Search Results (96)

This paper proposes a multi-sensor data-fusion-based method for real-scene 3D reconstruction and digital twin visualization of coal mine tunnels, aiming to address issues such as low accuracy in non-photorealistic modeling and difficulties in feature object recognition during traditional coal mine digitization processes. The research employs cubemap-based mapping technology to project acquired real-time tunnel images onto six faces of a cube, combined with navigation information, pose data, and synchronously acquired point cloud data to achieve spatial alignment and data fusion. On this basis, inner/outer corner detection algorithms are utilized for precise image segmentation, and a point cloud region growing algorithm integrated with information entropy optimization is proposed to realize complete recognition and segmentation of tunnel planes (e.g., roof, floor, left/right sidewalls) and high-curvature feature objects (e.g., ventilation ducts). Furthermore, geometric dimensions extracted from segmentation results are used to construct 3D models, and real-scene images are mapped onto model surfaces via UV (U and V axes of texture coordinate) texture mapping technology, generating digital twin models with authentic texture details. Experimental validation demonstrates that the method performs excellently in both simulated and real coal mine environments, with models capable of faithfully reproducing tunnel spatial layouts and detailed features while supporting multi-view visualization (e.g., bottom view, left/right rotated views, front view). This approach provides efficient and precise technical support for digital twin construction, fine-grained structural modeling, and safety monitoring of coal mine tunnels, significantly enhancing the accuracy and practicality of photorealistic 3D modeling in intelligent mining applications. Full article

As an emerging technology, Reconfigurable Intelligent Surfaces (RIS) offers an efficient communication performance optimization solution for the complex and spatially constrained environment of coal mines by effectively controlling signal-propagation paths. This study investigates the channel attenuation characteristics of a semi-circular arch coal-mine tunnel with a dual RIS reflection link. By jointly optimizing the base-station beamforming matrix and the RIS phase-shift matrix, an improved Twin Delayed Deep Deterministic Policy Gradient (TD3)-based algorithm with a Noise Fading (TD3-NF) propagation optimization scheme is proposed, effectively improving the sum rate of the coal-mine wireless communication system. Simulation results show that when the transmit power is 38 dBm, the average link rate of the system reaches 11.1 bps/Hz, representing a 29.07% improvement compared to Deep Deterministic Policy Gradient (DDPG). The average sum rate of the 8 × 8 structure RIS is 3.3 bps/Hz higher than that of the 4 × 4 structure. The research findings provide new solutions for optimizing mine communication quality and applying artificial intelligence technology in complex environments. Full article

The adverse propagation environment in underground coal mine tunnels caused by enclosed spaces, rough surfaces, and dense scatterers severely degrades reliable wireless signal transmission, which further impedes the deployment of IoT applications such as gas monitors and personnel positioning terminals. However, the conventional power enhancement solutions are infeasible for the underground coal mine scenario due to strict explosion-proof safety regulations and battery-powered IoT devices. To address this challenge, we propose singular value decomposition-based Lagrangian optimization (SVD-LOP) to minimize transmit power at the mining base station (MBS) for IRS-assisted coal mine wireless communication systems. In particular, we first establish a three-dimensional twin cluster geometry-based stochastic model (3D-TCGBSM) to accurately characterize the underground coal mine channel. On this basis, we formulate the MBS transmit power minimization problem constrained by user signal-to-noise ratio (SNR) target and IRS phase shifts. To solve this non-convex problem, we propose the SVD-LOP algorithm that performs SVD on the channel matrix to decouple the complex channel coupling and introduces the Lagrange multipliers. Furthermore, we develop a low-complexity successive convex approximation (LC-SCA) algorithm to reduce computational complexity, which constructs a convex approximation of the objective function based on a first-order Taylor expansion and enables suboptimal solutions. Simulation results demonstrate that the proposed SVD-LOP and LC-SCA algorithms achieve transmit power peaks of $20.8\mathrm{dBm}$ and $21.4\mathrm{dBm}$, respectively, which are slightly lower than the $21.8\mathrm{dBm}$ observed for the SDR algorithm. It is evident that these algorithms remain well below the explosion-proof safety threshold, which achieves significant power reduction. However, computational complexity analysis reveals that the proposed SVD-LOP and LC-SCA algorithms achieve $\mathcal{O}\left(N^3\right)$ and $\mathcal{O}\left(N^2\right)$ respectively, which offers substantial reductions compared to the SDR algorithm’s $\mathcal{O}\left(N^7\right)$. Moreover, both proposed algorithms exhibit robust convergence across varying user SNR targets while maintaining stable performance gains under different tunnel roughness scenarios. Full article

