Search Results (61)

Airfoil flow-field prediction is important for aerodynamic design, but wind-tunnel testing and computational fluid dynamics (CFD) remain costly and time-consuming. Deep learning enables fast inference, yet many existing models still rely on fixed grid representations, which may lead to insufficient learning in high-gradient regions and larger local errors. This study proposes Spatial Multilayer Perceptron (Spatial MLP) together with an Error-Gradient-Guided Data Sampling (EGDS) strategy for airfoil flow-field prediction. Spatial MLP adopts a coordinate-based point-wise prediction framework. A spatial decoder is introduced as an auxiliary branch to enhance global flow consistency during pretraining, while channel-wise multi-head attention is incorporated to improve cross-variable feature coupling. EGDS prioritizes physically informative points according to relative prediction error and gradient magnitude, while retaining random samples to preserve data diversity. Experiments on an independent test set show that Spatial MLP reduces the mean relative error (averaged over the velocity components u, v, and pressure p) by 15.2% relative to the MLP baseline. With EGDS, the overall mean relative error is further reduced by 34.5% relative to the MLP baseline. These results demonstrate that combining global consistency constraints with targeted sampling effectively improves both global prediction accuracy and local reconstruction quality in high-gradient flow regions. Full article

Dust pollution induced by blasting during tunnel construction via the drill-and-blast method poses a severe threat to workers’ health and construction safety. To address this issue, a wet chord grid dust removal and purification device adaptable to deep-buried long tunnels was developed in this study. The device integrates dust control and removal functions, featuring mobility, high purification efficiency, and water recycling capability. Through experimental tests, the optimal operating parameters of the system were determined: the dust removal efficiency reached a peak of 94.3% (laboratory optimal value from the basic parameter optimization test) when the frequency of the extraction axial flow fan was set to 30 Hz and the cross-sectional wind speed of the chord grid reached 3.34 m/s. The circulating water tank achieved the optimal water treatment performance under the conditions of a relative buried depth of 0.42 for the water inlet, a volume ratio of 1:2 for the sedimentation area to the clear water area, and a relative baffle height of 0.65. Numerical simulations based on CFD software (2021) revealed that the on-site dust removal efficiency of the device reached 79.86% and 87.9% under the working conditions where the tunnel face was 10 m and 100 m away from the connecting passage, respectively, which are in good agreement with the field measurement results. In the practical application at the Shierpo Tunnel of the Guangxi Tianba Expressway, the device achieved an average total dust removal efficiency of 78.4%, with 81.2% removal efficiency for PM₁₀ and 76.5% for PM_2.5, demonstrating excellent engineering applicability and dust removal performance for respirable dust. This study provides effective technical support and a theoretical basis for improving the construction environment of drill-and-blast tunnels. Full article

To address the challenge of surrounding rock instability in deep-buried tunnels crossing fractured fault zones, this study focuses on the Xigu Tunnel of the Lanzhou–Hezuo Railway. A combination of laboratory triaxial tests, an optimized multi-source advanced geological prediction workflow, and a site-specific parameter-weakened Mohr–Coulomb numerical simulation is employed to systematically reveal the physical–mechanical properties, spatial distribution, and deformation response of fractured rock masses under excavation-induced disturbance. The triaxial test results show that the average peak strength of the surrounding rock reaches 149.04 MPa; however, significant variability is observed among samples, and the failure mode exhibits a typical brittle–shear composite feature. The measured cohesion and internal friction angle are 20.57 MPa and 49.91°, respectively, indicating high intrinsic strength of individual rock blocks. Nevertheless, due to the presence of densely developed joints and crushed structures, the overall mass is loose and highly sensitive to dynamic disturbances such as blasting and excavation, revealing a unique mechanical paradox of high-strength rock blocks with low overall rock mass stability in deep-buried fractured zones. Joint TSP (Tunnel Seismic Prediction Ahead) and ground-penetrating radar (GPR) prediction reveals decreased P-wave velocity, increased Poisson’s ratio, and intensive seismic reflection interfaces; a quantitative index system for identifying the boundaries of narrow deep-buried fractured zones is proposed based on these geophysical characteristics. Combined with geological face mapping, these results confirm the existence of a highly fractured zone approximately 130 m in width, characterized by well-developed joints, heterogeneous mechanical properties, and localized risks of blockfall and groundwater ingress. The developed numerical model, with parameters weakened based on triaxial test and geological prediction data, effectively reproduces the deformation law of the fractured zone, and the simulation results agree well with field monitoring data, with peak displacement concentrated at section DK4 + 595, thus accurately identifying the center of the fractured belt as a key engineering validation result of the integrated technical framework. During construction, based on the identified spatial characteristics of the fractured zone and the proposed targeted support insight, enhanced dynamic monitoring and targeted support measures at the fractured zone center are required to ensure structural safety and long-term stability of the tunnel. This study develops an integrated engineering-oriented technical framework for deep-buried tunnels crossing narrow fractured zones, and provides novel mechanical insights and quantitative identification indices for such complex geological engineering scenarios. Full article

