Boring Machine Research Articles

Carbon Footprint Evaluation in Tunnels Excavated in Rock Using Tunnel Boring Machine (TBM)

The generation of CO2 is an important element in assessing the environmental impact generated in a tunnel construction project, making this knowledge very useful for evaluating different alternatives. In this study, an analysis of the carbon footprint has been carried out, including the main elements during the construction phase of a tunnel employing a tunnel boring machine (TBM). The research proposes several options for an easy and quick calculation of CO2 generation in a design phase. Its determination can be crucial for decision-making before and during the execution of any tunnel in the near future. The estimation models have been validated based on real case studies, defining the carbon footprint of each construction element. The proposed procedure can apply to any tunnel. However, it should be noted that it is an approximate analysis, and the limitations described in each section should be considered. The main CO2 generator found in the construction process is the lining element; the percentage varies between 50% in tunnels with smaller diameters (4–5 m) and 75% for tunnels with larger diameters (9–10 m), followed by the auxiliary elements, 16%, and the operation of the tunnel boring machine itself, 11.2%, while the other parts remain in a range between 1.3 and 5.7%. This knowledge makes it possible to define the aspects on which efforts should be focussed to reduce the carbon footprint of the tunnel construction process.

Predicting advance rate and cutter life in TBM tunnelling using evolutionary algorithm

ABSTRACT Tunnelling with a Tunnel Boring Machine (TBM) offers several advantages over conventional tunnelling methods when covering long distances, particularly regarding time, safety, and environmental impact. Prior to starting a TBM project, performance analysis is a crucial stage necessary to inform the initial investment decision and project cost estimation. However, this analysis requires extensive fieldwork and laboratory studies. The Advance Rate (AR), calculated from the Utilization Factor (U) – the ratio of excavation time to total project time – is a widely accepted measure of TBM performance. Total project time encompasses various parameters, including boring time, cutter inspection and replacement times, and constant factors like re-gripping, service, and maintenance times. In this study, we employed analytical methods, statistical analysis, and genetic programming approaches to evaluate the AR and thus enhance TBM performance analysis. Gamma tests were conducted to identify interrelated parameters in TBM operations. The results from these tests showed that the Applied Force per Cutter (FN) and penetration values (together forming the Field Penetration Index or FPI) are useful in estimating cutter consumption. While FN can be determined through laboratory tests, its field determination is challenging. We have developed several charts and a function derived from genetic programming to estimate the AR of a TBM. The study demonstrated that this function, formulated using genetic programming, possesses strong predictive capability and can significantly improve TBM performance analysis.

A novel semi-supervised prototype network with two-stream wavelet scattering convolutional encoder for TBM main bearing few-shot fault diagnosis

Accurately sensing the main bearing state and diagnosing fault types is crucial to ensure the safe operation of the main drive system of tunnel boring machines. Currently, the research on large-scale bearing fault diagnosis in industrial scenarios is severely limited by the quality and quantity of monitoring data. Conventional external vibration monitoring devices are difficult to adapt to complex and harsh working conditions of excavation equipment, and constantly changing low-speed and heavy-load operating conditions make similar labeled samples very scarce. To tackle this concern, we propose a semi-supervised prototype network with the two-stream wavelet scattering convolutional encoder (TWSCE-SSPN) based on roller state signals. By fusing radial and axial features of rollers using the two-stream structure and employing wavelet scattering transform and attention mechanism in the convolutional feature encoder, the model exhibits excellent feature mapping capabilities. Following the semi-supervised meta-learning paradigm, the proposed model uses the prototype generated by unlabeled sample features to modify the initial prototype generated by labeled sample features to augment the accuracy of classification in few-shot learning. The integrated sensing roller main bearing testbed was set up and fault datasets were established to verify the few-shot classification and anti-noise ability of the algorithm. Experimental results show that TWSCE-SSPN achieved 98.17 % accuracy at 1 shot, which is at least 18.17 % higher than existing methods. Furthermore, even under a signal-to-noise ratio of 0 dB, the few-shot recognition accuracy can remain 91.83 %. This verifies the superiority of the model in diagnosing main bearing faults under few-shot and strong noise conditions.

