Search Results (1,375)

Extreme pantograph–catenary contact force (PCCF) oscillations pose a serious challenge to the stable coupling between pantograph and catenary in high-speed railway systems. This paper introduces an active compensation control framework CPO-LQR-BC-SAC, which combines optimized Linear Quadratic Regulator (LQR) baseline control with behavior cloning (BC) and Soft Actor-Critic (SAC) deep reinforcement learning. First, the Crowned Porcupine Optimization algorithm (CPO) is used to offline tune the LQR weighting matrix, producing a high-performance CPO-LQR controller that significantly reduces PCCF fluctuation. Next, a dual model-based offline control law provides “expert” adjustments that further suppress extreme contact force values. Observing that superimposing these offline-tuned actions onto real-time CPO-LQR outputs yields further suppression gains, we developed the BC-SAC compensatory controller to provide corrective control actions. In this scheme, expert actions guide the SAC policy via a behavior cloning loss term in its loss function, and a decaying imitation weight ensures a balance between imitation and exploration. Simulation results demonstrate that, compared to both CPO-LQR and the idealized offline control law, the proposed CPO-LQR-BC-SAC framework achieves over 77% reduction in PCCF standard deviation and exhibits the ability to generalize across different pantograph types, confirming its effectiveness and robustness as a practical solution for mitigating extreme PCCF oscillations. Full article

In this paper, the concept of symmetry is utilized to inform the structural design of our multi-sensor fusion framework—that is, the hierarchical feature extraction and spatial–temporal correlation modeling exhibit symmetrical properties across sensor nodes and temporal scales. Monitoring bearing temperature in high-speed train bogies is crucial for assessing system health and ensuring operational safety. Accurate temperature prediction facilitates proactive maintenance. However, existing models struggle to capture multi-scale temporal patterns, long-term dependencies, and spatial correlations among bearings, and they often overlook varying operating conditions. To address these challenges and enhance prediction accuracy in real-world operations, this study proposes MSC-Ada-MTL, a novel framework that integrates multi-scale feature extraction and operating condition recognition through adaptive multi-task learning. The approach employs multi-scale hierarchical temporal networks (MSHNets) to capture temporal features across different scales from multiple bogie sensors. A speed-based recognition strategy classifies operating conditions to enhance model reliability and simplify prediction tasks. By leveraging multi-task learning, the framework simultaneously models temporal dynamics and spatial correlations, creating a comprehensive prediction model. Validation and ablation experiments demonstrate significant improvements in prediction accuracy and robustness across diverse operating scenarios. The proposed method effectively addresses the limitations of existing approaches by synergistically combining temporal multi-scale analysis, operational condition awareness, and spatial–temporal relationship modeling, providing enhanced adaptability for real-world railway maintenance applications. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

The time dimension critically shapes decision-making and conflict evolution in real-world scenarios. This paper extends the Graph Model for Conflict Resolution (GMCR) framework by integrating time attributes, proposing a novel Time-Sensitive GMCR (TSGMCR) methodology that supports concurrent moves by multiple decision-makers (DMs). Within TSGMCR, we define new stability concepts and implement comparative analysis. The methodology is applied to the Jakarta–Bandung high-speed railway project conflict, demonstrating its effectiveness in resolving complex real-world conflicts and identifying beneficial coalition formations. Full article

We present a straightforward emulsion-induced interfacial anisotropic assembly method for in- situ synthesis of bowl-shaped, self-encapsulated mesoporous polydopamine (BMPDA) nanocontainers (M-M@P) loaded with 2-mercaptobenzimidazole (2-MBI). Results showed that the loading capacity of the bowl-shaped mesoporous polydopamine reaches 24 wt.%. The M-M@P exhibits a cumulative MBI release of 91.61% after immersion in a 3.5 wt.% NaCl solution at pH = 2 for 24 h, accompanied by a corrosion inhibition efficiency of 95.54%. Additionally, the acid-responsive M-M@P not only enables controlled release of MBI but also synergistically promotes the formation of a protective film on the carbon steel substrate via the chelation of PDA-Fe³⁺, thereby enhancing the self-healing performance of waterborne epoxy coatings. Full article