Human engineering activity, such as cross-regional transportation construction, often disturbs the geological environment and triggers landslides. This study investigated a landslide induced by tunnel excavation in the northeastern region of the Qinghai–Tibet Plateau, exploring how a seemingly low-risk local small-scale landslide can trigger an engineering disaster. Based on field geological and geomorphological surveys, unmanned aerial vehicle (UAV) remote sensing photography, and SBAS-InSAR data analysis (time-series monitoring from 2021 to 2023), the spatiotemporal evolution patterns and causative mechanisms of landslide deformation were systematically elucidated. The results indicate the following: (1) The landslide evolved from initial multiple small local slides, gradually expanding and connecting to form a larger and deep-seated landslide. (2) SBAS-InSAR analysis revealed that the landslide deformation rate ranged from −38.13 to 12.01 mm/a, with a maximum cumulative deformation of 121.91 mm. Substantial deformation was concentrated in April–June 2021, June–August 2022, and April–July 2023. Spatially, the deformation intensity exhibited a pattern of middle section > front > rear, with greater deformation closer to the tunnel construction point. (3) The landslide deformation is primarily related to tunnel construction disturbance. The topography, geological structure, and frozen ground thawing exerted certain influences. The deformation mechanism is summarized as follows: Slope toe excavation initially triggers local sliding, leading to tension cracking at the rear edge. Subsequently, tunnel construction further promotes landslide expansion, resulting in the formation of a deep-seated landslide. This study showed that the landslide resulted from the combined effects of engineering activity and natural conditions. The results reveal that, under disturbances from inappropriate engineering activities, local small landslides may develop into major disasters. Therefore, the construction plan for the tunnel must be revised to mitigate such risks. Full article

In underground construction, the drilling and blasting method is widely used due to its advantages, such as low cost, simple implementation, and applicability under various geological and hydrogeological conditions. One parameter that significantly affects the effectiveness of drilling and blasting is the post-blast tunnel cross-sectional area. In this study, multiple linear regression analysis (MLRA) and multiple nonlinear regression (MNLR) models were used to predict the area of a tunnel face after blasting, utilizing 136 datasets containing parameters measured from the tunnel face area after blasting during the Deo Ca tunnel construction project. Three deep learning models, an artificial neural network (ANN) and two hybrid models combining an ANN with the particle swarm optimization (PSO) algorithm and an ANN with a genetic algorithm (GA), were then developed to predict the tunnel face area after blasting. The input variables for the calculation and prediction models included the designed tunnel face area (S_d), the specific charge (SC) of the explosion, the average borehole length (L), and the rock mass rating (RMR) of the rock mass on the tunnel face. The GA-ANN model’s results, including determination coefficient (R²) and mean square error (MSE) values of R²_train = 0.9562, R²_testing = 0.94, MSE_training = 0.0156, and MSE_testing = 0.0302, indicate that it provides a better prediction of the tunnel face area after blasting than the other models. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article

Objectives: Compression neuropathies such as Anterior Tarsal Tunnel Syndrome are usually associated with focal thickening at the compression site. This study aimed to determine the frequency and location of thickenings of dorsal foot nerves in asymptomatic, healthy volunteers. We hypothesized that focal nerve thickening of dorsal foot nerves is a frequent finding in asymptomatic individuals and occurs at anatomically plausible locations, potentially limiting the specificity of ultrasound in the diagnosis of anterior tarsal tunnel syndrome. Materials and Methods: In this prospective study, the nerves at the dorsal foot were examined with ultrasound in 60 volunteers without clinical signs of neuropathy. Cross-sectional area (CSA) changes along the nerve course were assessed, their anatomical location recorded, and demographic data collected. Results: Focal deep peroneal nerve (DPN) thickening was observed in 45% of participants, with a median CSA of 2.14 mm² (range: 0.84–5.16) and median length of 3.98 mm (range: 1.46–9.95). The most frequent site was the first tarsometatarsal joint (41%). Thickening occurred across all age groups. Superficial peroneal nerve (SPN) thickening was found in 13.3% of participants, primarily affecting the intermediate branch, with a median CSA of 1.82 mm² and length of 3.02 mm. No thickening was observed in the sural nerve (SN). A strong correlation was found between CSA and length of DPN thickening (r = 0.67, p < 0.001). Conclusions: Asymptomatic, focal thickening of dorsal foot nerves, particularly the DPN, is a frequent sonographic finding in healthy volunteers. These findings highlight the potential for false-positive ultrasound results and the necessity of correlating imaging findings with clinical examination when evaluating for anterior tarsal tunnel syndrome and similar neuropathies. Full article