Precise maintenance is vital for ensuring the safety of tunnel structures; however, traditional visual inspections are subjective and hazardous. Digital technologies such as LiDAR and imaging offer promising alternatives, but each has complementary limitations in geometric precision and visual representation. This study addresses these limitations by developing a three-dimensional modeling framework that integrates image and point cloud data and evaluates its effectiveness. Terrestrial LiDAR and UAV images were acquired three times over a freeze–thaw cycle at an aging, abandoned tunnel. Based on the data obtained, three types of 3D models were constructed: TLS-based, image-based, and fusion-based. A comparative evaluation results showed that the TLS-based model had excellent geometric accuracy but low resolution due to low point density. The image-based model had high density and excellent resolution but low geometric accuracy. In contrast, the fusion-based model achieved the lowest root mean squared error (RMSE), the highest geometric accuracy, and the highest resolution. Time-series analysis further demonstrated that only the fusion-based model could identify the complex damage progression mechanism in which leakage and icicle formation (visual changes) increased the damaged area by 55.8% (as measured by geometric changes). This also enabled quantitative distinction between active damage (leakage, structural damage) and stable-state damage (spalling, efflorescence, cracks). In conclusion, this study empirically demonstrates the necessity of data fusion for comprehensive tunnel condition diagnosis. It provides a benchmark for evaluating 3D modeling techniques in real-world environments and lays the foundation for digital twin development in data-driven preventive maintenance. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

Accurate and efficient rock mass characterization is essential in geotechnical engineering, yet traditional tunnel face mapping remains time consuming, subjective, and potentially hazardous. Recent advances in digital technologies and AI offer automation opportunities, but many existing solutions are hindered by slow 3D scanning, computationally intensive processing, and limited integration flexibility. This paper presents Tunnel Rapid AI Classification (TRaiC), an open-source MATLAB-based platform for rapid and automated tunnel face mapping. TRaiC integrates single-shot 360° panoramic photography, AI-powered discontinuity detection, 3D textured digital twin generation, rock mass discontinuity characterization, and Retrieval-Augmented Generation with Large Language Models (RAG-LLM) for automated geological interpretation and standardized reporting. The modular eight-stage workflow includes simplified 3D modeling, trace segmentation, 3D joint network analysis, and rock mass classification using RMR, with outputs optimized for Geo-BIM integration. Initial evaluations indicate substantial reductions in processing time and expert assessment workload. Producing a lightweight yet high-fidelity digital twin, TRaiC enables computational efficiency, transparency, and reproducibility, serving as a foundation for future AI-assisted geotechnical engineering research. Its graphical user interface and well-structured open-source code make it accessible to users ranging from beginners to advanced researchers. Full article

The dynamic characteristics of super-large-diameter twin tunnels under train vibration loads have become a critical issue affecting not only the engineering safety of their own tunnels but also adjacent tunnels. A numerical model of super-large-diameter (D = 15.2 m) twin tunnels was established by the discrete element method (DEM) to analyze the static and dynamic responses of adjacent tunnel structures and surroundings under train-induced vibrations. Three parameters were considered: internal walls, absolute and relative spacing, and water pressure. The results indicate that internal walls in super-large twin tunnels can significantly reduce the static and dynamic responses in both the structures and surroundings of the adjacent tunnel. The vehicular lane board (wall2) plays a determinative role, followed by the smoke exhaust board (wall1), while the left and right partition walls (wall3 and wall4) exhibit the least effectiveness. The static–dynamic responses of the liners and surroundings of adjacent tunnels in super-large twin tunnels are significantly greater than those in smaller twin tunnels when the absolute spacing is identical. Moreover, the significant differences in displacement and velocity between the liners and surroundings can lead to cracks, leakage, or even instability. Appropriate water pressure (149 kPa) can effectively mitigate dynamic responses in adjacent tunnel structures and surroundings. The dynamic characteristics of super-large-diameter twin tunnels differ markedly from those of small-diameter twin tunnels, with internal walls, twin tunnel spacing, and water pressure all influencing their static and dynamic behaviors. This study provides theoretical guidance for the design and operation of super-large-diameter twin tunnels. Full article

