Tunnelling and Underground Space Technology最新文献

Cavities behind the concrete lining of a shield tunnel may result in apparent damage or even collapse of the tunnel during its operation. It is necessary to predict the damage modes of a shield tunnel with cavities, and accordingly reinforce vulnerable areas of the tunnel. This paper investigates the damage modes of shield-tunnel models with cavities at different locations and sizes behind the concrete lining. The tunnel models used in the test are created using a 3D printing technique, with an aim of simulating the joints between segments. To consider the stratum-structure interaction, the tunnel models are created with grout-layers prefabricated between lining and soil. The 3D point cloud technique is then applied to observe the damage modes of the tunnel linings. The safety status of the shield tunnel is evaluated during the loading process, and categorized into safe, dangerous, and failure stages. Experimental results show that the damage modes of the shield tunnel with cavities contain concrete crack, concrete spalling, segment misalignment, and lining crush. Cavities at the tunnel crown and shoulder impose a substantial impact on the lining structure. Cracks propagating across three or more segments result in mutual compression between segments, forming a crack mesh, and consequently leading to concrete spalling. The tunnel lining undergoes a failure mode of segment misalignment when the cavity angle (size) is greater than 45°. As the volume of the cavity increases, the tunnel lining transitions to a failure mode of lining crush. The results in this study will facilitate the proactive reinforcement of the tunnel by predicting damage modes induced by cavities, ensuring its safe operation to a certain extent.

The rapid expansion of urban underground space (UUS) has become increasingly popular in densely populated urban areas worldwide. Although data-driven technology has facilitated the planning process successfully, the implementation mechanism of UUS planning remains obscure, potentially undermining the spatial performance. To address this gap, this study employs fuzzy-set qualitative comparative analysis (fsQCA) to analyze the causality of UUS development. Three primary models of UUS development-separate, interconnected, and integrated models-are categorized. Causal conditions and outcomes along with their quantitative metrics are proposed. Subsequently, 30 Chinese cases are analyzed using fsQCA to assess the necessity and sufficiency of UUS development. Three distinct causal paths are identified, namely strong economic strength, robust policy support, and advanced construction technology, which play critical roles in integrated development, while weak economic strength and inadequate policy support lead to separate development. The study underscores the importance of implementing UUS development models and provides valuable insights for UUS planning management.

This study investigates the enhancement of disposal efficiency for deep geological repositories (DGRs) based on three design factors: decay heat optimization, increased thermal limit of the buffer, and double-layer concept using coupled thermo-hydro-mechanical (THM) numerical simulations. Decay heat optimization is achieved by iteratively emplacing spent nuclear fuels having the maximum and minimum decay heat in a canister. Disposal areas can be reduced by 20 % to 40 % compared to the current reference disposal system in Korea (KRS⁺) in accordance with the combinations of the three design factors, alleviating challenges in site selection for the DGR. This study additionally identifies an optimal layer spacing of 500 m for the double-layer concept in the viewpoint of the buffer temperature, where thermal interaction between the upper and lower layers nearly disappears. However, determining the ultimate disposal and layer spacing requires engineering judgement, considering not only the thermal performance of the DGR but also various factors such as cost and difficulties of the construction and rock mass stability. DGRs designed with an increased thermal limit of the buffer poses a greater probability of rock mass failure around disposal tunnels and deposition holes due to elevated thermal stresses. Densely arranged heat sources for the DGRs with enhanced disposal efficiency lead to larger temperature increase even at the far-field scale, raising a possibility of thermally driven fracture shear activation with associated hydraulic, mechanical, and seismic changes.