Driven by the advancement of the global blue economy strategy and the rapid expansion of urbanization into deep underground spaces, the scale of critical infrastructure, ranging from cross-sea bridges and undersea tunnels to offshore wind farms and deep-buried utility tunnels, has reached unprecedented levels [...] Full article

With the rapid development of cross-sea infrastructure, the immersed tube method has been increasingly applied to deep-water immersed-tube tunnel construction. However, when the construction depth reaches the scale of one hundred meters, issues such as high hydrostatic pressure, complex hydrological conditions, and limited construction windows significantly elevate project risks. Against this backdrop, this study systematically reviews relevant domestic and international research findings in the context of 100-m-deep water environments and constructs a comprehensive risk index system covering the construction processes of the WBS breakdown system based on the WBS-RBS decomposition method within the HSE framework. A risk index weighting analysis combines quantitative and qualitative analysis, categorizing the indicators into qualitative and quantitative categories. Quantitative analysis employs threshold determination and the LEC method; qualitative analysis utilizes expert surveys and the G1 method. Ultimately, a model that combines multiple methods for a 100-m-deep water environment, integrating subjective expertise and objective data, is developed. On this basis, multi-level prevention and control measures are proposed for hundred-meter-deep water-immersed tube construction. The results demonstrate that the proposed system can effectively identify key risk sources under deep-water conditions and provide practical countermeasures, offering significant guidance for ensuring construction safety and engineering quality in hundred-meter immersed-tube tunnel projects. Full article

The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article

The safety of the deep-buried, long tunnel at the active fault is a crucial issue in the Yangtze River to Hanjiang River Water Diversion Project, which crosses the Tongcheng River Fault. This study presents the first systematic investigation into the behavior of large deep-buried water diversion tunnels crossing active faults. Based on an analysis of the geostress field, numerical simulations were conducted to evaluate the response of the lining without adaptive measures. Subsequently, a method for estimating hinged design parameters was proposed, and reasonable design values were determined. Furthermore, the effectiveness of the adaptive hinged structure in improving anti-dislocation performance was assessed using a self-developed evaluation framework for tunnel lining. The results show that (1) Geostresses include a 35° angle between horizontal principal stress and the tunnel axis, with horizontal stresses of 20 MPa (axial) and 21 MPa (perpendicular), and vertical stress of 18 MPa. (2) Without adaptive measures, tunnel deformation peaks in the fault zone, showing vault-floor convergence; maximum principal stresses and liner damage concentrate there. (3) The proposed hinge-type adaptive design suggests a 6 m segmented section length and 2–4 cm hinge width initially; sensitivity analysis recommends 6 m and 5 cm, respectively. (4) Adaptive measures reduce tensile stress in the fault zone, significantly mitigating deformation, stress, and liner damage, proving their efficacy in enhancing anti-fault-rupture performance. Full article

Water and mud inrush hazards pose significant threats to the safety, environmental stability, and resource efficiency of tunnel construction, representing a critical barrier to the development of sustainable transportation infrastructure. Misjudgment—especially missed detections of severe hazards—can lead to extensive geological disturbance, excessive energy consumption, and severe socio-environmental impacts. However, pre-trained large-scale models still face two major challenges when applied to tunnel hazard classification: limited labeled samples and the high cost associated with misclassifying severe hazards. This study proposes a sustainability-oriented intelligent classification framework that integrates a large-scale pre-trained model with multi-strategy data augmentation to accurately identify hazard levels during tunnel excavation. First, a Synthetic Minority Over-Sampling Technique (SMOTE)-based multi-strategy augmentation method is introduced to expand the training set, mitigate class imbalance, and enhance the model’s ability to recognize rare but critical hazard categories. Second, a deep feature extraction architecture built on the robustly optimized BERT pretraining approach (RoBERTa) is designed to strengthen semantic representation under small-sample conditions. Moreover, a hierarchical weighting mechanism is incorporated into the weighted cross-entropy loss to emphasize the identification of severe hazard levels, thereby ensuring zero missed detections. Experimental results demonstrate that the proposed method achieves an accuracy of 99.26%, representing a 27.96% improvement over the traditional SVM baseline. Importantly, the recall for severe hazards (Levels III and IV) reaches 100%, ensuring zero misjudgment of major hazards. By effectively reducing safety risks, minimizing environmental disruptions, and promoting resilient tunnel construction, this method provides strong support for sustainable and low-impact underground engineering practices. Full article

