In this study, we developed a three-dimensional (3D) location estimation and tunnel mapping system to locate an autonomous robot in the rampway of an underground mine using 3D point cloud registration. A 3D point cloud of the mine tunnel was measured using a 3D light detection and ranging (LiDAR) sensor and registered using the iterative closest point (ICP) algorithm to estimate the 3D pose of the robot. This was combined with two-dimensional LiDAR, inertial measurement unit, and encoder sensors to estimate the 3D trajectory of the robot. Additionally, the 3D tunnel mapping was performed using the 3D trajectory of the robot and the 3D point cloud data of the tunnel. A comparison of the tunnel maps created using conventional surveying equipment and the robot indicated a mapping error of 0.2275 m and localization error of 0.2465 m confirming the excellent overall tunnel mapping and localization performance. The tunnel mapping areas were further compared by selecting areas with relatively high and low ICP matching accuracies; the calculated errors were 0.6186 and 0.2257 m in the areas with low and high accuracies, respectively. Furthermore, the accuracy of the ICP matching tended to be low in areas where the change in the pitch angle of the robot was large.

Deep geothermal reservoirs could provide widespread access to clean and renewable energy around the world. However, hydraulic stimulation of these reservoirs to create sufficient injectivity and heat extraction has frequently induced earthquakes during and, in particular, after reservoir stimulation, which raises public concerns. This study aims to provide a possible explanation for post-injection seismicity and understand how it responds to well bleed-off as a common industrial practice to control such seismic activity. To this end, we perform coupled hydromechanical simulations of reservoir stimulation in a conceptual model comprising a deep granitic reservoir intersected by a network of long fractures and a nearby, critically-stressed fault. We find a combination of mechanisms triggering post-injection seismicity with time delays of several months after stopping injection: (1) poroelastic stressing that transmits normal and shear stress and causes undrained pressure buildup on the fault, (2) fracture-dominated pore pressure migration toward the fault, and (3) long-lasting along-the-fault pressure diffusion toward pre-stressed fault patches, promoted by dilation-induced fault permeability changes. In this setting, bleed-off causes rapid pressure decline in the near-wellbore region but marginal pressure changes farther away. The resulting attenuations of pore pressure and shear stress on the fault plane may not be enough to prevent fault reactivation. Bleed-off may counterintuitively accelerate fault slip by rapid relaxation of normal stress on the fault, which not only brings the stress state closer to failure conditions, but also accelerates pore pressure diffusion along the fault by slightly increasing its permeability. We show that bleed-off can effectively control post-injection seismicity only if rupture initiates from a structure in close proximity and with sufficient hydraulic connection to the wellbore. Future research should be directed toward the optimization of stimulation and post-stimulation design in light of the involved triggering mechanisms and through effective combination with subsurface characterization to control post-injection seismicity.

Face stability is one of the essential problems in shield tunneling. When tunneling in cobble stratum or mixed face ground conditions, significant cutting-induced cutterhead vibration would occur and affect the face stability. To reveal the mechanism and effect of vibration on the tunnel face stability, a transparent tunnel model with a movable vibration exciter was designed and a series of model tests were performed under different vibration magnitudes A_a and frequencies f. Meanwhile, particle image velocimetry was used to reveal the displacement field and the failure pattern of the tunnel face. The test results indicate that the cutting-induced vibration produces a significant reduction effect on the tunnel face stability, as expressed by the increase of the face support pressure and the failure zone when the vibration magnitude and frequency increase. Compared with the static unloading conditions, the width of the failure wedge L_wt increased by about 5.75% and 35.66% for the loose and dense sand, respectively, under dynamic unloading conditions (A_a = 0.2g, f = 10 Hz). The limit support pressure increased up to about 0.20γD at a vibration of 0.3g and 50 Hz, much larger than those of static conditions, which were about 0.08γD-0.09γD. An observable self-stabilizing arch can be formed in dense sand under static unloading conditions, while under dynamic unloading conditions, the long-time stable soil arch would not occur. The contributions of this paper could provide an insightful understanding of the effects of cutterhead vibration on tunnel face stability.