Numerical investigation of temperature distribution on a tunnel boring machine disc cutter affected by geomechanically and excavation parameters

This study focuses on the thermal behavior of tunnel boring machines (TBMs) through an in-depth investigation into the temperature distribution of their disc cutters. Utilizing the differential quadrature method (DQM), the research conducts a comprehensive numerical analysis to assess the impact of excavation and geological parameters on disc cutter temperature and wear. The accuracy of the DQM model is validated against the finite difference method (FDM), demonstrating comparative results with reduced computational requirements. The findings indicate a significant correlation between disc cutter temperature and various factors, such as rotational speed, spacing, geological conditions, and material strength. Notably, increased spacing or cutter speed leads to higher temperatures and accelerated cutter wear. Moreover, geological factors, particularly rock strength, influence friction coefficients, affecting disc cutter temperatures significantly. For instance, even a slight increase in cutter spacing results in a substantial 65% rise in cutter consumption, underscoring the relevance of these findings for life cycle assessment (LCA) evaluations across diverse geological and environmental conditions in TBM operations.

Structural Optimization of the Main Bearing in a Tunnel Boring Machine Considering Clearance

Abstract An optimal design method for the main bearing of a tunnel boring machine is proposed. In this method, the fatigue life is used as the objective function. Structural parameters, including clearance, are considered as design variables. First, a quasi-static model of the main bearing and a calculation model of the fatigue life are established. The correctness of the theoretical method is verified by comparing it with the calculation results of the finite element method. Next, the influence of clearance on the load-carrying performance under external loads is analyzed. There is an optimal negative clearance for the axial loaded and radial rows. With the increase in the external loads, the optimal negative clearance gradually decreases. The variation laws of the load-carrying performance for the axial loaded and supporting rows affected by axial clearance mainly depend on the bias load degree of the main bearing. Finally, based on the optimal design model of the main bearing, the optimal internal structure is obtained using the genetic algorithm. The optimized fatigue life is improved by 92.2%. The load-carrying performance of the optimal main bearing has also been significantly enhanced compared to the initial design. Therefore, the proposed optimization method provides a practical approach to the main bearing design.

Analysis of Zhejiang Heping Financing Lease Model in the Context of Dual-Business Integration

Zhejiang Heping Automobile Sales and Service Co., Ltd., established in 2000, is a leading automotive sales enterprise integrating car sales and repair services. Undergoing three stages of development, namely, expanding and strengthening car sales, venturing into financing leasing, and consistently enhancing efficiency, the company has successfully achieved a clever integration of "financing leasing + car sales." This integration ensures a steady and ample supply of funds for the enterprise while simultaneously increasing profits. Seizing the opportunities of era development and conducting a rational analysis of the industry's strengths and weaknesses, Zhejiang Heping has innovatively adopted a new approach, combining "car sales + tunnel boring machine financing leasing," thereby ensuring a robust cash flow for the enterprise, continuously expanding business operations, reducing operational risks, and increasing overall company returns.

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

LABORATORY INVESTIGATION OF THE EFFECT OF SODIUM SILICATE AND BENTONITE ON THE MECHANICAL PROPERTIES OF THE GROUT BEHIND THE SEGMENT IN MECHANIZED EXCAVATION

Nowadays, the use of Tunnel Boring Machines (TBM) in tunnelling has increased in urban environments. One of the effective parameters in the excavation process is the use of grout behind the segments to prevent ground settlement and penetration into the tunnel. The investigation of the effects of two-component grout (bentonite and sodium silicate) on the mechanical properties of the grout plays a crucial role in mechanized excavation. Therefore, this article aims to investigate the effect of various parameters on the properties of the two-component grout. Eight different mixing designs were considered in this research. Gel-time tests, viscosity test, water bleeding test, uniaxial compressive strength tests (UCS), and direct shear tests were conducted for each mixing design. The UCS tests were performed for eight mixing designs and at four different ages of the grout, while the direct shear test was conducted for five mixing designs at the age of one day. The results showed that increasing the sodium silicate content from 4% to 9.4% reduced the Gel-time to 7 seconds, and increasing the bentonite content from 2.3% to 4.58%, increased the marsh funnel time to 12 seconds. The uniaxial compressive strength of the grout increased to 0.682 MPa at the age of 28 days. Finally, by conducting the direct shear test on the grout and obtaining the parameters of cohesion and internal friction angle, it was determined that these two parameters depend on the values of bentonite and sodium silicate, and they change with the increase or decrease of these grout components.

