Underground Space最新文献

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.

Local failures (loss of concrete or reinforcement) can severely compromise the bearing capacity of shield segments, damaging the tunnel structures. To investigate the effects of local openings on the bearing behavior and failure mechanism, four full-scale bending tests were conducted on specimens with different opening positions and diameters; monitoring of load, displacement, and concrete strain was performed during loading. The test results reveal that both the opening position and diameter significantly influence the bearing characteristics of the segment. The failure process includes four sequential stages distinguished by three critical loads, namely the cracking, failure, and ultimate loads. Subsequently, the numerical model of the local failure segment was established using the elastoplastic damage constitutive relation of the concrete and verified by inversing the full-scale test results. Based on the numerical model, parametric analyses were performed to comprehensively investigate the influences of the opening position, concrete loss, and reinforcement loss on the bending capacity. Furthermore, an analytical model was proposed, indicating that the opening position is the primary factor decreasing the bearing capacity, followed by the opening diameter and reinforcement loss. The results of this study can provide a theoretical basis for the safety assessment and remedial design of subway shield tunnels under extreme breakthrough conditions.

This study presents a detailed investigation into the soil arching effects within deep foundation pits (DFPs), focusing on their mechanical behavior and implications for structural design. Through rigorous 3D finite element modeling and parameter sensitivity analyses, the research explores the formation, geometric characteristics, and spatial distribution of soil arching phenomena. The investigation encompasses the influence of key parameters such as elastic modulus, cohesion, and internal friction angle on the soil arching effect. The findings reveal that soil arching within DFPs exhibits distinct spatial characteristics, with the prominent arch axis shifting as excavation depth progresses. Optimal soil arching is observed when the pile spacing approximates three times the pile diameter, enhancing soil retention and minimizing deformation risks. Sensitivity analyses highlight the significant impact of soil parameters on soil arching behavior, underscoring the critical role of cohesive forces and internal friction angles in shaping arching characteristics. By elucidating the interplay between soil parameters and soil arching effects, the research provides insights for optimizing pile spacing and structural stability.

With the rapid development of urban underground space, the construction of shield-driven cross-river twin tunnels is increasing, and the complex hydro-mechanical coupling effects and twin-tunnel interactions bring huge construction risks to such projects, which have attracted more and more attention. This study aims to understand the excavation effects induced by shield driving of cross-river twin tunnels through numerical simulation. A refined three-dimensional numerical model based on the fully coupled hydro-mechanical theory is established. The model considers the main components of the slurry pressure balance shield (SPBS) machine, including support force, jacking thrust, grouting pressure, shield-rock interaction and lining-grouting interaction, as well as the detailed construction process. The purpose is to examine the excavation effects during construction, including rock deformation around tunnels, the change in pore pressure, and the response of the lining. The results show the influence range of twin-tunnel excavation on rock deformation and pore pressure, as well as the modes of lining response. In addition, this study also systematically investigates the effects of water level fluctuation and burial depth on twin-tunnel excavation. The results indicate that the increase of water level or burial depth will enhance the excavation effects and strengthen the twin-tunnel interactions. These results provide useful insights for estimating the construction impact range and degree of twin tunnels, and serve as basic references for the design of cross-river twin tunnels.

Multiform fractures have a direct impact on the mechanical performance of rock masses. To accurately identify multiform fractures, the distribution patterns of grayscale and the differential features of fractures in their neighborhoods are summarized. Based on this, a multiscale processing algorithm is proposed. The multiscale process is as follows. On the neighborhood of pixels, a grayscale continuous function is constructed using bilinear interpolation, the smoothing of the grayscale function is realized by Gaussian local filtering, and the grayscale gradient and Hessian matrix are calculated with high accuracy. On small-scale blocks, the pixels are classified by adaptively setting the grayscale threshold to identify potential line segments and mini-fillings. On the global image, potential line segments and mini-fillings are spliced together by progressing the block frontier layer-by-layer to identify and mark multiform fractures. The accuracy of identifying multiform fractures is improved by constructing a grayscale continuous function and adaptively setting the grayscale thresholds on small-scale blocks. And the layer-by-layer splicing algorithm is performed only on the domain of the 2-layer small-scale blocks, reducing the complexity. By using rock mass images with different fracture types as examples, the identification results show that the proposed algorithm can accurately identify the multiform fractures, which lays the foundation for calculating the mechanical parameters of rock masses.

