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This paper focuses on theoretical analytical models to calculate the limit support pressure and vertical earth pressure on the cutting face for tunnels. The failure zone is divided into two parts: a sliding failure zone and an upper loosen zone, and the limit support pressure calculation equation is derived. To verify the rationality of the theoretical model, it was compared with the existing theory, numerical simulation, and centrifugal test, and then the parameter analysis was carried out. The results show that the results of this paper agree well with the existing theory, numerical simulation, and centrifugal test. The inclination angle of the proposed mechanism is determined based on the results of the existing centrifuge test, and the recommended inclination angle is between 52° + u/2 and 54° + u/2. The method is proven to be safe and accurate. It can provide a theoretical basis for similar projects. [ABSTRACT FROM AUTHOR]

X. Li, D. Zhang, and Y. Hou [[21]] monitored ground deformation during the excavation process of shield tunnels in water-rich strata and verified the results using the numerical software FLAC 3D. Thus, secondary grouting of the existing tunnel should be carried out promptly before the tunnel underpass excavation. The first paper, authored by X. Li, Y. Yang, X. Li, and H. Liu [[14]], introduced three shield tunnel construction cases to acquire the main driving parameters for the evaluation of cutting head clogging. The second paper, prepared by J. Du, Q. Fang, J. Wang, and G. Wang [[2]], divided the process of a high-speed train passing through a railway tunnel into three stages, according to the spatial relationship between the train and the tunnel. [Extracted from the article]

Deep-buried tunnels with weak surrounding rock are frequently encountered issues in traffic engineering. It plays an important role in the excavation process and the project operation. This paper applies the theoretical analysis and laboratory test related to four different conditions in terms of their thickness to determine the mechanical response of deep-buried tunnel lining. Then, the energy dissipative structure theory is employed to explain the experimental results. This paper has made the following achievements: firstly, it is found that the toughness of the secondary lining was found to be often the most important indicator of tunnel safety, with better-toughness linings having higher tensile strength and crack resistance. Secondly, it suggests that the inclusion of steel reinforcement in the concrete lining can effectively improve the secondary lining toughness. Finally, it proves that the more ductile liner had more energy, higher load-carrying capacity, and was better able to maintain the overall stability of the structure. [ABSTRACT FROM AUTHOR]

This document is a discussion of a research paper titled "Numerical Study of the Effect of Ground Improvement on Basal Heave Stability for Deep Excavations in Normally Consolidated Clays" by Ari Surya Abdi and Chang-Yu Ou. The authors conducted a three-dimensional finite-element method analysis to investigate the impact of ground improvement on the stability of deep excavations in soft clays. They proposed a modified Terzaghi's method based on their findings. However, the discussion raises concerns about the reliability of the authors' numerical simulation results and suggests the need for validation through field or experimental data. It also questions the use of an elastic-perfectly plastic Mohr-Coulomb model and the absence of mention of dewatering in the original paper. The discussion further examines the displacement patterns and deflection profiles of retaining walls during excavation. The author requests a response from the original authors. [Extracted from the article]

Discontinuity is critical for strength, deformability, and permeability of rock mass. Set information is one of the essential discontinuity characteristics and is usually accessed by orientation grouping. Traditional methods of identifying optimal discontinuity set numbers are usually achieved by clustering validity indexes, which mainly relies on the aggregation and dispersion of clusters and leads to the inaccuracy and instability of evaluation. This paper proposes a new method of Fisher mixed evaluation (FME) to identify optimal group numbers of rock mass discontinuity orientation. In FME, orientation distribution is regarded as the superposition of Fisher mixed distributions. Optimal grouping results are identified by considering the fitting accuracy of Fisher mixed distributions, the probability monopoly and central location significance of independent Fisher centers. A Halley-Expectation-Maximization (EM) algorithm is derived to achieve an automatic fitting of Fisher mixed distribution. Three real rock discontinuity models combined with three orientation clustering algorithms are adopted for discontinuity grouping. Four clustering validity indexes are used to automatically identify optimal group numbers for comparison. The results show that FME is more accurate and robust than the other clustering validity indexes in optimal discontinuity group number identification for different rock models and orientation clustering algorithms. [ABSTRACT FROM AUTHOR]