Stability design of a geosynthetic reinforced rock cuttings wall

Rocks excavation with tunnel boring machines induces a lot of cuttings. This granular material needs to be stored. When reinforced by geosynthetics, significant tensile stresses can be obtained in the resulting composite structure which allows to build steep slopes. A real case study of a reinforced rock cuttings wall is presented in this paper. To study accurately this complex problem, two calculation methods were used: a limit equilibrium (using the Bishop criteria), classically used for these kinds of stability problems and two-dimensional finite element calculations. These two methods are different and give different results. The FE calculation is the more representative and the more conservative method for such designs when considering the studied case.

A New Sequential Sampling Method for Surrogate Modeling Based on a Hybrid Metric

Abstract Sequential sampling methods have gained significant attention due to their ability to iteratively construct surrogate models by sequentially inserting new samples based on existing ones. However, efficiently and accurately creating surrogate models for high-dimensional, nonlinear, and multimodal problems is still a challenging task. This paper proposes a new sequential sampling method for surrogate modeling based on a hybrid metric, specifically making the following three contributions: (1) a hybrid metric is developed by integrating the leave-one-out cross-validation error, the local nonlinearity, and the relative size of Voronoi regions using the entropy weights, which well considers both the global exploration and local exploitation of existing samples; (2) a Pareto-TOPSIS strategy is proposed to first filter out unnecessary regions and then efficiently identify the sensitive region within the remaining regions, thereby improving the efficiency of sensitive region identification; and (3) a prediction-error-and-variance (PE&V) learning function is proposed based on the prediction error and variance of the intermediate surrogate models to identify the new sample to be inserted in the sensitive region, ultimately improving the efficiency of the sequential sampling process and the accuracy of the final surrogate model. The proposed sequential sampling method is compared with four state-of-the-art sequential sampling methods for creating Kriging surrogate models in seven numerical cases and one real-world engineering case of a cutterhead of a tunnel boring machine. The results show that compared with the other four methods, the proposed sequential sampling method can more quickly and robustly create an accurate surrogate model using a smaller number of samples.

Performance and Applicability of Recognizing Microseismic Waveforms Using Neural Networks in Tunnels

The sudden and harmful nature of rockbursts in tunnels necessitates an accurate and applicable method for automatically recognizing rock fracture signals during rockburst microseismic (MS) monitoring. In this paper, the performance and applicability of recognizing MS waveforms using an artificial neural network (ANN) and a deep neural network (DNN) were studied in tunnels excavated by different methods. The results show that ANN performs very well in recognizing rock fracturing waveforms with a signal-to-noise ratio (SNR) ≥ 3 but has a low accuracy for those with an SNR < 3. The DNN also performs well for waveforms with SNR ≥ 3, and has a relatively high accuracy for waveforms with SNR < 3. The ANN model can be used in tunnels excavated by drilling and blasting (D&B) since there are fewer “small” rock fracturing events. The DNN model is applicable in tunnels excavated by the tunnel boring machine (TBM), recognizing more “small” events. In addition, the ANN model is a better choice, with fewer training samples at the initial stage of monitoring working. With continuous monitoring, the DNN model can be used to ensure and improve the accuracy. These results lay a foundation for automatic rockburst MS monitoring techniques in tunnel engineering.