To comply with the requirements of sustainable energy development, China has proposed the strategic goal of achieving dual carbon. Systematic and scientific development and utilization of urban underground space will provide critical support for reducing carbon emissions and enhancing carbon sink capacity. This paper examines the transmission and distribution ring pit project of Fuzhou Binhai New City, China, divided into four regions, where the selection of the support system is determined by the project’s characteristics. Stability is analyzed using in-situ monitoring data from the R4 area, and the deformation of the support system is predicted using machine learning. The predicted maximum lateral deformation of the support system may reach the warning value, necessitating corrections to the existing support parameters. On this basis, the deformation during foundation pit excavation is simulated, and the effects of key factors such as pile geometric parameters, pile penetration depth, and anchor cable insertion ratio on the deformation are analyzed. The study shows that pile deformation control is optimal when the support parameters include a 1.3 insertion ratio, a 20° anchor cable angle, and a 200 kN prestressing force, enabling the construction of the remaining three areas. This study can serve as a valuable reference for the design and analysis of deep foundation pits under special stratigraphic conditions in coastal areas.

Recently, AI-based models have been applied to accurately estimate tunnel boring machine (TBM) energy consumption. Although data-driven models exhibit strong predictive capabilities, their outputs derived from “black box” processes are challenging to interpret and generalize. Consequently, this study develops an XGB_MOFS model that cooperates extreme gradient boosting (XGBoost) and multi-objective feature selection (MOFS) to improve the accuracy and explainability of energy consumption prediction. The XGB_MOFS model includes: (1) a causal inference framework to identify the causal relationships among influential factors, and (2) a MOFS approach to balance predictive performance and explainability. Two case studies are carried out to verify the proposed method. Results show that XGB_MOFS achieves a high degree of accuracy and robustness in energy consumption prediction. The XGB_MOFS model, balancing accuracy with explainability, serves as an effective and feasible tool for regulating TBM energy consumption.

Current semantic segmentation models have limitations in addressing tunnel lining crack, such as high complexity, misidentification, or inability to detect tiny cracks in specific practical scenarios, which is crucial for precise assessment of tunnel lining health. We developed a novel approach called EDeepLab, aiming to achieve a higher precision detection and segmentation of lining surface crack. EDeepLab improves upon the original DeepLabV3+ framework by replacing its backbone network with an optimized lightweight EfficientNetV2. The amount of EfficientNetV2 block computation is reduced and a self-designed shallow feature fusion module is used to merge the layers to enhance parameter utilization efficiency. Furthermore, the normalization-based attention module and convolutional block attention module attention mechanisms are integrated to classify and process both high and low dimensional information features. This allows for comprehensive utilization of global semantic information and channel information, thereby enhancing the model’s feature extraction capability. Results in constructed metro-tunnel crack dataset demonstrate that the number of parameters is reduced from 144.45 M in the DeepLabV3+ to 99.80 M in the EDeepLab. EDeepLab achieves a mean intersection over union of 84.77%, mean pixel accuracy of 94.96%, and frames per second of 18.52 f/s. The proposed EDeepLab outperforms other models including U-Net, ResNet and fully convolutional networks in the quantitative analysis of tiny cracks and noise interference.

Structural damages during an earthquake are typically controlled by seismic demands, which are represented by the combination of amplitude of ground motion and cyclic load effects. Since traditional methods normally assume the lognormal distributions of seismic demands and resistance parameters, uncertainties are inevitably induced in the seismic fragility analysis. In this paper, the Copula function and adaptive bandwidth kernel density estimation method (ABKDE) are used to establish a novel multidimensional seismic fragility analysis framework. Based on the results of incremental dynamic analysis for subway station structures, ABKDE is adopted to establish single-parameter seismic fragility curves for both the maximum inter-story drift ratio (MIDR) and cumulated dissipated hysteretic energy (CDHE), respectively. Subsequently, the Copula function is used to formulate a bivariate seismic fragility function considering the correlations among seismic demand measures and establish the corresponding fragility curves. Finally, comparative analyses are conducted to evaluate seismic fragility curves using Copula-based dual and single-parameter damage models as well as the traditional damage models. It is found that the seismic fragility analysis method using the Copula function has the ability to gain a comprehensive consideration of the MIDR and CDHE during the damage process of subway station structures. Moreover, this newly developed seismic fragility analysis framework can capture the influence of the correlation between deformation and energy under various peak ground accelerations on structural damage. Thus, this framework can provide a scientific basis for predicting structural damage in subway stations subjected to varying intensities of ground motion while considering multiple damage indicators.