Detecting water leakage is vital for assessing tunnel structure operational conditions. Currently, deep learning (DL) based methods for leakage detection have shown promising results. However, their robustness in complex backgrounds remains limited due to challenges in extracting essential features from leakage areas. To tackle this issue, a novel detection model for water leakage is proposed, based upon feature enhancement learning. The model adopts Mask R-CNN as its core framework and seeks to enhance detection performance through three strategies as follows. Firstly, using the brightness aggregation of leakage pixels, Otsu method is initially used to segment leakage pixels. The segmented outcome is employed alongside the original image for network input, which can offer guided training to the recognition network and enhance its ability to separate leakage from backgrounds effectively. Secondly, considering the perception difference across feature extraction layers in DL networks, Non-Local Block is integrated into low-level networks, correlating leakage areas and global pixels. Additionally, Squeeze-and-Excitation Block is proposed to amplify channel weights for leakage in high-level networks, augmenting its ability to perceive crucial characteristics within leakage regions. Thirdly, addressing the issue of insufficient leakage boundary feature perception by unidirectional pyramids in existing networks, we present a Bidirectional Feature Pyramid Network. Besides, this proposed model applies one distinct inter-layer feature fusion based on the pyramid’s direction. The algorithm’s performance is evaluated using a tunnel leakage dataset. Through conducting ablation experiments, it was verified that the proposed model consistently outperforms other comparison algorithms in leakage detection accuracy.

Indoor moisture is a crucial aspect of the environment in naturally ventilated underground shelters that remain unattended for long periods of time. The objective of this study was to investigate the impact of natural ventilation on the indoor temperature and humidity of underground shelters using a multi-zone airflow simulation and thereby evaluate the ability of natural ventilation to maintain the indoor environment of shelters in various climates in China. Firstly, a multi-zone airflow model was coupled with the dynamic heat transfer in an underground shelter to simulate the natural ventilation rate and indoor environment. The accuracy of this coupled simulation was subsequently validated using the data measured at an actual shelter. Next, the verified simulation method was applied to analyse the seasonal fluctuations in the natural ventilation rate of underground shelters in the various climates in China; these results were applied to determine the corresponding annual variations in the indoor temperature and humidity. The analysed results show that the relationship between the ventilation rate and indoor humidity level is dependent on both season and climate, and that the influence of the ventilation rate can be negative during summer, especially in wet climate zone. Finally, these findings were evaluated to provide guidelines for regulating natural ventilation according to climate, informing the management of unattended shelters in China.

Shield tunneling during metro construction frequently hits steel reinforced concrete diaphragm walls. A high risk is involved in using a shield machine to cut through a diaphragm wall owing to limited theoretical research and engineering experience. To evaluate the performance of the shield machine and obtain feasible operation parameters, this study conducts a full-scale field test using a shield machine to cut through a diaphragm wall in Suzhou, China. The machine has a diameter of 6.84 m, equipped with 38 disc cutters and 48 scrapers. The wall has a width of 8.8 m, height of 9.7 m, and thickness of 0.6 m, reinforced with 25 mm and 28 mm diameter main steel rebars. The study finds: 1) the shield machine has adequate capacity to cut the diaphragm wall with small wear (maximum amplitude of 0.7 mm) to disc cutters and light damage to scrapers; 2) the concrete is mainly damaged under compression shear and peel-off with 95 % of particle size smaller than 10 cm, whereas the steel rebars are broken under the combined effects of compression shear and pull-apart with five damage modes identified based on different damage mechanism; and 3) a low advance rate (1–2 mm/min) and medium rotational speed (0.6 rpm) are recommended such that the machine can cut the wall smoothly with wall acceleration below 0.15 g and maintains its thrust and toque close to 10 % and 15 % of rated thrust and torque, respectively. The findings prove the feasibility of using a shield machine to cut through a diaphragm wall, and provide guidance for project implementation.