Urban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their ability to adapt to dynamic construction conditions. This study proposes an integrated framework combining digital twin and knowledge graph technologies to enhance real-time risk assessment and emergency response in tunnel construction. The digital twin continuously integrates real-time monitoring data, including settlement measurements, TBM operational parameters, and structural responses, creating a virtual representation of the tunneling environment. Meanwhile, the knowledge graph structures domain knowledge and applies rule-based reasoning to infer potential hazards, detect abnormal conditions, and suggest mitigation strategies. The proposed approach has been successfully applied to a practical tunnel project in China, where it played a crucial role in emergency response and risk mitigation. By integrating real-time monitoring data with the knowledge-driven reasoning system, the developed framework enabled the early identification of anomalies, rapid risk assessment, and the formulation of effective mitigation strategies, preventing further structural impact. This bidirectional interaction between the digital twin and the knowledge graph ensured that the real-world data informed the automated reasoning, while the inference results were visualized within the digital twin for intuitive decision support. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems. Full article

Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models currently exist, creating uncertainty in the selection of appropriate frameworks. Moreover, no widely accepted standard or reference model has yet been developed in the field of road infrastructure management. Therefore, this study examined the current standards and models employed in the adoption and implementation of DTs in road infrastructure management, focusing on their dimensions (layers) and functional components. A bottom-up approach was adopted by comprehensively reviewing the existing literature on road networks, bridges, tunnels, and other civil infrastructures and urban DTs. Ultimately, a DT framework was developed, comprising five core layers with their respective components and functionalities, to facilitate network-level integrated road infrastructure management. Moreover, the proposed framework’s implementation scenario enhances its applicability in the field. Overall, this study provides valuable insights for researchers and practitioners involved in DT implementation in infrastructure management and supports future standardization efforts in this domain. Full article

Due to the spatial constraints of underground environments, the spacing between dual-line tunnels in urban metro systems is often limited, leading to potential mutual interference during the operation of trains in closely spaced parallel tunnels. In this study, a twin-tunnel model was developed using PFC2D to simulate the variations in displacement, velocity, porosity, and strain of the T2 structure and its surroundings under eight conditions (F_i = 62.4–131.5 kN, i = 1, 2, 3…, 8), elucidating the static and dynamic responses of the adjacent tunnel structure and its surroundings. The results indicate that the vertical response of T2 sleepers is significantly larger than the horizontal response under the same load. Increasing train loads induce non-uniform deformation in T2 liners, and excessive overloading may result in microcracks or structural failure. The velocity and displacement at the ground surface are substantially more significant than those in the surrounding areas closer to the vibration source, primarily due to the surface amplification effect. The surroundings of the adjacent tunnel experience uneven compressive forces, potentially causing liner separation. Under the A₇ condition, the static and dynamic responses of the tunnel structure and its surroundings sharply decreased due to the combined effects of pressure and train load dynamics. This phenomenon is attributed to the interplay between the pressure effect and the dynamic amplification effect of the train load. It is recommended that the operational train load in practical engineering should not exceed the A₄ condition (92.0 kN). This study can provide a reference for analyzing the static and dynamic responses of twin-tunnel structures under metro overloading conditions. Full article

This study presents a real-time monitoring system integrating Building Information Modeling (BIM) and digital twin technology to enhance maintenance efficiency and safety in urban infrastructure. Unlike conventional periodic inspections, which miss dynamic changes and increase costs, this system uses a BIM model at LOD 400 for a solar-powered noise barrier tunnel integrated with the Wansan Tunnel in South Korea. It incorporates IoT sensor data, including vibration, tilt, light, air quality, and water detection, which are synchronized via the Autodesk Forge API, and WebSockets and visualized on a web-based dashboard. A demonstration from 22 October to 7 November 2024 confirmed that this system had stable data transmission, with light sensor rates exceeding 90%, and enabled the detection of anomalies such as irregular illuminance and structural shifts, thereby supporting informed maintenance decisions. While it is proven that BIM–digital twin integration improves NBT management, partial air quality data gaps highlight areas for refinement. This framework lays the groundwork for predictive maintenance through advanced analytics. Full article