The tunnel pavement is generally made of asphalt or concrete. Due to the relatively fixed material of pavement, the effect of tunnel pavement setting on the lighting environment and visual performance of drivers has not received sufficient attention, especially the impact on the visual performance of drivers during driving has not been revealed. Therefore, experimental research on the visual recognition performance of an obstacle on asphalt and concrete pavements inside tunnels during dynamic driving was conducted in this study. The results indicate that under the same pavement illumination, the luminance on concrete pavement is higher than that on asphalt pavement due to the higher reflectance of concrete. The visible distance of the human eyes for a gray obstacle with a reflectance of 0.2 on the concrete pavement is greater than that on the asphalt pavement, and the visible distance of the obstacle on the concrete pavement increases by more than 28%. When the color of the obstacle and the pavement are close, it can be challenging for observers to recognize the obstacle, and the pavement and obstacle need to have a higher level of luminance for the recognition. During dynamic driving, the visible distance at a speed of 60 km/h is 1.2 to 1.4 times that at a speed of 80 km/h, which means the influence of vehicle speed on the human eye’s recognition of obstacles on asphalt and concrete pavements should be taken into consideration in the design of road tunnel lighting. The correlated color temperature and S/P value of LED light have little impact on the visible distance of human eyes to the obstacle on the asphalt and concrete pavements, but it does create different visual perceptions. As the correlated color temperature and S/P value increase, the lighting environment of the tunnel gradually gives a brighter feeling to drivers.

Shield tunneling in urban underground space necessitates tight control over support pressure at the tunnel face and a thorough insight into ground collapse mechanisms. This study conducts a model test and a theoretical validation to clarify the mechanisms of face failure and subsequent ground collapse in sand during earth pressure balanced shield (EPBS) tunneling operations. The experiment investigates the changes in soil pressure and surface subsidence patterns during shield tunneling and collapse stages, to elucidate the entire process of ground collapse triggered by shield tunneling disturbances. A novel methodology was proposed to ensure effective verification of the rotational failure mechanism, focusing on the collapse pit morphology and the critical collapse pressure. The results indicate that: (1) precise control over the shield tunneling and screw conveyor rotation speeds is essential for tunnel face stability; (2) the sand with low moisture content is prone to stepwise ground collapse under shield tunneling disturbances; (3) soil pressure measurements at the cutterhead are more indicative of face failure and imminent ground collapse than those from the soil chamber; (4) there is a consistent alignment between the rotational failure mechanism and observed collapse pit morphology, albeit with slight variations due to tunneling disturbances; (5) the experimentally determined critical collapse pressure is higher than the theoretical prediction, indicating an underestimation of risks in the current model. The study advances the understanding of the face failure mechanisms in shield tunnels, thereby providing insights into the design and safety of shield tunneling within engineering practices.

The modulus of soil reaction, representing the stiffness of a soil surrounding pipes, is a critical parameter in the design of buried flexible pipes. This study conducted plate loading tests on corrugated polyvinyl chloride, smooth polyvinyl chloride, and high-density polyethylene pipes buried in lightweight cellular concrete (LCC) backfills at densities of 400, 475, 550, and 650 kg/m³ to investigate the pipe deformation behavior and moduli of soil reaction. In addition, this study examined the effects of the narrow trench condition on the pipe deformation and modulus of soil reaction. In these tests, the vertical and horizontal diameter changes of pipes under the vertical pressures applied through a hydraulic jack were measured. Test results reveal that the average moduli of soil reaction of plastic pipes within a wide trench backfilled by the LCCs at densities of 400, 475, 550, and 650 kg/m³ were back-calculated as 66, 99, 133, and 205 MPa, respectively, using the modified Iowa formula. Furthermore, the back-calculated moduli of soil reaction for LCCs exhibited linear relationships with their densities and unconfined compressive strengths and were higher than the recommended values for the commonly used soil backfills. Based on the vertical deformation criterion of 5% pipe diameter, the ultimate bearing capacities of flexible pipes buried in wide LCCs at densities of 475, 550, and 650 kg/m³ exceeded 500 kPa. The LCC with a narrow trench exhibited a lower modulus of soil reaction and ultimate bearing capacity but a larger pipe diameter change.

