Underground Space最新文献

Subjected to the coupling action of multiple hazards in hydraulic engineering, hydraulic tunnels may be corroded and damaged to varying degrees during their service lives, which will decrease the seismic performance of these structures. However, the research and seismic design of significant hydraulic engineering projects focus on investigating the structural response based on the design material parameters, which may overestimate the seismic capacity of structures during their service lives. In this paper, research is performed to identify the effect of hydro-chemo-mechanical corrosion on the seismic performance of hydraulic tunnels with different burial depths. A plastic damage model of time-varying concrete degradation induced by the hydro-chemo-mechanical effect is first determined and implemented, and the endurance time acceleration records are generated in MATLAB. Then, a study of the endurance time relationship of hydro-chemo-mechanical corrosion-affected hydraulic tunnels, considering the fluid–structure-surrounding rock interaction systems throughout the service period, is undertaken to directly associate the structural response with the predefined evaluation index. Moreover, this research constructs 3D time-varying fragility surfaces considering the hydro-chemo-mechanical effect and seismic intensity. The results show that the relative displacement of hydro-chemo-mechanical corrosion-affected hydraulic tunnels is larger than that of nonaffected hydraulic tunnels. Hydro-chemo-mechanical effect-induced material deterioration will lead to an increase in the cumulative damage (crack) area and damage degree of hydraulic tunnels. Additionally, the seismic fragility analysis shows that the longer the service time of hydro-chemo-mechanical corrosion-affected hydraulic tunnels, the more likely they are to collapse. Hence, attention should be given to improving the aseismic capacity of hydro-chemo-mechanical corrosion-affected hydraulic tunnels in future seismic design and performance assessments.

One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls. The maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent structures. Nowadays, the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of calculation. This paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls, namely eXtreme gradient boosting (XGBoost), least square support vector regressor (LS-SVR), and random forest (RF), to predict maximum lateral displacement of pile walls. The results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669, the highest coefficient of determination 0.9991, and the lowest root mean square error 0.3544. Although the LS-SVR, and RF models were less accurate than the XGBoost model, they provided good prediction results of maximum lateral displacement of pile walls for numerical outcomes. Furthermore, a sensitivity analysis was performed to determine the most effective parameters in the XGBoost model. This analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.

This paper presents the development of a framework for the parametric design of tunnels using geographic information system (GIS) mapping and building information modelling (BIM). According to the framework, a parametric representation of each system component (e.g., layers of rock mass, size of excavation, topography, fault geometry, primary lining, secondary lining, rock bolts, etc.) can be incorporated in the GIS model with high levels of detail and used for the automatic generation of numerical models for the design of tunnel construction. This parametric evaluation allows the designer to consider several levels of detail for each component, for the efficient design and process optimization in conventional tunneling. Information between parametric analysis and numerical simulations can be freely exchanged with significantly reduced computational effort while results can be exported into BIM. It is anticipated that the proposed framework could lead to a comprehensive, yet efficient analysis of complex conventional tunneling project using parametric evaluations at the early design stages.

Geotechnical centrifuge tests were conducted to examine the influence of invert voids and surface traffic loads on 1400 mm diameter reinforced concrete pipes buried with a shallow soil cover depth of 700 mm. Void formation beneath the pipe was simulated during centrifuge testing. The test results revealed that before void formation, the surface load directly above the middle of the pipe caused a significant increase in not only the circumferential bending moments but also the longitudinal bending moments, the latter of which was considerable and could not be ignored. Void formation beneath the middle of the pipe led to a reduction in both the circumferential bending moments and longitudinal bending moments at all measuring positions, i.e., crown, springline, and invert. The most significant reduction occurred at the invert, and there was even a reversal in the sign of the invert longitudinal bending moment. A comparison was made between centrifuge tests with erosion voids and surface loads at different horizontal positions, which had a marked influence even when the positions differed by half a pipe length. Joint rotation played an important role in relieving large bending moments of pipe barrels in a jointed pipeline when the void and surface load were located at the joint.

