Search Results (154)

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

The paper presents the evaluation and research undertaken to propose an optimal solution for the Capra–Bâlea road tunnel, within the framework of rehabilitating and modernizing the entire road section, with the objective of ensuring uninterrupted vehicular traffic during the winter season. The Capra–Bâlea road tunnel is the longest operational and under exploitation tunnel in Romania, measuring 887 m, and the highest-altitude road tunnel structure in the country, at 2042 m above sea level. It serves as a connection between the historic regions of Tara Romaneasca and Transylvania via the DN7C national road, commonly referred to as the Transfagarasan, which is among Romania’s most significant tourist routes, and contains five of the ten existing road tunnels in the country. The tunnel passes through crystalline metamorphic rocks typical of the Fagaras mountains. The construction method was typical of the 1970s, combining drill-and-blast in the central section with cut-and-cover execution at the two ends. The technical condition of the tunnel, evaluated through a detailed technical inspection, is presented, highlighting defects and proposing rehabilitation or restoration solutions. The existing cross sections are described and comparatively analyzed against the currently recommended cross-sections in accordance with present standards and gauge requirements. A three-dimensional simulation of both the current and original cross-sections was performed to investigate the behavior of this type of structure, and solutions for tunnel rehabilitation and modernization are recommended. Finally, the advantages of the proposed solution are discussed. Full article

The irregular geometry of highway tunnel linings, combined with uneven terrain and external disturbances, often causes inspection robots to deviate from their predefined paths. Due to the strong coupling inherent in robotic systems, these deviations propagate to the end-effector, significantly compromising automated inspection accuracy and effectiveness. To tackle these issues, this study introduces an Enhanced Extended Sliding Mode Predictive Control (EESMPC) method, which integrates an adaptive Extended State Observer (ESO). The algorithm is derived from the robot chassis model and a desired trajectory error model, enabling precise contour profile tracking. Crucially, the integrated ESO actively estimates and compensates for unmodeled disturbances and system uncertainties within the state feedback, thereby enhancing both path tracking stability and precision. Comparative MATLAB simulations and experimental path tracking tests evaluated the performance against three other controllers. The results demonstrate that the EESMPC algorithm achieves superior tunnel lining tracking performance, exhibiting marked improvements in both tracking accuracy and system robustness. Consequently, this approach significantly enhances the automated inspection accuracy and operational efficiency of highway tunnel inspection robots. Full article

Reliable positioning and mapping in roadway tunnels are crucial for vehicle-based monitoring and inspection, especially considering the challenging environmental conditions such as rapidly changing illumination, low-texture environments, and repetitive structural elements. While general LiDAR-inertial odometry (LIO) frameworks and loop-closure detection methods are effective in general scenarios, they often suffer from severe drift or incorrect loop constraints under these specific conditions. These challenges are further exacerbated by the inherent uncertainties associated with low-cost sensors. This paper introduces a narrow field-of-view LiDAR-centric RTK-visual-inertial SLAM system enhanced by three key modules: semantic-assisted loop detection and matching, two-stage RTK quality control, and adaptive factor graph optimization (FGO). In the first module, the proposed semantic loop descriptor (SLD) matching is used to determine the potential loop closure locations and then integrates the corresponding constraint as graph nodes. The quality control module addresses RTK outlier rejection during tunnel entry and exit, employing an event-driven stochastic model to characterize the uncertainty between RTK and the other sensors, effectively suppressing RTK-induced errors. FGO module performs optimization by incorporating LIO, RTK, and loop closure factors, employing a keyframe-based strategy to produce globally optimized poses while continuously updating the map. The proposed Tunnel-SLAM was evaluated against state-of-the-art SLAM algorithms in four extended roadway tunnels, ranging in traveling distance approximately from 5 to 10 km. Experimental results demonstrate that the proposed SLAM achieved a final drift of less than 2 m with loop closure, demonstrating significantly reducing the drift, while other existing SLAM frameworks fail catastrophically or have large drift. Full article