This study investigates the overall smoke control performance using shafts in a naturally ventilated tunnel in the case of multiple fire sources. Detailed comparisons were also made with the corresponding single fire source scenarios. The results show that the interaction between multiple fire sources affects smoke control performance, resulting in a lower smoke layer height compared to the corresponding single fire scenario. For the multiple fire sources scenarios, the smoke layer height in the fire section first decreases and then keeps stable, as the fire center spacing increases. The smoke layer height in the fire section is 20%-25% lower than that in a single fire source scenario for a given total heat release rate. The minimum smoke layer height at the adjacent non-fire tunnel section is much lower than that in the fire section due to the disturbance of the first group of shafts. For a small tunnel fire such as a car fire, the critical safety distances for firefighters and evacuees increase as the fire source spacing decreases. For a large tunnel fire such as a bus fire, the effect of fire source spacing on the critical safety distance is limited, while the shaft interval plays an important role. The fire source spacing and the number of fire sources have limited influences on the smoke spread length due to the small differences in the induced air flow velocity and overall smoke exhaust rate through shafts. When the fire sources are located under one shaft, the number of shafts required for complete smoke exhaust is the least and the total smoke spread length is the shortest. For a given fire location, the smoke spread length increases significantly with an increasing shaft interval. This study contributes to the design of natural ventilation shafts in tunnels possibly with multiple fire sources.

Shallow-buried large-span tunnels may bend or collapse owing to loads, and their surface structures present considerable safety issues. At Huashanyilu station on Qingdao Metro Line 6 in China, theoretical studies and interior model tests were conducted to effectively increase the bearing capacity of the tunnel. The anchoring bearing mechanism of the high prestress compensating support system was revealed, and the system was built using a negative Poisson’s ratio (NPR) bolt at its core. We compared and analyzed the fracture evolution characteristics of the compensating and conventional support systems under various loads. The results showed that the compensating support system effectively increased the support strength and residual safety factor of the bearing arch, whereas the use of a high-prestress NPR anchor reduced the early deformation of the surrounding rock. The coupling failure modes of the arch tension extrusion failure and arch foot shear fracture occurred when the tunnel surrounding the rock was overloaded. The compensatory support system produces a bearing arch that is extremely resistant to external loads with minimal deformation of the tunnel surface and arch frame, excellent surrounding rock integrity, and a low stress rate. The radial and tangential peak stresses exceeded those of the passive support system, and the structural block fell when it became unstable. The maximum displacement of the arch stays constant at −5.7 mm after tunnel excavation. NPR bolts have remarkable applications in this field. The conclusions of this study have a significant impact on the regulation of the stability of the surrounding rock in large-span tunnels.

This paper proposes an innovative method for selecting the severest design ground motions based on overall damage characterization of underground structures. The selection procedure is elaborated using 4749 ground motions, exemplifying various forms of underground structures in class III sites. Initially, an overall damage index, predicated on dual-parameters of deformation and hysteretic energy dissipation, is proposed as an engineering demand parameter to quantitatively depict the failure state of underground structures. Subsequently, given the inadequacy of a single intensity measure in evaluating the damage of underground structures, composite intensity measures with higher correlation to the index are constructed using partial least squares regression method. The composite intensity measures served as the damage potential characterization parameter for ground motions concerning underground structures. Consequently, alternative databases of severest design ground motions are derived through these composite intensity measures. The ground motions in this alternative database are employed as inputs for nonlinear dynamic analysis of underground structures. The severest design ground motions are identified by ranking the overall damage index to underground structures. Finally, a comparison with traditional selecting method demonstrates that the proposed method yields more accurate results.

The popularity of trenchless techniques as a means of utility pipeline installation in urban environments, specifically microtunnelling/pipe-jacking, has increased in recent years due to its minimally-disruptive nature and reduced carbon footprint in comparison to conventional open-cut excavation methods. The response of pipes during the jacking process is complex and is governed by several factors, including ground conditions, the amount and distribution of lubrication, pipe and annulus size, pipeline misalignments and jacking force eccentricity, among others. Design practice remains based on empirical equations and previous drives through similar geology, resulting in uncertainty in jacking force estimates, thereby restricting adoption of the technique. In order to improve our understanding of the pipe-jacking process, pipes incorporating sensors providing real-time measurements of earth pressures, pore water pressures, axial strains and hoop strains can be used; but the number of such studies reported in the literature is small and the potential of instrumentation on routine projects is largely untapped. Moreover, jacking pipe monitoring practice lags behind the state-of-the-art instrumentation techniques used for monitoring other geotechnical infrastructure. The purpose of this paper is to provide a thorough review of learnings from instrumented pipe-jacking case studies and other supporting research, as well as to propose potential solutions to research gaps in the current state of design practice and field monitoring of pipe jacking projects.

