Tunnelling and Underground Space Technology最新文献

In water conservancy, transportation, and mining projects, the timely acquisition of geological structural information from tunnels is critical in the analysis of engineering geological problems during the investigation and construction stages. The acquisition of comprehensive and accurate geological information from a tunnel surface remains challenging. This study provides an automatic extraction method for geological discontinuities on a tunnel surface by integrating 2D textural semantic features and 3D geological semantic features. A dense point cloud is generated using multiline parallel sequence images, after which the 3D geological semantic features, including the local geological attitude, are calculated. Through a virtual projection from 3D to 2D, the red, green, and blue (RGB) images and geological semantic images based on views of the interior umbrella arch and the sidewalls of the tunnel surface are obtained. The feature mapping between the 2D textural semantic features and the 3D geological semantic features is determined accordingly. The virtual RGB images and geological semantic images serve as dual inputs for ensemble learning for pixel block segmentation, and the output is a similarity probability tensor that describes the probability that each pixel will belong to its surrounding pixel blocks. The pixel blocks are clustered on the basis of pole and contour plots of their geological attitudes to extract geological discontinuities. Experiments were conducted to confirm and evaluate the feasibility and veracity of the proposed method. The developed method automatically extracts geological discontinuities of a tunnel surface and extends the scope of surveying and mapping through geological remote sensing.

The increasing frequency of structural damage and reinforcement repairs in long-term highway tunnels necessitates an understanding of their effects on drivers. This study examines drivers’ physiological responses to visible structural changes in highway tunnels. Using a vehicle static in-the-loop platform, we created various models of apparent tunnel structure changes for simulated driving experiments. These experiments enabled a detailed analysis of the effects of such changes on driver safety, utilizing metrics such as eye movements, regions of interest, heart rate, and vehicle speed. The results show that visible alterations in tunnel structures significantly affect drivers’ physiological responses. Structural spalling and fire residues within tunnel structures notably increased drivers’ vigilance and psychological stress, resulting in a 14.7% increase in the average number of fixations, a 26.35% increase in the average duration of fixations, and a 36.05% increase in heart rate variability. Additionally, tunnel spalling tends to cause drivers to accelerate or exceed the speed limit, with maximum speeds reaching 17.87% above the designed speed. In contrast, repairs involving cover arch erection had minimal impact on drivers, with eye movement and heart rate data similar to those in ordinary tunnels. However, reinforcement with steel strips and corrugated steel in tunnels has attracted significant attention, with the area of interest exceeding 50% of the tunnel area, potentially leading to distracted driving. This study clarifies the extent of the influence of visible tunnel structure changes on drivers, providing a reference for damage assessment, reinforcement, and repair measures for long-term operated tunnels.

Concealment of filling constructions poses significant challenges for quality assurance in filling engineering. Direct surveillance of fill dispersal currently remains infeasible, while conventional detection techniques suffer deficiencies in efficiency. This research proposes a framework integrating elastic wave monitoring and hybrid deep learning for predictive modelling of filling state transitions and diffusion range. During the sand filling of the immersed tunnel, elastic wave data is collected via elastic wave testing, and the response energy characteristic is derived through time-domain analysis. The trends in elastic wave response energy are correlated with three filling states: free diffusion, accumulation, and filled state, using Seasonal and Trend decomposition using Loess (STL) for seasonal trend analysis. Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) are utilized to extract spatiotemporal features from the response energy trends, facilitating accurate prediction of the trends’ development and the sand filling state over time. The performances of the proposed strategy are illustrated through an application to the case study of the sand filling construction of the Chebeilu immersed tunnel. The CNN + LSTM model with the proposed strategy gave excellent results (MAE 0.0663, MSE 0.0071, RMSE 0.0845). The model can predict fill state changes and quantify diffusion radii to optimize and guide the construction process.