The damage status of shield machine disc cutters directly impacts the safety and efficiency of tunnelling projects. Current manual inspection methods involve high risks and low efficiency, while existing detection methods suffer from low accuracy and poor real-time performance in complex environments, often lacking quantitative analysis capabilities. To address these issues, this paper proposes an intelligent identification and quantitative assessment method for disc cutter damage based on the Deep Forest (DF) model. First, an eddy current sensor calibration platform was established, and a mapping relationship between output voltage and actual wear was developed through piecewise fitting to achieve precise wear quantification. In the data preprocessing stage, signal quality was improved via filtering, and typical damage features such as edge chipping, cracks, and eccentric wear were extracted using pulse edge detection. These feature segments were then resampled to construct the model training dataset. The DF model utilizes a hierarchical ensemble structure to mine data correlations, enabling accurate identification of four states: normal, edge chipping, eccentric wear, and cracks. Simultaneously, a DF regression model was employed to provide continuous quantitative predictions of damage size. Experimental results show that the classification model achieved accuracies of 98%, 96%, and 96% on the training, validation, and test sets, respectively, with weighted average F1-scores exceeding 0.96. The regression model achieved a coefficient of determination (R²) of 0.9940 and a root mean square error (RMSE) of 0.4051 on the test set. Both models demonstrate excellent performance and generalization, achieving full coverage from “qualitative state identification” to “quantitative wear assessment,” thereby providing reliable decision support for cutter maintenance and replacement. Full article

Objective: The study aim was panoramic sonographic inspection of the median nerve (MN) from the wrist to the forearm in cases and controls. Additionally, integration of comparisons at various levels may aid in identifying principal ultrasound parameters of carpal tunnel syndrome (CTS). Methods: Dynamic, static, and panoramic sonographies of 65 healthy and 83 CTS hands were performed. Multileveled qualitative (MN and flexor retinaculum morphology) and quantitative (cross-sectional area CSA, differences, and ratios) MN variables for CTS, followed by comparative statistical analysis to predict values and patterns of MN, were derived. Results: Subjectively, hypoechoic, vascular, compressed, hypomobile MN and bowed thickened flexor retinaculum were significantly more prevalent in cases (28.9–66.3%) than in controls (0–7.7%). Objectively, the proximal to inlet (pi) and the forearm at 12 cm (12) were the most representative sites. The area under curve (AUC) values for the MN dimensions, in decreasing order, were 0.9, 0.89, 0.86, and ≤0.80 for the CSA difference ‘pi’ and ‘12’ (Cpi-C12), the CSA proximal to inlet (Cpi), the ratio of CSA at pi and 12 (Cpi/C12), and the CSA at inlet (Ci), respectively. Their cut-off values were 3.7, 9.1, 1.8, and 7.2 mm², respectively. Differences and ratios between ‘Cpi’ and ‘Ci’ were less reliable (AUC ≤ 0.74, sensitivity ≤ 61.4%). Flexor retinaculum bowing, thickening, and MN flattening ratios were unreliable. Conclusions: Sensitivity, specificity, and precision of MN sonographic parameters in CTS increase by utilizing differences and ratios between wrist and forearm rather than isolated values. The recommended site in wrist is proximal to the inlet, and in the forearm, the best site to determine ratios and differences is at 12 cm from the distal wrist crease. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article

Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extensive research on rockburst mechanisms and mitigation, the causal analysis of individual events remains challenging due to the complex interaction between seismicity, geological conditions, stress redistribution, and operational factors. This study proposes a structured and multidisciplinary methodology for the causal analysis of rockbursts in deep high-stress tunnels. The methodology integrates seismicity analysis, geological and geotechnical characterization, operational assessment, field damage inspection, and hypothesis-driven interpretation to systematically reconstruct the sequence of processes leading to rockburst occurrence. The proposed approach is applied to a rockburst that occurred in 2020 in the Conveyor Belt tunnel (TC) of the Andes Norte Project, El Teniente Division, Codelco (Chile). The event reached a local magnitude of Mw = 1.7 and caused significant damage to tunnel support systems. Results indicate that the rockburst was associated with excavation- and blasting-induced stress redistribution, leading to the activation of a sub-horizontal rupture plane and subsequent damage propagation toward the excavated tunnel. The methodology provides a transparent and adaptable analytical framework for integrating multidisciplinary data into a coherent causal interpretation. Although demonstrated using a competent and brittle rock mass, the framework can be adapted to other deep tunneling projects under high-stress conditions by adjusting the governing parameters according to site-specific geological, geomechanical, and operational characteristics. The proposed approach supports improved understanding of rockburst mechanisms and informed decision-making for seismic risk management in deep underground excavations. Full article