Specific energy (SE) is an important index to measure crushing efficiency in mechanized tunnel excavation. Accurate prediction of the SE of tunnel boring machine disc cutters is important for optimizing the crushing process, reducing energy consumption, and minimizing machine wear. Therefore, in this paper, the sparrow search algorithm (SSA), combined with six chaotic mapping strategies, is utilized to optimize the random forest (RF) model for predicting SE, referred to as the COSSA-RF prediction models. For this purpose, an SE prediction database was established for training and validating model performance, encompassing 160 sets of experimental data, each with six input parameters: uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), disc cutter diameter (D), cutter tip width (T), cutter spacing (S), and cutter penetration depth (P), along with a target parameter, SE. The evaluation results indicate that the COSSA-RF models demonstrate superior performance compared to other four machine learning models. In particular, the Chebyshev map-SSA-RF (CHSSA-RF) model achieves the most satisfactory prediction accuracy among all models, resulting in the highest coefficient of determination R² and dynamic variance-weighted global performance indicator values (0.9756 and 0.0814) and the lowest values of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) (6.4742, 4.0003, and 20.41%). Lastly, the results of interpretability analysis of the best model through SHapley Additive exPlanations, local interpretable model-agnostic explanations, and Vivid methods show that the importance of input parameters ranked as follows: UCS, BTS, P, S, T, and D. Moreover, interactions between parameters (UCS and BTS, BTS and P, and BTS and S) significantly influence the model predictions.

This study evaluates the greenhouse gas (GHG) emissions associated with the construction of subway tunnels, aiming to identify the primary sources of emissions and provide insights into emission reduction strategies. Using the civil engineering construction of specific tunnels of a subway line in Guangdong Province, China, as a case study, this research quantitatively analyzes the composition of GHG emissions across three stages: upstream building materials production, building materials transportation, and on-site construction. The results indicate that upstream building materials production and on-site construction collectively account for over 95% of the total GHG emissions during tunnel construction. The analysis further reveals that a small proportion of building materials and construction machinery accounts for the majority of total GHG emissions during tunnel construction, aligning with the Pareto principle. The findings emphasize the importance of accurate evaluation of high-impact building materials and construction machinery, particularly in contexts where basic energy consumption data are limited. Strategies such as utilizing recycled materials and enhancing machinery efficiency can lead to significant emission reductions. For instance, achieving a recycling rate of 10% to 30% for steel and concrete can reduce total GHG emissions from tunnel construction by 5.51% to 9.94%, while improving machinery efficiency by 10% to 30% can reduce emissions by up to 2.29%. These findings provide a scientific basis for low-carbon subway tunnel construction.

Constructing vertical shafts in densely populated urban areas with complex geological conditions poses significant challenges, necessitating innovative construction techniques and design optimization. This study investigates the deformation behavior of a 42.5 m deep shaft excavated using the vertical shaft sinking machine (VSM) method in Shanghai’s soft soil conditions comprising deep cohesive soil layers. Comprehensive numerical analysis simulated the VSM construction process, analysing deformations within the shaft structure, surrounding soil, and adjacent buildings while evaluating the influence of varying reinforced ring base depths. Results reveal a significant 30% reduction in the maximum lateral shaft deformation, from 28 to 20 mm, by increasing the reinforced ring base depth to an optimal 16 m, enhancing lateral stability. Vertical deformations exhibited complex settlement and uplift mechanisms in segmental rings and piles, influenced by factors like excavation stages, pile installation, water pressures, and adjacent loads. The optimal 16 m depth effectively mitigated uplift, and optimized load distribution, limiting the maximum settlement to 12 mm while minimizing dewatering-induced uplift effects. Analysis indicated reduced lateral movements and settlements in surrounding buildings with increasing distance from excavation, highlighting VSM’s potential for minimizing impacts on neighboring structures. This study emphasizes VSM’s suitability for shaft projects in geologically complex areas, providing insights for design, mitigating environmental impacts, and enhancing deep excavation safety and efficiency in soft soils. The findings contribute to optimizing vertical shaft construction, ensuring successful underground infrastructure execution in challenging conditions. Identifying the optimal reinforced ring base depth promotes sustainable urban development by minimizing disturbances. This research advances innovative methods and strategies for complex underground projects.