Analytics and visualization of multi-dimensional and complex geo-data, such as three-dimensional (3D) subsurface ground models, is critical for development of underground space and design and construction of underground structures (e.g., tunnels, dams, and slopes) in engineering practices. Although complicated 3D subsurface ground models now can be developed from site investigation data (e.g., boreholes) which is often sparse in practice, it remains a great challenge to visualize a 3D subsurface ground model with sophisticated stratigraphic variations by conventional two-dimensional (2D) geological cross-sections. Virtual reality (VR) technology, which has an attractive capability of constructing a virtual environment that links to the physical world, has been rapidly developed and applied to visualization in various disciplines recently. Leveraging on the rapid development of VR, this study proposes a framework for immersive visualization of 3D subsurface ground models in geo-applications using VR technology. The 3D subsurface model is first developed from limited borehole data in a data-driven manner. Then, a VR system is developed using related software and hardware devices currently available in the markets for immersive visualization and interaction with the developed 3D subsurface ground model. The results demonstrate that VR visualization of the 3D subsurface ground model in an immersive environment has great potential in revolutionizing the geo-practices from 2D cross-sections to a 3D immersive virtual environment in digital era, particularly for the emerging digital twins.

The emergence of curved shield tunnels poses a significant construction challenge. If the quality of the segment assembly is not guaranteed, many segment cracks and damage will result from the stress concentration. Sensing the contact stresses between segmental joints is necessary to improve the quality of segments assembled for shield tunnel construction. Polyvinylidene difluoride (PVDF) piezoelectric material was chosen for the sensor because it can convert contact stresses into electrical signals, allowing the state of the segmental joints to be effectively sensed. It matches the working environment between the segmental joints of the shield tunnel, where flexible structures such as rubber gaskets and force transfer pads are present. This study proposes a piezoelectric sensing method for segmental joints in shield tunnels and conducts laboratory tests, numerical analyses, and field tests to validate the feasibility of the method. The results indicate that the PVDF film sensor can effectively sense the entire compression process of the gasket with different amounts of compression. The piezoelectric cable sensor can effectively sense the joint offset direction of the gasket. For differently shaped sections, the variation in the force sensed by the piezoelectric cable sensors was different, as verified by numerical simulation. Through the field test, it was found that the average contact stress between the segmental joints was in the range of 1.2–1.8 MPa during construction of the curved shield tunnels. The location of the segmental joints and the type of segment affect the contact stress value. The field monitoring results show that piezoelectric sensing technology can be successfully applied during assembly of the segments for effective sensing of the contact stress.

The immersed tunnel is considered an effective solution for traffic problems across rivers and seas. The sand filling layer, as an important part of immersed tunnel foundation treatments, directly affects segment attitude stability. Due to difficulties in quality control of concealed construction and the complex hydrodynamic environment, the sand filling layer is prone to compaction defects, further leading to changes in segment attitude. However, limited by structural concealment and state complexity, most studies consider the sand filling layer part of the foundation to study its impact on settlement while neglecting its influence on segment attitude. This research proposes an evaluation method for the sand filling layer state based on elastic wave testing and the elastic wave characteristic parameters selected come from analysis of the time domain, frequency domain and time–frequency domain. By classifying the elastic wave characteristic parameters through the K-means clustering method, the relationship between the state of the sand filling layer and the elastic wave characteristic parameters is established. The state of the sand filling layer is divided into dense, incompact, and void. A numerical model is established based on the Guangzhou BI-UT immersed tunnel with incompact and void sand filling layer states to simulate deformation and torsion. The results indicate that the settlement of the tunnel segment is low in the eastern region and high in the western region due to the presence of a less dense sand filling layer, with a maximum differential settlement of 0.04 m. The evaluation method plays a crucial role in guiding the construction of immersed tube tunnels.