The existing analytical methods lack a reasonable explanation for the cracking and deformation response mechanism of two-way reinforced concrete (RC) slabs under combined horizontal and vertical loads during a fire. In addition, there is a lack of comparative studies on different boundary conditions. Therefore, solid finite-element models were established using ABAQUS 6.14 software to simulate the behavior of two-way RC slabs under combined horizontal and vertical loads in fire. The models considered two different support conditions: four edges simply supported (FSS) and adjacent edges simply supported and adjacent edges quasi-fixed (ASSAQF). Based on experimental model verification, mechanical and parametric analyses were performed to further investigate the deflection, stress variation characteristics, and mechanical response of a concrete slab and reinforcements. The results show that (1) The stress redistribution process of two-way RC slabs under combined horizontal and vertical loads with these two support conditions (FSS and ASSAQF) during fire undergoes four stages: elastic, elastic–plastic, plastic, and tensile cracking. (2) Increasing the horizontal load, vertical load level, and length–width ratio and decreasing the slab thickness all shorten the fire resistance to a certain extent. (3) Compared to slabs with FSS, the stronger support condition of slabs with ASSAQF significantly prolongs the duration of the inverted arch effect stage, resulting in a superior fire resistance, with the fire resistance performance improved by 11–59%. Full article

Slope landslide risk presents a critical challenge throughout high-speed railway construction and operation. Precise detection of sliding surfaces is essential for disaster prevention. This study develops an electrical detection method using complex electrode arrays, specifically addressing high-speed railway slope exploration constraints including confined spaces, significant investigation depths, and complex terrain. Numerical simulations analyzed the electric field distribution characteristics of power supply electrodes under various spatial constraints (half-space and full-space), revealing resolution differences between power supply combinations for target areas. Further comparative numerical modeling demonstrated that complex electrode arrays significantly enhance imaging quality over simple arrays in complex terrain. Finally, field validation confirmed the high reliability of complex observation systems for detecting slip surfaces along high-speed railway slopes. Therefore, under complex terrain conditions, utilizing complex observation systems to acquire multi-dimensional spatial data, integrated with topography-incorporated inversion technology, enables precise slip surface detection. This approach provides a reliable method for geological hazard mitigation in high-speed railway operations. Full article

This study aims to clarify the temperature-dependent degradation mechanisms of the steel–concrete interface in NaCl solution environments at the nanoscale, focusing on the key components of calcium silicate hydrate (C-S-H, the primary hydration product of cement) and iron oxyhydroxide (γ-FeOOH, a critical component of steel passive films in highly alkaline environments). Using Materials Studio software (2023) and molecular dynamics simulations, the evolution of the interface’s performance under temperatures ranging from 300 K to 390 K (corresponding to 27 °C to 117 °C) is systematically investigated. The results reveal that elevated temperatures degrade the performance of C-S-H/γ-FeOOH interfaces through three main mechanisms: (1) The stability of the hydration shell around aggressive ions is weakened, enabling these ions to occupy the coordination positions of calcium ions on the interface and form stable ion pairs with surface calcium ions, thereby weakening interfacial bonding. (2) The mobility of surface calcium ions is enhanced, reducing the strength of the interaction of ion pairs and diminishing the mediating role of calcium ions in connecting the C-S-H and γ-FeOOH phases. (3) Hydrogen bond stability at the interface decreases, as indicated by reduced hydrogen bond angles and numbers, coupled with increased hydrogen bond lengths. The above three reasons lead to a decrease in adsorption energy in the C-S-H/γ-FeOOH interface, which degrades the interface bond’s performance. Full article

With sub-millimeter deformation capture capability, InSAR technology has become an important tool for surface deformation monitoring. However, it is still limited by interferences like land subsidence and bridge deformation in long-term linear engineering monitoring, failing to accurately identify track deformation. Based on RadarSAT-2 and Sentinel-1A satellite data from 2013 to 2023, this study uses time-series InSAR technology (PS-InSAR) to accurately invert the track deformation information of the Beijing–Tianjin Intercity Railway (Beijing section) in the past decade. Key findings demonstrate (1) rigorous groundwater policies (extraction bans and artificial recharge) drove up to 48% regional subsidence mitigation in Chaoyang–Tongzhou, with synchronous track deformation exhibiting 0.6‰ spatial gradient; (2) critical differential subsidence identified at DK11–DK23, where maximum annual settlement decreased from 110 to 49.7 mm; (3) XGBoost-SHAP modeling revealed dynamic driver shifts: confined aquifer depletion dominated in 2015 (>60%), transitioned to delayed consolidation in 2018 (45%), and culminated in phreatic recovery–compressible layer coupling by 2022 (55%). External factors (tectonic/urban loads) played secondary roles. The rise in groundwater levels induces soil dilatancy, while the residual deformation in cohesive soils—exhibiting hysteresis relative to groundwater fluctuations—manifests as surface subsidence deceleration rather than rebound. This study provides a scientific basis for in-depth understanding of the differential subsidence mechanism along high-speed railways and disaster prevention and control. Full article