Based on the service environment of underground structures, we proposed an electrochemical deposition method for repairing leakage cracks in underground structures and explained its basic principles. Experiments were conducted in the laboratory using electrochemical deposition methods to repair cracked concrete in asymmetric structures in groundwater environments. The macroscopic repair results confirmed that the electrochemical deposition method based on aluminum sulfate calcium acetate electrolyte solution can achieve a crack surface closure rate of 100% and a permeability decrease of 3-4 orders of magnitude after 7 d of repair in groundwater environment, achieving the effect of crack closure. The crack closure rate of the groundwater surface increased with groundwater concentration. Based on the microstructure analysis of X-ray diffraction, thermogravimetry and scanning electron microscope, the mechanism of electrochemical deposition method for repairing underground leakage cracks was revealed. It was confirmed that the main components of deposition products on the crack surface were compounds containing aluminum and calcium such as ettringite and gypsum while the main components on the groundwater side were magnesium calcium compounds such as magnesium hydroxide and calcium carbonate. And as the ion concentration in the groundwater increased, the amount of deposition products on the groundwater side has increased, which is consistent with the macroscopic results. The mercury intrusion porosimetry results showed that the electrochemical deposition method can increase the proportion of small pores in the matrix and optimize the pore size distribution of the matrix. Finally, on-site experiments were conducted on the electrochemical deposition method to repair leakage cracks on Nantong Metro Line 2, China. After repairing the cracks for 3 d, the leakage rate decreased from 6.06 to 1.95 mL/min, and the water seepage path changed, confirming that this method can be applied to the field work.

Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. To address this problem, this paper proposes a hybrid intelligent optimization framework that combines interpretable machine learning, intelligent optimization algorithms, and multi-objective optimization and decision-making methods. The nonlinear relationship between the input parameters and ground settlement (GS) is fitted based on the light gradient boosting machine (LGBM), and the effect of the input parameters on GS is analysed based on SHapley additive exPlanation for further feature selection. Subsequently, the hyperparameters of LGBM were determined based on the sparrow search algorithm (SSA) to better fit the input-output relationship. On this basis, a multi-objective intelligent optimization model is established to solve the optimized operating parameters of shield machine by non-dominated sorting genetic algorithm II and technique for order preference by similarity to ideal solution to reduce GS and improve drilling efficiency. The results demonstrate that the SSA-LGBM model predicts GS with high accuracy, exhibiting an RMSE of 4.775, a VAF of 0.930 and an R² of 0.931. These metrics collectively reflect the model’s excellent performance in prediction accuracy, ability to explain data variability, and control of prediction bias. The multi-objective optimization model is effective in optimizing two objectives, and the improvement can reach up to 39.38%; at the same time, the model has high scalability and can also be applied to three or more objectives. The intelligent optimization framework for shield construction parameters proposed in this paper can generate the optimal parameter combinations for shield machine manipulation, and provide reference and guidance when there are conflicting optimization objectives.

Stray current can cause corrosion of underground structural rebar, adding rubber particles to the invert-filling concrete is an effective prevent method to reduce stray current corrosion. In our research, the corrosion calculation model of multi-ring shield tunnel containing rubber concrete invert-filling was established, the coupling analysis of electric field and chemical field in composite structures was realized through mesoscale simulations, and the accuracy of calculation model was verified by full-scale test. Through calculation, the corrosion characteristic of segment rebar and bolt of multi-ring shield tunnel were investigated under different rubber content. The result shows that adding rubber particles to the invert-filling can not only reduce the corrosion current density of segment rebar and tunnel bolt effectively, but also affect the distribution form of rebar corrosion current density in both circumferential and longitudinal directions. When the rubber content increases from 5% to 20%, the maximum corrosion density of segment rebar and tunnel bolt will decrease from 31% to 58% and 30% to 32%, respectively. Under different stray current leakage modes, when the rubber content and input voltage are the same, the segment and bolt corrosion current density under single rail-two points leakage mode is greater than that in the two rails-single point leakage mode.

Significant movement of in-situ retaining walls is usually assumed to begin with bulk excavation. However, an increasing number of case studies show that lowering the pore water pressures inside a diaphragm wall-type basement enclosure prior to bulk excavation can cause wall movements in the order of some centimeters. This paper describes the results of a laboratory-scale experiment carried out to explore mechanisms of in situ retaining wall movement associated with dewatering inside the enclosure prior to bulk excavation. Dewatering reduces the pore water pressures inside the enclosure more than outside, resulting in the wall moving as an unpropped cantilever supported only by the soil. Lateral effective stresses in the shallow soil behind the wall are reduced, while lateral effective stresses in front of the wall increase. Although the associated lateral movement was small in the laboratory experiment, the movement could be proportionately larger in the field with a less stiff soil and a potentially greater dewatered depth. The implementation of a staged dewatering system, coupled with the potential for phased excavation and propping strategies, can effectively mitigate dewatering-induced wall and soil movements. This approach allows for enhanced stiffness of the wall support system, which can be dynamically adjusted based on real-time displacement monitoring data when necessary.