China, situated in the circum-Pacific seismic belt, experiences frequent seismic activity and faces diverse geological conditions, making structural stability of paramount importance, especially under seismic conditions. The majority of current earthquake generation methods do not consider the nonstationary nature of earthquakes. This paper introduces a spectral representation-random function model for generating nonstationary earthquakes, effectively simulating stochastic seismic ground motion. Furthermore, traditional slope stability analysis methods are deterministic and incapable of providing probabilistic assessments of slope instability. Therefore, this paper proposes a unified framework for static and dynamic structural reliability analysis based on the direct probability integration method, quantifying the impact of stochastic seismic ground motion on the dynamic reliability of slope stability. Finally, the proposed methods are applied to an excavation slope in Nanjing, using sliding displacement and safety factors as evaluation criteria to study the reliability of the slope under the influence of stochastic seismic events. [ABSTRACT FROM AUTHOR]

It is essential to establish a digital twin scene, which helps to depict the dynamically changing geographical environment accurately. Digital twins could improve the refined management level of intelligent tunnel construction; however, research on geographical twin models primarily focuses on modeling and visual description, which has low analysis efficiency. This paper proposes a knowledge-guided intelligent analysis method for the geometric deformation of tunnel excavation profile twins. Firstly, a dynamic data-driven knowledge graph of tunnel excavation twin scenes was constructed to describe tunnel excavation profile twin scenes accurately. Secondly, an intelligent diagnosis algorithm for geometric deformation of tunnel excavation contour twins was designed by knowledge guidance. Thirdly, multiple visual variables were jointly used to support scene fusion visualization of tunnel excavation profile twin scenes. Finally, a case was selected to implement the experimental analysis. The experimental results demonstrate that the method in this article can achieve an accurate description of objects and their relationships in tunnel excavation twin scenes, which supports rapid geometric deformation analysis of the tunnel excavation profile twin. The speed of geometric deformation diagnosis is increased by more than 90% and the cognitive efficiency is improved by 70%. The complexity and difficulty of the deformation analysis operation are reduced, and the diagnostic analysis ability and standardization of the geographic digital twin model are effectively improved. [ABSTRACT FROM AUTHOR]

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details. [ABSTRACT FROM AUTHOR]

This study employs scientometric analysis to investigate the current trajectory of research on tunnel boring machine (TBM) performance and collaborative efforts. Utilizing software tools like Pajek 5.16 and VOSviewer 1.6.18, it scrutinizes literature from 2000 to 2021 sourced from the Web of Science (WOS). The findings illuminate TBM research as an interdisciplinary and intersectoral field attracting increasing national and institutional attention. Notable contributions from China, Iran, the United States, Turkey, and Australia underscore the global significance of TBM research. The recent upsurge in annual publications, primarily driven by Chinese research initiatives, reflects a renewed vigor in TBM exploration. Additionally, the paper presents a succinct evaluation of TBM advantages and drawbacks compared to conventional drill and blast methods, discussing key considerations in excavation methodology selection. Moreover, the study comprehensively reviews TBM performance prediction models, categorizing them into theoretical, empirical, and artificial intelligence-driven approaches. Finally, rooted in metaverse theory, the discourse delves into the immersive learning model and the architecture of a TBM metaverse. In the future, the immersive training and learning model diagram can be employed in scenarios such as employee training and the promotion of safety knowledge. Additionally, the TBM metaverse architecture can simulate, monitor, diagnose, predict, and control the organization, management, and service processes and behaviors of TBMs. This will enhance efficient collaboration across various aspects of the project production cycle. This forward-looking perspective anticipates future trends in TBM technology, emphasizing societal impact and enhancement of economic benefits. [ABSTRACT FROM AUTHOR]