Comprehensive analysis of oversized cargo transportation market in Poland (2001-2022): Trends, regulations, and industry dynamics

The oversized cargo transportation market in Poland actively participates in developing the economy, providing services necessary for implementing large infrastructure and industrial projects on a national and European scale. Development and functioning of this sector in the EU and globally requires overcoming many organisational, technical and administrative barriers and difficulties, and the authors of several publications indicate how these services are developing. The current study contains a detailed analysis of data from 2001-2022. The paper characterises the changes in particular years, indicating the main causes of the occurring processes, and defines the existing trends. It presents the legal framework that regulates the functioning of the domestic market. Moreover, it discusses the number of permits and the value of fees for issuing permits collected by the General Directorate for National Roads and Motorways (Generalna Dyrekcja Dróg Krajowych i Autostrad, GDDKiA) over two decades. The article also evaluates trends concerning the number of licenses more than 50% decrease compared to the maximum in 2008. It assesses the course of changes in values, which in 2021 reached their highest level during the period under assessment. Furthermore, it discusses the differences that occurred in the number of permits issued and applications submitted in the years 2014-2022. It describes the structure of the number of permits issued in categories IV-VII in 2014-2020 and categories III-V in 2021-2022. It characterises the course of the largest operation in the history of Polish road haulage - the transport of an abnormally-sized TBM (Tunnel Boring Machine) - in terms of requirements and problems that occurred during the preparation and fulfilment of this order and were typical for the functioning of the sector. The evaluation provides the basis for summaries and conclusions on the Development and condition of the oversized cargo transportation sector in Polish road transport sector.

Physics-informed deep reinforcement learning for enhancement on tunnel boring machine's advance speed and stability

The traditional mode of Tunnel Boring Machine (TBM) operation is limited in their applicability and efficiency to meet the growing demand for underground spaces. Current methods to address this problem often lack physics interpretability and automatic adaptivity. In response to this issue, this paper describes an approach that integrates a physics-informed reinforcement learning (PIRL) algorithm into a TBM operation. The method combines a physics-informed machine learning (PIML) model and physics reward functions considering the working mechanism of earth pressure balance (EPB) TBMs, forming into a physics-informed Twin Delayed Deep Deterministic (pTD3) algorithm. The study reveals that the TBM performance can be improved by 69.3% using the pTD3 algorithm compared to manual operation. Integrating physics knowledge into reinforcement learning proves significantly effective in enhancing the TBM operations. The proposed method has the potential to revolutionize TBM operation and pave the way for more efficient, reliable, and automatic tunnel construction.

Risk analysis in underground tunnel construction with tunnel boring machines using the Best–Worst method and data envelopment analysis

BackgroundThe use of tunnel boring machines is continuously increasing; however, this activity faces many risks, resulting in negative events. MethodsA novel approach using the best–worst method and a data envelopment analysis model was proposed to analyze the risks of underground tunnel construction with tunnel boring machines. The proposed approach was validated using a realistic case study of metro construction in Thailand. ResultsThe proposed approach efficiently analyzed the risks and produced more solid findings. The most critical and least affected risks can be identified according to risk scores in descending order. ImplicationThis study contributes a new method of risk analysis that benefits project managers and stakeholders who design risk management plans to reduce the occurrence and mitigate the severity of metro works. OriginalityThe new risk analysis can obtain the best compromise ranking of risks based on decision-makers’ preferences, probabilities, and various consequences under different circumstances.

K-means-based heterogeneous tunneling data analysis method for evaluating rock mass parameters along a TBM tunnel

Rapid and accurate judgment of the rock mass condition is the key to guaranteeing the safety and efficiency of tunnel boring machine (TBM) tunneling. This paper proposes a method for evaluating rock mass parameters based on K-means clustering, grouping tunneling areas according to the values of TBM tunneling parameters. A dataset including rock mass and TBM tunneling data is treated by logistic normalization and principal component analysis (PCA), and large volumes of tunneling data with different features are transformed into appropriate volumes of dimensionless data. K-means clustering is used, samples are grouped according to the values of tunneling data, and the specific ranges as defined by clustering are regarded as the unified evaluated results of each group. Based on the C1 part of the Pearl Delta water resources allocation project, 100 training samples and 30 testing samples were field-collected, and the proposed method was realized by the training samples and verified by the testing samples. The evaluation accuracies of uniaxial compressive strength (UCS), and joint frequency (Jf) were 90%, and 86.7% respectively, demonstrating that the evaluation had acceptable values, and the proposed method was greatly helpful for judging rock conditions.

