML) algorithms offer a promising avenue where computers learn from operator actions to establish parameter relationships autonomously. This study introduces an innovative approach to enhancing operator monitoring and TBM data comprehension. A robust correlation between TBM operator behaviour and TBM logged data is established by leveraging an Optuna-assisted ML methodology-the research light on the intricate dynamics influencing TBM advance rate parameters. Operational data is collected from micro slurry tunnel boring machine (MSTBM) umbrella support excavations. The proposed framework harnesses Optuna, an advanced hyperparameter optimization platform, to dynamically refine jack speed and torque settings. Through meticulous analysis of the interplay between TBM operator decisions and real-time logged data, the AI model discerns patterns, empowering informed decision-making. Using Optuna, a range of models, including random forest (RF), K-nearest neighbours (kNN), decision tree (DT), XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were automatically compared and tuned. The best model's (RF) performance is evaluated through a correlation coefficient (R
) of 96%, mean squared error (MSE) of 119.7, and mean absolute error (MAE) of 4.42 for jack speed decision making while 83% of R
, MSE of 0.62, and MAE of 0.42 for the torque decision making. This intelligent model can assist the TBM operator in making decisions about TBM control.
This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization efficiency of tunnel boring machines (TBMs). The rock mass quality ...ratings, which are based on the Chinese code for geological survey, were used to provide “labels” suitable for supervised learning. As a result, the generation of machine prediction for rock mass grades reasonably agreed with the ground truth documented in geological maps. In contrast, the main operational parameters, i.e., thrust and torque, can be reasonably predicted based on historical data. Consequently, 18 collapse sections of the Yinsong project have been successfully predicted by several researchers. Preliminary studies on the selection of the optimal penetration rate and cost were conducted. This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China. For the first time, large and well-documented TBM performance data has been shared for joint scientific research. Moreover, the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.
This study aims to propose four Machine Learning methods of Gaussian process regression (GPR), support vector regression (SVR), decision trees (DT), and K-nearest neighbors (KNN) to predict disc ...cutter's life of TBM. 200 datasets monitored during the Alborz service tunnel construction in Iran, including TBM operational parameters, geometry, and geological conditions, were applied in the models. The 5-fold cross-validation method was considered to investigate the prediction performance of the models. Finally, the GPR model with R2 = 0.8866/RMSE = 107.3554, was the most accurate model to predict TBM disc cutter's life. KNN model with R2 = 0.1753/RMSE = 288.9277, produced the minimum accuracy. To assess each parameter's contribution in the prediction problem, the backward selection method was used. The results showed that TF, RPM, PR, and Qc parameters significantly contribute to TBM disc cutter's life. However, RPM and PR parameters were more and less significant compared to the others.
•Using 200 datasets of TBM disc cutter life.•Applying the 5-fold cross-validation method in modeling.•Using four machine learning methods to predict TBM disc cutter life.•Identify the most accurate machine learning method.•Identify the most effective parameters on TBM's disc cutter life.
Contractor G. Hinteregger & Söhne Baugesellschaft mbH submitted an alternative proposal for the ”Rodundwerk I, new headrace and distribution pipe system“ project commissioned by Illwerke vkw AG which ...uses a new launch structure for an open gripper TBM. An Austrian utility model has been registered for this new development. This paper describes the process.
Im Zuge des Projekts „Rodundwerk I, Neuer Kraftabstieg und Verteilrohrleitung” der Illwerke vkw AG hat das ausführende Unternehmen, die Fa. G. Hinteregger & Söhne Baugesellschaft mbH, ein Alternativangebot abgegeben, im Zuge dessen eine neuartige Anfahrkonstruktion für eine offene Gripper‐TBM (TBM‐O) eingesetzt wurde. Für diese Neuentwicklung wurde ein österreichisches Gebrauchsmuster angemeldet. Im nachfolgenden Artikel soll dieses Verfahren vorgestellt werden.
The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy ...of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
•The metallic gasket sealing performance was confirmed with required compression load.•The structural assessments satisfied the RCC-MR structural requirements.•Cooling circuits were improved ...considering flow and draining performances.•The manufacturing feasibility was preliminary assessed.
The operation and test of mock-ups of tritium breeding blankets relevant for a future commercial reactor is one of the goals of the ITER machine. To accomplish this goal, mock-ups of breeding blankets, called Test Blanket Modules (TBMs), are installed in three ITER equatorial ports. Each TBM and the associated shield form a TBM-set that is mechanically attached to a steel frame called TBM Frame. A Frame and two TBM-Sets form a TBM Port Plug (TBM PP). The ITER Organization is responsible for the design and manufacture of the TBM Frames and of the Dummy TBMs that could replace the TBM-sets in case they were not available. This paper describes the recent results of the design supporting analyses for the TBM Frames and Dummy TBMs that is presently in the preliminary design stage and their impact on the design.
During hard rock tunnel boring machine (TBM) excavation, shields behind the cutterhead are usually in direct contact with the tunnel wall and therefore subjected to friction forces that occur within ...this interface. The effect of shield friction in hard rock TBM tunneling has received little attention so far and literature on this topic is scarce and conflicting. To investigate the friction coefficient for the planning of TBM excavations, specialized shear tests were conducted where steel specimens were sheared against lithologically different rock specimens under representative normal forces and shearing speeds. The tests were executed with and without the use of bentonite lubrication. The results show that there is a significant difference between different lithologies and also that using bentonite does not lower the friction coefficient as expected. To elaborate on the effect of shield friction during construction, a framework for interpretation of TBM operational data based on experience from construction sites is provided. Whereas thorough interpretation of the data enables one to draw conclusions about the shield friction, it still remains difficult to assess the real effect of shield friction due to the limited possibilities to observe the ongoing phenomena. This study therefore provides the basis for theoretical and practical assessments of the effect of shield friction for the planning and construction phase of a tunnel. This becomes increasingly important in the light of new contractual developments that aim at differentiating “standard” from “special” advance in an objective and reproducible way.
Highlights
Thorough elaboration of the effect of shield friction for hard rock tunnel boring machine excavation.
First presentation of special shear tests to assess the friction coefficient for different rock types, with or without the use of bentonite.
Discussion of the use of tunnel boring machine operational data to draw conclusions about the effect of shield friction during excavation.
Questioning the effectiveness of bentonite lubrication for tunnel boring machine excavation.
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