Rockburst is a kind of common geological disaster in deep tunnel engineering. It has the characteristics of causing great harm and occurring at random locations and times. These characteristics seriously affect tunnel construction and threaten the physical and mental health and safety of workers. Therefore, it is of great significance to study the tendency of rockburst in the early stage of tunnel survey, design and construction. At present, there is no unified method and selected parameters for rockburst prediction. In view of the large difference of different rockburst criteria and the imbalance of rockburst database categories, this paper presents a two-step rockburst prediction method based on multiple factors and the stacking ensemble algorithm. Considering the influence of rock physical and mechanical parameters, tunnel face conditions and excavation disturbance, multiple rockburst criteria are predicted by integrating multiple machine learning algorithms. A combined prediction model of rockburst criteria is established, and the results of each rockburst criterion index are weighted and combined, with the weight updated using the field rockburst record. The dynamic weight is combined with the cloud model to comprehensively evaluate the regional rockburst risk. Field results from applying the model in the Grand Canyon tunnel show that the rockburst prediction method proposed in this paper has better applicability and higher accuracy than the single rockburst criterion. [ABSTRACT FROM AUTHOR]

In order to study the influence of excavation sequence of three-hole parallel shield tunnel on surface settlement and segment convergence, an improved "three-stage analysis method" was proposed to calculate the surface settlement of three-hole parallel shield tunnel. Based on Peck's existing theory, this method deduced the ground settlement formula under the three-hole parallel condition, and can calculate the ground settlement more accurately. Based on the engineering background of a shield tunnel section in Jiangsu Province, a three-dimensional model of a three-hole parallel shield tunnel was established by using Flac3d software, and the three-hole parallel shield tunnel was simulated under four working conditions: right–center–left, right–left–center, right–left–right and right–center (reverse)–left. This paper analyzed the influence of tunnel excavation sequence on surface settlement, soil displacement and deformation of tunnel segments. The construction sequence was optimized based on the above influencing factors. It was found that Case 4, S-type sequential excavation, produced the least ground settlement. The surface settlement value caused by S-type excavation sequence was only 11.41 mm, and the convergence value of the segments generated by S-type excavation sequence was relatively small. Considering the economic factors such as construction efficiency and benefit, the S-shaped excavation sequence was better. The new calculation method of tunnel surface settlement and the optimal sequence of tunnel construction proposed in this paper can provide reference for actual construction. [ABSTRACT FROM AUTHOR]

Rockburst is a phenomenon inwhich free surfaces are formed during excavation, which subsequently causes the sudden release of energy in the construction of mines and tunnels. Light rockburst only peels off rock slices without ejection, while severe rockburst causes casualties and property loss. The frequency and degree of rockburst damage increases with the excavation depth. Moreover, rockburst is the leading engineering geological hazard in the excavation process, and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering. Therefore, the prediction of rockburst intensity grade is one problem that needs to be solved urgently. By comprehensively considering the occurrence mechanism of rockburst, this paper selects the stress index (σθ/σc), brittleness index (σc/σt), and rock elastic energy index (Wet) as the rockburst evaluation indexes through the Spearman coefficient method. This overcomes the low accuracy problem of a single evaluation index prediction method. Following this, the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed. The batch gradient descent (BGD) module is used to replace the gradient descent algorithm, which effectively improves the efficiency of the network and reduces the model training time. Moreover, the multi-scale residual (MSR) module solves the problem of network degradation when there are too many hidden layers of the deep neural network (DNN), thus improving the model prediction accuracy. The experimental results reveal the BGDMSR-DNNmodel accuracy to reach 97.1%, outperforming other comparable models. Finally, actual projects such as Qinling Tunnel and Daxiangling Tunnel, reached an accuracy of 100%. The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade. [ABSTRACT FROM AUTHOR]