The collapse of the tunnel face is a prevalent geological disaster in tunnelling. This study employs a three-dimensional (3D) material point method (MPM) to simulate the dynamic collapse process and post-failure mechanisms of the tunnel face. The specific focus is on the scenario where the auxiliary air pressure balanced shield with a partially filled chamber is shut down. To assess the suitability of the 3D MPM, numerical solutions are compared with the results from small-scale experimental tests. Subsequently, a series of large-scale numerical simulations is conducted to explore the dynamic collapse characteristics of the tunnel face induced by the shutdown of the EPB shield under various support air pressures and cutter head conditions. The temporal evolution of the accumulated soil masses in the soil chamber and ground responses under different support air pressures, cutter head types and opening ratios are discussed. In particular, the associated surface subsidence due to the tunnel face collapse is determined and compared with empirical solutions. Numerical results confirm the applicability of the 3D MPM for simulating the large-scale tunnel face collapse scenarios, spanning from small to large deformation analysis.

The precast composite reinforced concrete wall with the advantages of fewer joints, superior impermeability and rapid construction provides an efficient and environmental friendly alternative in the construction of underground utility tunnels in the last few years. To investigate the seismic performance of precast concrete composite walls of utility tunnels with grouting-sleeve connection under out-of-plane loads, a series of quasi-static cyclic tests were performed on the full-scale sidewall specimens with different axial compression ratios in this study. The experimental results including the failure modes, crack distributions, and the influence of different connections on the out-of-plane seismic performance of precast concrete composite wall were carefully examined and compared with those from the cyclic tests of the cast-in-place sidewalls of the utility tunnel. The test results show that the seismic performance of the precast concrete composite sidewall specimen, such as the hysteresis curves, the ultimate bearing capacity, stiffness degradation pattern and the ductility ratio, is basically the same as that of the cast-in-place specimen, indicating that the seismic performance of the prefabricated structure is equivalent to that of the cast-in-place structure. Moreover, the grouting-sleeves of the joints can effectively transfer the reinforcement stress until the failure of the precast concrete composite sidewall specimens, which exhibits excellent out-of-plane ductility and serviceability.

Ground losses due to tunneling would induce settlement of nearby raft foundations. To study the change in behavior of the raft foundations over time due to tunnel excavation in soft clay, a series of centrifuge model tests were conducted. The results reveal that the raft stiffness has a significant influence on the development of the gap between the raft and the ground. The width of the gap beneath the flexible foundation would increase over time, leading to a further increase in tensile strain after excavation, whereas the gap for raft foundations with a large stiffness would reduce with time, causing a gradual decrease in tensile strain. The modification factor (MF) design approach is also evaluated with the test results and demonstrates that the MF design approach would underestimate the tensile strain of the flexible raft and provide a relatively conservative prediction for larger stiffnesses.

This paper proposes an efficient method for quantifying the stratigraphic uncertainties and modeling the geological formations based on boreholes. Two Markov chains are used to describe the soil transitions along different directions, and the transition probability matrices (TPMs) of the Markov chains are analytically expressed by copulas. This copula expression is efficient since it can represent a large TPM by a few unknown parameters. Due to the analytical expression of the TPMs, the likelihood function of the Markov chain model is given in an explicit form. The estimation of the TPMs is then re-casted as a multi-objective constrained optimization problem that aims to maximize the likelihoods of two independent Markov chains subject to a set of parameter constraints. Unlike the method which determines the TPMs by counting the number of transitions between soil types, the proposed method is more statistically sound. Moreover, a random path sampling method is presented to avoid the directional effect problem in simulations. The soil type at a location is inferred from its nearest known neighbors along the cardinal directions. A general form of the conditional probability, based on Pickard's theorem and Bayes rule, is presented for the soil type generation. The proposed stratigraphic characterization and simulation method is applied to real borehole data collected from a construction site in Wuhan, China. It is illustrated that the proposed method is accurate in prediction and does not show an inclination during simulation.