Soil Stabilisation prior to the Driving of the Tunnel Boring Machine under Buildings and Facilities During the Construction of the Sofia Metropolitan

Soil Stabilisation prior to the Driving of the Tunnel Boring Machine under Buildings and Facilities During the Construction of the Sofia Metropolitan

Experimental and numerical studies on mechanical behavior of pre-stressed infilled rock joints subjected to horizontal vibration loading

Underground jointed rock masses are usually subjected to a coupled effect of static pre-stress and vibration disturbance from rock-cutting in Tunnel Boring Machines (TBMs) tunneling. To investigate the mechanical behavior of pre-stressed infilled rock joints disturbed by horizontal vibration loading, a series of laboratory and numerical experiments were conducted, based on the in-situ vibration characteristics during TBM rock-cutting. The P-wave velocity and direct shear test techniques are combined to quantitatively characterize the damage of infilled rock joints. The experimental results reveal that the damage degree of rock joints exhibits a nonlinear increase with the increase of vibration frequency and amplitude, and the decrease of static pre-stress. Low static pre-stress (below 0.5 MPa), high vibration frequency (above 70 Hz) and high vibration amplitude (above 0.3 mm/s) are adverse factors to the stability of rock joints. From the PFC2D simulation, the contact force chain and contact force field were analyzed to elucidate the failure mechanism of infilled rock joints. The failure mode of infilled rock joints is closely related to the level of parameters, consisting with the results in laboratory experiments. In addition, the comparison of vibration results for weakly and strongly filling material demonstrates that grouting reinforcement enhanced the vibration resistance of in-situ jointed rock masses.

Classifying Rock Fragments Produced by Tunnel Boring Machine Using Optimized Convolutional Neural Network

Classifying Rock Fragments Produced by Tunnel Boring Machine Using Optimized Convolutional Neural Network

Analysis and prediction of small-diameter TBM performance in hard rock conditions

Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is integral to tunnelling project planning and execution. It has been applied in the industry for several decades with varying success. Most prediction models are based on or designed for large-diameter TBMs, and much research has been conducted on related tunnelling projects. However, only a few models incorporate information from projects with an outer diameter smaller than 5 m and no penetration prediction model for pipe jacking machines exists to date. In contrast to large TBMs, small-diameter TBMs and their projects have been considered little in research. In general, they are characterised by distinctive features, including insufficient geotechnical information, sometimes rather short drive lengths, special machine designs and partially concurring lining methods like pipe jacking and segment lining. A database which covers most of the parameters mentioned above has been compiled to investigate the performance of small-diameter TBMs in hard rock. In order to provide sufficient geological and technical variance, this database contains 37 projects with 70 geotechnically homogeneous areas. Besides the technical parameters, important geotechnical data like lithological information, unconfined compressive strength, tensile strength and point load index is included and evaluated. The analysis shows that segment lining TBMs have considerably higher penetration rates in similar geological and technical settings mostly due to their design parameters. Different methodologies for predicting TBM penetration, including state-of-the-art models from the literature as well as newly derived regression and machine learning models, are discussed and deployed for backward modelling of the projects contained in the database. New ranges of application for small-diameter tunnelling in several industry-standard penetration models are presented, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.

High-precision cutterhead torque prediction for tunnel boring machines using an attention-based embedded LSTM neural network

For avoiding the serious accident caused by the cutterhead jamming of tunnel boring machines, high-precise cutterhead torque (CHT) prediction has important implications for adjusting operational parameters. For accurately predicting the CHT, a novel CHT prediction approach is presented. Firstly, correlation analysis based on cosine similarity are performed to select the monitoring data highly correlated to CHT. Secondly, a cell state extraction module (CSEM) is designed to replace the saturation-prone forget gate for reducing the saturation rate of forget gate. With CSEM, a novel embedded long short-term memory (ELSTM) network with dual-memory structure is proposed and its state transition updating formulas are derived. Lastly, an attention-based deep neural network is designed for fusing the sequential features learned by ELSTM at all instants into the predicted CHTs. Compared with the current approaches, the contrast experiments on an in-situ datasets show that the proposed method has stronger predictive ability under various geological conditions.