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Innovation in TBM traffic tunnels
Tunnel Boring Machine (TBM) constructed tunnels are widespread, and can deliver significant environmental and cost benefits. However, as noted in the noteworthy examples of TBM traffic tunnels presented in this book, there are still important challenges associated with them, linked in particular to structural safety in the event of earthquakes, as well as cost and safety issues during operation. To face these challenges, Innovation in TBM Traffic Tunnels presents three innovative concepts in the field of construction of TBM rail and road tunnels: the TISB concept that improves the structural safety of those built on soft soil in seismic areas, and the TMG and TMF concepts, for rail and road tunnels, respectively, that allow for significant reduction of their cost and the improvement of safety during operation. Examples of the application of these new concepts in the conceptual design of specific tunnel cases are presented and compared with solutions based on common approaches, demonstrating the additional benefits of these concepts. The book also draws attention to other innovations in TBM tunnelling that may improve the construction of tunnels in the future, especially when using the concepts mentioned above. Innovation in TBM Traffic Tunnels is aimed at professionals involved in the planning, design, and construction of tunnels for transport infrastructure, including authorities, consultants and construction companies, worldwide.
Book
A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance
Full-face tunnel boring machine (TBM) is a modern and efficient tunnel construction equipment. A reliable and accurate TBM performance (like penetration rate, PR) prediction can reduce the cost and help to select the appropriate construction method. Therefore, this study introduces a new hybrid intelligence technique, i.e., grey wolf optimizer-feature weighted-multiple kernel-support vector regression (GWO-FW-MKL-SVR) to predict TBM PR. For this purpose, a tunnel in China was selected as a case study and the most important parameters on TBM performance, i.e., chamber earth pressure, total thrust, cutterhead torque, cutterhead speed, cohesion, internal friction angle, compression modulus, the ratio of boulder, uniaxial compressive strength and rock quality designation, were measured and considered as model inputs. To show the capability of the GWO-FW-MKL-SVR model, three models including biogeography-based optimization (BBO)-FW-MKL-SVR, MKL-SVR, and SVR were also proposed to predict the TBM PR. To select the best predictive models, some performance indices, i.e., coefficient of determination (R2), root mean square error (RMSE) and variance accounted for (VAF) were considered and calculated. The obtained results showed that the GWO-FW-MKL-SVR model receives the highest accuracy in predicting the TBM PR for both train and test stages. R2 values of 0.946 and 0.894, for train and test stages of the GWO-FW-MKL-SVR model, respectively, confirmed that this new hybrid model is considered as a powerful, applicable and simple technique in predicting the TBM PR. By performing feature weight analysis, it was found that the effects of the uniaxial compressive strength, rock quality designation and cutterhead speed features were higher than the other input parameters on the TBM PR.
Journal Article
Brief communication: RADIX hole
The RADIX (Rapid Access Drilling and Ice eXtraction) optical dust logger is part of the exploratory drilling system developed at the University of Bern. It was previously untested because no RADIX borehole reached the depth of the required bubble-free ice. In June 2023, we fitted the logger with an adapter to enable operation and testing in the deep EastGRIP (East Greenland Ice-core Project) borehole. A high-quality dust record was obtained for the Bølling-Allerød-Younger Dryas-Early Holocene period. The light scattered by the dust in the ice around the borehole was slightly higher than the detection range of the logger, requiring a reduction in the sensitivity for future deployments.
Journal Article
A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis
As a critical component of a tunnel boring machine (TBM), the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive. Currently, under conditions of strong noise and complex working environments, traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy. To address these issues, we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network (RDMA-WDAN) for TBM main bearing fault diagnosis. Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules, achieving better domain adaptation despite significant domain interference. The residual denoising component utilizes a convolutional block to extract noise features, removing them via residual connections. Meanwhile, the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel-spatial attention mechanism to extract multiscale features, concentrating on deep fault features. During training, a weighting mechanism is introduced to prioritize domain samples with clear fault features. This optimizes the deep feature extractor to obtain common features, enhancing domain adaptation. A low-speed and heavy-loaded bearing testbed was built, and fault data sets were established to validate the proposed method. Comparative experiments show that in noise domain adaptation tasks, proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%, 23.088%, 43.133%, 16.344%, 5.022%, and 9.233% over dense connection network (DenseNet), squeeze-excitation residual network (SE-ResNet), antinoise multiscale convolutional neural network (ANMSCNN), multiscale attention module-based convolutional neural network (MSAMCNN), domain adaptation network, and hybrid weighted domain adaptation (HWDA). In combined noise and working condition domain adaptation tasks, the RDMA–WDAN improves the accuracy by 45.672%, 23.188%, 43.266%, 16.077%, 5.716%, and 9.678% compared with baseline models.