This study introduces an innovative Roughness-CANUPO-Dip-Facet (R-C-D-F) methodology for the measurement of dip angle and direction in geological rock facets. The R-C-D-F method is distinguished by its comprehensive four-step approach, encompassing filtration through roughness analysis, CANUPO analysis, and dip angle filtration, followed by facet segmentation as the measurement step. To achieve precise and efficient results, the method specifically focuses on isolating joint embedment, achieved by systematically filtering out joint bands. This selective filtration process ensures that measurements are conducted exclusively on relevant joint embedment points. The novelty of this methodology lies in its capability to automatically eliminate joint bands while retaining the joint embedment points, facilitating precise measurements without manual intervention. Three site models were evaluated using the R-C-D-F method, alongside four different techniques for measuring dip angle and direction: plane fitting, normal vector conversion, facet segmentation, and compass measurements. The results demonstrated that all methods accurately calculated the dip angle, with an accuracy ranging from 97 % to 99.4 %. The facet segmentation method was selected as the optimal measurement tool due to its automatic nature and capacity to provide accurate results without manual intervention. Furthermore, the optimal local neighbour radius (LNR) for calculating normal vectors was determined, with findings indicating that a larger LNR value enhances accuracy but also increases computational time. A verification was conducted to estimate the dip angle used for filtering and discarding additional points representing joint rock bands, with the optimal value being 45, 30, and 45 degrees for the respective sites.

The R-C-D-F method effectively detected and eliminated 100 % of joint band points while retaining 81 % of joint embedment points, and the facet segmentation method provided accurate dip angle and direction measurements for each joint embedment segment. These outcomes underscore the robustness and precision of the R-C-D-F method in geological engineering and rock stability studies.

The longitudinal equivalent bending stiffness (LEBS) typically exhibits nonlinearity under the coupling of multiple environmental and construction loads. In this research, we propose the Circumferential Joint Fiber Section Model (CJFM) to analyze the longitudinal bending behavior affected by critical factors such as geometric, material, and contact nonlinearities, and the influence range of the circumferential joint. The performance of CJFM is then verified via classical analytical solutions and laboratory model experiments. Furthermore, the applicability of the proposed model is further confirmed based on the Zhanjiang Bay undersea tunnel in China. The results show that the CJFM accurately simulates the full evolution process of seven modes and employs different constitutive models. The longitudinal stress of typical section in Zhanjiang Bay undersea tunnel reveals a distinct pattern of alternating tension and compression from top to bottom, with a noticeable temporal variation of five stages. Utilizing the CJFM, a safety partition of bending mode is constructed, ranging from healthy to unsafe. Upon the estimated bending moment and axial force, the safety status of the test ring is consistently evaluated to be in normal service, and it is inferred that the subsequent state in future will maintained in normal service under similar circumstances.

Radon release during underground engineering excavation is mainly from the newly generated fracture surfaces of the rock mass rupture, and the accurate prediction of radon release depends on the quantitative characterization of the rock mass rupture. To examine the correlation between radon release and rock mass rupture, a series of triaxial compression radon release tests were carried out and the effective radon exhalation rate (J_eff) was defined, a linear function of rock fracture area and radon release was developed. Based on the continuous-discontinuous element method (CDEM), a quantitative equation between rock fracture degree (D) and radon release by numerical model for triaxial compression tests was obtained. Subsequently, a synthetic rock mass (SRM) method combining discrete fracture network (DFN) model and CDEM was used to analyze the scale dependence of radon release from rock mass rupture, and the representative element volume (REV) size of the radon release from rock mass rupture was determined. Radon release increases exponentially with increasing sample size. Radon release increases and then decreases with the increase of joint dip angle, however, radon release increases with the increase of joint trace length amplification factor. Additionally, an underground powerhouse excavation model to derive the evolution of radon release from the surrounding rock with the number of excavation layers was established. The results of this research can provide a basis for radon pollution control during underground engineering excavation.

To reveal the strengthening effect of the CFRP method on the fire-damaged segment. In this study, the fire-damaged segment was strengthened with the CFRP-PCM method and CFRP-sheet method, respectively. The crack development, deformation characteristics, failure mode, load-bearing behaviour and internal force evolution of the segment specimens were analysed. The results indicated that the CFRP-PCM method can improve the crack development of the segment, while the CFRP-sheet method can only prevent the crack initiation of the segment before tearing between the CFRP sheet and the segment. The fire-damaged segment strengthened by the CFRP-PCM method exhibited gradual deformation and failure characteristics, whereas the fire-damaged segment strengthened by the CFRP-sheet method exhibited sudden failure with no discernible damage characteristics. The CFRP-sheet method had a higher initial strengthening effect on the fire-damaged segment than the CFRP-PCM method before tearing between the CFRP sheet and the segment, while the strength utilisation rate of the CFRP grid was higher than that of the CFRP sheet. The CFRP method of strengthening the fire-damaged segment can improve the tensile strength of the tension zone, increase the sectional stiffness, and restore the load-bearing capacity; however, this comes at the expense of the deformation capacity. The study provides a reference value for the strengthening design of fire-damaged linings in shield tunnels.