This study assesses the wind resistance and vortex-induced vibration (VIV) risks of the Dongzhou River Bridge in China reconstruction during critical construction stages. Computational Fluid Dynamics (CFD) simulations analyzed wind effects when the twin main girders were maximally separated, revealing asymmetric vortex shedding patterns influenced by upstream–downstream aerodynamic interactions. The upstream girder’s wake generated complex flow fields, increasing turbulence on the downstream girder and indicating elevated VIV susceptibility. A 1:50 scale aeroelastic model validated these findings through wind tunnel tests, confirming that CFD-predicted critical VIV wind speeds aligned with experimental observations. Tests identified a distinct “jump-like” vibration mode at specific wind speeds (35–40 m/s full-scale equivalent), characterized by abrupt amplitude escalation rather than gradual growth—a signature of unstable VIV resonance. However, measured amplitudes remained below the 61.5 mm full-scale equivalent safety threshold, confirming that vibrations posed no critical risk. While aerodynamic coupling between girders requires monitoring during cantilever construction, the study concludes that existing control measures ensure safe construction and operation without structural modifications. These results provide actionable guidelines for wind risk mitigation through construction sequencing and real-time wind speed restrictions. Full article

Transportation infrastructure is central to economic development and the daily lives of citizens. However, rapid urbanization, increasing vehicle ownership, and growing concerns about sustainable development have significantly heightened the complexity of managing these systems. Although digital twin (DT) technology holds great promise, most current research focuses on specific areas, lacking a comprehensive framework that spans the entire lifecycle of transportation infrastructure, from planning and construction to operation and maintenance. The technical challenges of integrating different DT systems remain unclear, which to some extent limits the potential of DT technology in the management of transportation infrastructure. To address this gap, this review first summarizes the fundamental concepts and architectures involved in DT systems for transportation infrastructure, such as roads, bridges, tunnels, and hubs. From a lifecycle perspective, DT systems for transportation infrastructure are categorized based on functional scope, data integration methods, and application stages, and their key technologies and basic frameworks are outlined. Subsequently, the potential applications of DT in various lifecycle stages of transportation infrastructure—planning and construction, operation and maintenance, and decommissioning and renewal—are analyzed, and current research progress is reviewed and discussed. Finally, the challenges and future directions for achieving a full lifecycle DT system for transportation infrastructure, encompassing technical, operational, and ethical aspects, are discussed and summarized. The insights gained herein will be valuable for researchers, urban planners, engineers, and policymakers. Full article

The development of urban areas has led to an increase in the use of subsoil for installing transportation networks. These systems usually comprise the construction of side-by-side twin running tunnels built sequentially and in close proximity. Different studies have demonstrated that under such conditions, there is an interaction between tunnels, leading to greater settlements compared with those obtained if the tunnels were excavated separately. Supported by those findings, several analytical methods have been proposed to predict the settlements induced by the excavation of the second tunnel. This paper examines the applicability of these proposals across multiple case studies published in the literature by comparing the analytical predictions with the reported monitoring data of 57 sections. The results indicate that, regardless of the different soil conditions and geometrical characteristics of the tunnels, a Gaussian curve accurately describes the settlements in greenfield conditions and those induced by the second tunnel excavation, although with the curve becoming eccentric in this case. Despite some significant scatter observed, most methods predict the settlements induced by the second tunnel with reasonable accuracy, with Hunt’s method presenting the best fit metrics. The obtained findings confirm that existent methods can be a valid tool to predict the settlements induced by twin tunnelling during the early stages of design, although do also contain limitations and pitfalls that are identified and discussed throughout the paper. Full article

To create a discretized prediction model for the deformation of an adjacent pipeline, the pipeline structure is discretized, the differential equations governing the longitudinal deformation of the pipeline are inferred, and the displacement expressions and the solution methods of the virtual nodes of each unit are provided after discretization. This approach is based on the Pasternak foundation beam theory. It aims to address the issue of the difficulty in predicting the deformation of the adjacent pipeline caused by shield tunneling in a saturated soft ground layer in the Yangtze River Delta. The deformation pattern of the surrounding soil is determined and confirmed through additional numerical simulation, and the discretized prediction model is contrasted with the conventional Winkler foundation beam model and the Pasternak foundation beam model. The findings demonstrate that the discrete prediction model is simpler to solve and more accurately describes the deformation characteristics of the adjacent pipeline as well as the deformation distribution law. The calculated deformation characteristics primarily appear as the adjacent pipeline’s deformation due to the double tunnel boring exhibiting a “mono-peak shape” with a large middle and small ends, which is consistent with the actual situation. The two main factors influencing the pipeline deformation are the shield tunneling distance and pipeline spacing; the former has a negative correlation with the pipeline deformation, while the latter has a positive correlation. This work can offer a straightforward deformation prediction technique for shield tunneling in the Yangtze River Delta’s saturated soft ground next to existing pipelines. Full article