An approach based on a Physics-Informed Neural Network (PINN) is introduced to tackle the two-dimensional (2D) rheological consolidation problem in the soil surrounding twin tunnels with different cross-sections, under exponentially time-growing drainage boundary. The rheological properties of the soil are modelled using a generalized viscoelastic Voigt model. An enhanced PINN-based solution is proposed to overcome the limitation of traditional PINNs in solving integral–differential equations (IDEs) equations. In particular, two key elements are introduced. First, a normalization method is employed for the spatio-temporal coordinates, to convert the IDEs governing the consolidation problem into conditions characterized by unit-duration time and unit-area geometric domain. Second, a conversion method for integral operators containing function derivatives is devised to further transform the IDEs into a set of second-order constant-coefficient homogeneous linear partial differential equations (PDEs). By using the TensorFlow framework, a series of PINN-based models is developed, incorporating the residual adaptive sampling method to address the 2D consolidation equations of soft soils surrounding tunnels with different burial depths and cross-sections. Comparative analyses between the PINN-based solutions, and either finite element or analytical solutions highlight that the aforementioned normalization stage empowers PINNs to solve the PDEs across different spatial and temporal scales. The integral operator transformation method facilitates the utilization of PINNs for solving intricate IDEs.

The utilization of spatial curve shield tunnelling has now emerged as the prevailing approach in shield engineering, giving rise to a more intricate ground influence. This paper establishes a three-dimensional computational model based on the elastic pass solution and integrates existing research to derive a comprehensive analytical solution for the additional stresses induced in the ground during shield tunnel construction under complex spatial curve conditions formed by connecting two simple line segments. The theoretical approach proposed in this study is subsequently validated through the utilization of a sophisticated numerical model and the approximate solution derived from previous research. Based on the yaw tunneling phenomenon of a shield in spatial curves, this paper independently analyzes the additional stress field of the soil by incorporating relevant yaw parameters and ultimately derives a series of fundamental principles for shield tunneling in space curves. The findings presented in this paper have significant implications for a wide range of shield tunneling projects.

During slurry pressure-balance (SPB) shield tunneling in a sand layer, the excavated sand mixes with bentonite slurry. Herein, the pressure infiltration of the saturated sand by the bentonite–sand slurry is modeled to describe the slurry and companion-water flows during the mud spurt period. A maximum infiltration-depth prediction model is also implemented, based on the principles of particle deep-bed filtration. The model closely matches experimental results for a slurry with a low sand-to-bentonite mass ratio, but tends to overestimate the infiltration distance as the mass ratio increases. Furthermore, the prediction model reflects the continuous infiltration phenomenon associated with elevated sand concentrations in the slurry. When combined with the maximum-depth prediction model, the infiltration model strongly aligns with the experimental findings for the mud spurt period. The combined model effectively explains the bentonite–sand slurry infiltration mechanism and accurately predicts mud-spurt occurrences during SPB shield tunneling in sandy layers.

An in-situ heater experiment, known as ALC1605, is being carried out as a part of the research and demonstration program of the Cigéo project, the deep geological disposal facility for intermediate-level long-lived and high-level radioactive waste in France. This experiment is conducted in the Meuse/Haute-Marne underground research laboratory located at a depth of 490 m within the Callovo-Oxfordian claystone (COx) layer.

The ALC1605 experiment consists of a 28.5 m long horizontal steel-cased micro-tunnel. The heat emitted by the high-level waste (HLW) packages is mimicked by heater devices installed between 10 m and 25 m. This is the second full-scale heater prototype of the HLW disposal cell concept. Unlike the first full-scale prototype, the annular gap between the steel sleeve and the rock formation is filled with an alkaline grout material to reduce the corrosion of the sleeve.

The main objectives of this experiment are to study: (i) the thermomechanical behavior of the steel sleeve, and (ii) the thermo-hydro-mechanical (THM) behavior of the rock, with a particular attention to the effect of the backfill material by comparing the experimental results with its predecessor.

At this stage, the temperature in the sleeve has reached a steady state with a maximum value of 85 °C. The backfill material modifies the loading/ovalization process of the sleeve compared to the experiment without backfill material, resulting in vertical convergence and horizontal divergence. Furthermore, analyses of the two anisotropy planes were carried out before and after the heating to study the intrinsic anisotropy of the COx behavior. These analyses show that the pore pressure distribution due to the micro-tunnel excavation plays a non-negligible role in the thermally induced pore pressure, accentuating the anisotropic THM response of the COx.