For the stability analysis of the surrounding rock mass of an underground powerhouse, reliable mechanical parameters of the rock mass and appropriate analysis methods are highly important. This paper discusses the rationality of using the mechanical parameters of the representative volume element (RVE) of a jointed rock mass as equivalent mechanical parameters and using the equivalent continuity method to simulate the excavation of a large underground powerhouse in a jointed rock mass, with the underground powerhouse of the Wuyue pumped storage power station as an example. Initially, the discrete fracture network (DFN) and synthetic rock mass (SRM) of the jointed rock mass were established. The size of the RVE of the rock mass was determined to be 23 m × 23 m × 23 m through numerical tests. The mechanical parameters of the RVE were used as the equivalent mechanical parameters of the rock mass. Then, the two-dimensional numerical calculation of the excavation of the main powerhouse was carried out using the equivalent continuous method and the discontinuous method. The mean relative error between the deformation of the surrounding rock calculated by the two methods is 8.24 %, which shows that the equivalent continuous method can calculate the overall deformation after excavation of a large underground powerhouse in a jointed rock mass. Furthermore, the three-dimensional equivalent continuous numerical calculation of underground powerhouse excavation is carried out by using the equivalent mechanical parameters determined by the RVE and Hoek–Brown criterion. Compared with the actual measurement results of the multipoint displacement meter, the mean relative error of the calculation result based on the RVE is 12.37 %, and the mean relative error of the calculation result using the Hoek–Brown criterion is 20.37 %, indicating that the numerical calculation using the mechanical parameters of the RVE of the jointed rock mass as equivalent mechanical parameters can consider the size effect of the jointed rock mass and reduce the error of the numerical calculation. Our results are expected to provide guidance for evaluating the stability of an underground powerhouse.

The burgeoning demand for land resources in cities has spurred the development of intricate underground infrastructure networks. Therefore, evaluating the performance and stability of entire existing underground complexes in the event of construction-related disturbances becomes increasingly crucial for ensuring overall urban resilience. This paper adopts a coupled modeling technique of the Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH) for the analysis of catastrophic failure mechanisms in underground complexes, with a focus on disturbances due to nearby tunneling. Two-dimensional centrifuge model experiments were used to thoroughly calibrate the coupled FEM-SPH method, validating its accuracy and suitability for the simulation of soil–structure interaction problems. Subsequently, a full-scale integrated model of the underground complex and formation in a given area was established, including the subway network and highway system. Taking into account the longitudinal connections of the tunnel segments, the catastrophic coupling mechanisms related to construction disturbances in deep tunneling were investigated. The results indicate that collapsed soil caused by deep construction disturbances spreads through the gaps between the tunnels towards the ground, acting as a force-transmitting medium to correlate the deformations of the different structures. The response of the structures was evaluated using five different patterns of deformation, including tunnel settlement, dislocation, opening, rotation, and ovalization. In addition, the evolution of the performance of the underground complex during construction disturbances was analyzed using three types of indicators. Finally, the assessment of the catastrophic failure degree and the identification of vulnerable areas within the complex were carried out.

Underground pedestrian networks (UPNs), serving as a comprehensive approach for utilising urban underground spaces to address connectivity and spatial challenges, have undergone significant development in numerous major city centres. Well-designed UPNs are pivotal in developing compact cities, bolstering urban resilience, enhancing urban transportation systems, fostering urban renewal, and facilitating sustainable urban development. This study explores the construction methodology of UPNs by utilising the source-sink theory and relevant models. Using Xinjiekou District in Nanjing City as a case study, this study employs the minimum cumulative resistance model to simulate a UPN and conduct analysis of pedestrian flow clustering on the simulation results. The findings demonstrate that our proposed method effectively elucidates and predicts the connectivity of UPNs, enabling us to construct an optimal network with minimal cost. Moreover, the simulation network significantly enhances the optimisation of pedestrian flow distribution. Building upon these simulation results, the UPN in the study area is optimised by integrating real-world conditions and conducting degree centrality analysis. The findings are then utilised to propose policy recommendations for UPN layout planning in urban centres. This study serves as a valuable reference for underground space planning in the study area and provides insights for UPN construction in other urban centres.