In submerged tunnel construction, the installation accuracy of pre-embedded components directly impacts subsequent engineering quality and operational safety. However, current on-site construction still primarily relies on manual measurement and two-dimensional drawings for guidance, resulting in significant positioning errors, delayed information transmission, and inefficient installation inspections. To enhance the digitalization and intelligence of submerged tunnel construction, this paper proposes a BIM- and AR-based human–machine collaborative management method for pre-embedded components in submerged tunnel segments. This method establishes a site-wide panoramic model as its foundation, enabling intelligent matching of component model geometry and semantic information. It facilitates human–machine interaction applications such as AR-based visualization for positioning and verification of pre-embedded components, information querying, and progress simulation. Additionally, the system supports collaborative operations across multiple terminal devices, ensuring information consistency and task synchronization among diverse roles. Its application in the Mingzhu Bay Submerged Tunnel Project in Nansha, Guangzhou, validates the feasibility and practical utility of the proposed workflow in a pilot case, and indicates potential for further validation in broader construction settings. 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

Accurate scene understanding is crucial for the safe and stable operation of underground utility tunnel inspections. Addressing the characteristics of low-light environments, this paper proposes an object recognition method based on low-light enhanced image semantic segmentation. Secondly, by analyzing image data from real underground utility tunnel environments, the visual language model undergoes scene image fine-tuning to generate scene description text. Thirdly, integrating these functionalities into the system enables real-time processing of captured images and generation of scene understanding results. In practical applications, the average accuracy of the improved recognition model increased by nearly 1% compared to the original model, while the accuracy and recall of the fine-tuned visual-language model surpassed the untuned model by over 70%. Full article

Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring. Full article

Accurate and timely identification of cutter anomalies is crucial for ensuring the safety and efficiency of shield tunneling. To address the issues of poor timeliness and high costs associated with traditional periodic manual inspection methods, this study establishes a cutter anomaly identification model based on the BO-Light GBM algorithm, focusing on slightly weathered metamorphic rock formations. Six parameters closely related to the tunneling state were selected to construct the feature set, and one-class support vector machines (SVMs) were employed to remove anomalous samples. On this basis, a baseline Light GBM model with preset hyperparameters was developed, achieving a preliminary accuracy of 96.04%. Further hyperparameter tuning using Bayesian optimization boosted the overall accuracy of the final BO-Light GBM model to 99.40% while improving training efficiency by approximately 50% compared to exhaustive grid search. Interpretability analysis conducted via SHAP values revealed that chamber pressure, cutterhead rotation speed, total thrust, and cutterhead torque were the primary contributing features, with patterns consistent with actual tunneling conditions, confirming the accuracy of the model’s predictions. The research outcomes provide valuable theoretical guidance and technical support for similar engineering applications. Full article

Water leakage in subway tunnel linings poses significant risks to structural safety and long-term durability, making accurate and efficient leakage detection a critical task. Existing deep learning methods, such as UNet and its variants, often suffer from large parameter sizes and limited ability to capture multi-scale features, which restrict their applicability in real-world tunnel inspection. To address these issues, we propose an Efficient Multi-Scale U-shaped KAN-based Segmentation Network (EMS-UKAN) for detecting water leakage in subway tunnel linings. To reduce computational cost and enable edge-device deployment, the backbone replaces conventional convolutional layers with depthwise separable convolutions, and an Edge-Enhanced Depthwise Separable Convolution Module (EEDM) is incorporated in the decoder to strengthen boundary representation. The PKAN Block is introduced in the bottleneck to enhance nonlinear feature representation and improve the modeling of complex relationships among latent features. In addition, an Adaptive Multi-Scale Feature Extraction Block (AMS Block) is embedded within early skip connections to capture both fine-grained and large-scale leakage features. Extensive experiments on the newly collected Tunnel Water Leakage (TWL) dataset demonstrate that EMS-UKAN outperforms classical models, achieving competitive segmentation performance. In addition, it effectively reduces computational complexity, providing a practical solution for real-world tunnel inspection. Full article