Tunnelling has increasingly become an essential tool in the exploration of underground space. A typical construction problem is the face instability during tunnelling, posing a great threat to associated infrastructures. Tunnel face instability often occurs with the soil arching collapse. This study investigates the combined effect of cutterhead opening ratio and soil non-uniformity on soil arching effect and face stability, via conducting random finite-element analysis coupled with Monte–Carlo simulations. The results underscore that the face stability is strongly associated with the evolution of stress arch. The obtained stability factors in the uniform soils can serve as a reference for the design of support pressure in practical tunnelling engineering. In addition, non-uniform soils exhibit a lower stability factor than uniform soils, which implies that the latter likely yields an underestimated probability of face failure. The tunnel face is found to have a probability of failure more than 50% if the spatial non-uniformity of soil is ignored. In the end, a practical framework is established to determine factor of safety (FOS) corresponding to different levels of probability of face failure considering various opening ratios in non-uniform soils. The required FOS is 1.70 to limit the probability of face instability no more than 0.1%. Our findings can facilitate the prediction of probability of instability in the conventionally deterministic design of face pressure.

Contemporary demands necessitate the swift and accurate detection of cracks in critical infrastructures, including tunnels and pavements. This study proposed a transfer learning-based encoder-decoder method with visual explanations for infrastructure crack segmentation. Firstly, a vast dataset containing 7089 images was developed, comprising diverse conditions—simple and complex crack patterns as well as clean and rough backgrounds. Secondly, leveraging transfer learning, an encoder-decoder model with visual explanations was formulated, utilizing varied pre-trained convolutional neural network (CNN) as the encoder. Visual explanations were achieved through gradient-weighted class activation mapping (Grad-CAM) to interpret the CNN segmentation model. Thirdly, accuracy, complexity (computation and model), and memory usage assessed CNN feasibility in practical engineering. Model performance was gauged via prediction and visual explanation. The investigation encompassed hyperparameters, data augmentation, deep learning from scratch vs. transfer learning, segmentation model architectures, segmentation model encoders, and encoder pre-training strategies. Results underscored transfer learning's potency in enhancing CNN accuracy for crack segmentation, surpassing deep learning from scratch. Notably, encoder classification accuracy bore no significant correlation with CNN segmentation accuracy. Among all tested models, UNet-EfficientNet_B7 excelled in crack segmentation, harmonizing accuracy, complexity, memory usage, prediction, and visual explanation.

The stability of underground entry-type excavations (UETEs) is of paramount importance for ensuring the safety of mining operations. As more engineering cases are accumulated, machine learning (ML) has demonstrated great potential for the stability evaluation of UETEs. In this study, a hybrid stacking ensemble method aggregating support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), multilayer perceptron neural network (MLPNN) and extreme gradient boosting (XGBoost) algorithms was proposed to assess the stability of UETEs. Firstly, a total of 399 historical cases with two indicators were collected from seven mines. Subsequently, to pursue better evaluation performance, the hyperparameters of base learners (SVM, KNN, DT, RF, MLPNN and XGBoost) and meta learner (MLPNN) were tuned by combining a five-fold cross validation (CV) and simulated annealing (SA) approach. Based on the optimal hyperparameters configuration, the stacking ensemble models were constructed using the training set (75% of the data). Finally, the performance of the proposed approach was evaluated by two global metrics (accuracy and Cohen’s Kappa) and three within-class metrics (macro average of the precision, recall and F₁-score) on the test set (25% of the data). In addition, the evaluation results were compared with six base learners optimized by SA. The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy, Kappa coefficient, macro average of the precision, recall and F₁-score were 0.92, 0.851, 0.885, 0.88 and 0.883, respectively. The rock mass rating (RMR) had the most important influence on evaluation results. Moreover, the critical span graph (CSG) was updated based on the proposed model, representing a significant improvement compared with the previous studies. This study can provide valuable guidance for stability analysis and risk management of UETEs. However, it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.