Journal Article
Formulas and calculations for drilling operations
\"Presented in an easy-to-use format, this second edition of Formulas and Calculations for Drilling Operations is a quick reference for day-to-day work out on the rig; It also serves as a handy study guide for drilling and well control certification courses; Virtually all the mathematics required on a drilling rig is here in one convenient source, including formulas for pressure gradient, specific gravity, pump, output, annular velocity, buoyancy factor, and many other topics; Whether open on your desk, on the hood of your truck at the well, or on an offshore platform, this is the only book available that covers the gamut of the formulas and calculations for petroleum engineers that have been compiled over decades; Some of these formulas and calculations have been used for decades, while others are meant to help guide the engineer through some of the more recent breakthroughs in the industry's technology, such as hydraulic fracturing and enhanced oil recovery. There is no other source for these useful formulas and calculations that is this thorough; An instant classic when the first edition was published, the much-improved revision is even better, offering new information not available in the first edition, making it as up-to-date as possible in book form; Truly a state-of-the-art masterpiece for the oil and gas industry, if there is only one book you buy to help you do your job, this is it!\"-- Provided by publisher.
Book
Experimental and Numerical Simulation Investigation on Rock Breaking for Granite with Laser Irradiation
To investigate the efficacy of laser as an auxiliary rock breaking technology and its compatibility with TBM (tunnel boring machine) tools, we conducted three experimental series with different laser power levels and scorching durations. Static tests showed that higher laser power led to greater rock-breaking depth. The relationship between laser power and kerf depth was linear for short irradiation times but followed a logarithmic trend for longer times. However, laser assisted by high-pressure gas blowing rock breaking showed a consistent linear increase in depth with rising laser power. Mobile laser rock breaking tests demonstrated decreased kerf depth with increased movement speed. Numerical simulations using a thermal–mechanical coupling model confirmed that longer laser scorching times led to more extensive internal cracks in rocks, primarily shear cracks, and reduced the force needed for hob rock breaking. This research suggested the feasibility of laser technology as a complement to TBM hobs for rock breaking.
Highlights
This study investigated the effects of laser power, irradiation time, high-pressure gas blowing and laser movement speed on laser irradiation breaking granite
Blowing high-pressure gas-assisted laser irradiation rock can improve kerf depth markedly.
Laser irradiation rock induced mainly shear cracks and a minority of tensile cracks.
Hob rock breaking simulation suggested that 5–10 s laser irradiation is optimal.
Journal Article
Computational solid mechanics for oil well perforator design
This book presents the computational methods for solving the solid mechanic problems in the oil well perforator design. Both Lagrangian and Eulerian methods are used to solve the pertinent stress-strain equations and the shock wave running through the materials. Seven good performance oil well perforators and two conical shaped charges for defeating the reactive armor are included in this book as references. The computer programs written in Fortran for the calculation of high explosive burn time and burn distance, shear modulus and yield strength for many materials, as well as MATLAB plotting programs for many perforators are available online as supplementary materials for the book-- Provided by publisher.
Book
Development of similar materials with different tension-compression ratios and evaluation of TBM excavation
Studying the disturbance patterns of surrounding rock and soil during TBM excavation holds great significance in ensuring the safe construction of tunnels. Geomechanical model testing serves as an effective approach for such research. However, the current model tests suffer from lengthy curing times of rock similar materials and the simplification of tunneling simulation devices. To address these issues, this paper presents an innovative approach by preparing a rock similar material with early strength and good brittleness and utilizing a self-developed scaled TBM testing machine for rock breaking simulations. The conducted tests demonstrate that an increased content of sulphoaluminate cement significantly enhances the compressive-tensile ratio of the rock-like similar materials, thus indicating the excellent performance of the developed materials. The impact zone of TBM tunneling primarily encompasses the surrounding rock within 0.5D in front of the tunnel face and 2D at the rear. The proposed method for similar material preparation and the model test device put forward in this paper can serve as valuable references for similar tunneling experiments.
Journal Article