The T-tail configuration, while offering advantages for large transport aircraft, is susceptible to peculiar aerodynamic phenomena such as deep stall and flutter, necessitating high-fidelity dynamic scaling for wind tunnel testing. In order to address the issue of similarity in the dynamic characteristics of scaled T-tail models, we propose a comprehensive optimization design method for dynamic scaled equivalent models of T-tail structures with rear fuselages. The development of an elastic-scaled model is accomplished through the integration of the least squares method with a genetic sensitivity hybrid algorithm. In this framework, the objective function is defined as minimizing a weighted sum of the frequency errors and the modal shape discrepancies ($1-$ Modal Assurance Criterion) for the first five modes, subject to lower and upper bound constraints on the design variables (e.g., beam cross-sectional dimensions). The findings indicate that the application of finite element modelling in conjunction with multi-objective optimization results in the scaled model that closely aligns with the dynamic characteristics of the actual aircraft structure. Specifically, the frequency error of the optimized model is maintained below 2%, while the modal confidence level exceeds 95%. A ground vibration test (GVT) was conducted on a fabricated scaled model, with all frequency errors below 3%, successfully validating the optimization approach. This GVT-validated high-fidelity model establishes a reliable foundation for subsequent wind tunnel tests, such as flutter and buffet experiments, the results of which are vital for validating the full-scale aircraft’s aeroelastic model and informing critical flight safety assessments. The T-tail elastic model design methodology presented in this study serves as a valuable reference for the analysis of T-tail characteristics and the design of wind tunnel models. Furthermore, it provides insights applicable to multidisciplinary optimisation and the design of wind tunnel models for other similar elastic scaled-down configurations. Full article

Deformation monitoring of heavy-haul railway tunnels is essential for ensuring operational safety. However, the spatial resolution of traditional point-based sensors is often insufficient for capturing the continuous deformation fields of tunnel structures. To overcome this limitation, in this study, densely distributed strain data that are acquired through distributed fiber-optic sensing technology are used, and a deep learning-based inversion framework that integrates high-resolution strain measurements with sparsely sampled convergence data is introduced. By employing a hybrid particle swarm optimization–random forest (PSO-RF) algorithm, a deep correlation model is constructed to establish the relationship between distributed strain profiles and discrete convergence measurements. This approach enables the prediction of cross-sectional convergence across multiple tunnel sections by using only a limited set of calibrated convergence sensors in combination with continuous strain field data, thereby effectively achieving global deformation inversion with minimal hardware deployment. The proposed method was validated through numerical simulations and field tests by using monitoring data from a heavy-haul railway tunnel. The algorithm exhibited a mean absolute error of less than 2 mm, thus demonstrating its ability to supply high-resolution deformation field data that are essential for structural health monitoring and diagnostics of tunnel infrastructures. Full article

The present investigation presents the field measurement and prediction of tunneling-induced surface ground settlement in Tianjin Metro Line 7, China. The cross-section of a metro tunnel exhibits circular symmetry, thereby making it suitable for tunneling with a circular shield machine. The ground surface may deform during the tunneling stage. In the early stage of tunneling, few measurement data can be collected. To obtain a better usable prediction model, two kinds of neural networks according to the model-agnostic meta-learning (MAML) scheme are presented. One kind of deep learning strategy is a combination of the Back-Propagation Neural Network (BPNN) and the MAML model, named MAML-BPNN. The other prediction model is a mixture of the MAML model and the Long Short-Term Memory (LSTM) model, named MAML-LSTM. Founded on several measurement datasets, the prediction models of the MAML-BPNN and MAML-LSTM are successfully trained. The results show the present models possess good prediction ability for tunneling-induced surface ground settlement. Based on the coefficient of determination, the prediction result using MAML-LSTM is superior to that of MAML-BPNN by 0.1. Full article