Rail corrugation and roughness represent typical irregularities on railway and tramway tracks, which cause increased dynamic forces, high-frequency vibrations, reduced riding comfort, shorter track lifespan, higher maintenance costs, and increased noise levels. Roughness and corrugation can be measured by evaluating the unevenness of the rail longitudinal running surface, which can be conducted using handheld devices or trolleys (directly on the track). Alternatively, vehicle or track-based indirect methods offer practical solutions for determining the condition of the rail running surface. This paper presents a methodology for rail corrugation and roughness evaluation, using bogie frame vibration data from an instrumented in-service tramway vehicle operating on Zagreb’s tramway network. Furthermore, it investigates the effects of various factors on the evaluation method, including wheel roughness, lateral positioning, signal processing methods, horizontal geometry, wheel–rail contact force, and tramway vehicle vibroacoustic characteristics. It was concluded that a simplified methodology that did not include transfer functions or wheel roughness measurements yielded relatively good results for evaluating rail corrugation and roughness across several wavelength bands. To improve the presented methodology, future research should assess the vehicle’s vibroacoustic characteristics with experimental hammer impact tests, measure the influence of wheel roughness on wheel–rail contact and bogie vibrations, and refine the measurement campaign by increasing test runs, limiting speed variation, and conducting controlled tests. Full article

The rapid development of intelligent high-speed railways (HSRs) has significantly improved the transportation efficiency of modern transit systems, while also imposing higher bandwidth demands on mobile communication systems. Free-space optical (FSO) communication technology, as a promising solution, can effectively meet the high-speed data transmission requirements in intelligent HSR scenarios. In this paper, we consider an intelligent HSR system based on beamwidth-adaptive FSO communication and investigate the coverage performance of the system. Different from the circular cells used in traditional radio frequency wireless communication systems, this paper focuses on the coverage problem of narrow-strip-shaped cells in HSR systems based on FSO communication. When the transmitter emits a wide beam, the channel gain includes geometric loss, atmospheric attenuation, and atmospheric turbulence. When the transmitter emits a narrow beam, the channel gain includes pointing error, atmospheric attenuation, and atmospheric turbulence. To adapt the width of the transmitter’s beam, we propose a beamwidth-adaptive HSR system and a beamwidth-adaptive method. Furthermore, we derive closed-form expressions of the edge coverage probability (ECP) and the percentage of cell coverage area (CCA), where the ECP is the probability that the received signal-to-noise ratio at the cell edge is greater than or equal to a given threshold, and the percentage of CCA dictates the percentage of locations within a cell that are not in outage. The accuracy of the derived theoretical expressions is validated through Monte-Carlo simulations. The average relative error of the ECP between theoretical and simulation results is only 0.035%, and the corresponding error of the percentage of CCA is 0.087%. In addition, the impacts of factors such as cell diameter, transmission power, signal-to-noise ratio threshold, and weather visibility on coverage performance are also discussed. Full article

Traditional methods for predicting surrounding rock grades ahead of tunnel faces encounter challenges: image-based approaches are susceptible to environmental interference, while parameter-based classification may disrupt construction. This study proposes an intelligent rock grade identification method by integrating longitudinal displacement profile (LDP) evolution patterns with deep learning. First, the numerical model was validated against V-D theoretical curves, and LDP evolution laws were systematically analyzed for three rock types (GSI = 15, 30, 50) under nine geological combinations. The results indicate that (1) homogeneous strata exhibit deformation peaks followed by declines; (2) GSI = 15 strata show significantly larger deformations; and (3) stratified schemes display pre-interface deformation peaks and post-interface deformation controlled by subsequent lithology. A novel hybrid neural network was developed to classify strata using LDP curves as input. The model achieved 93.25% training accuracy and 91.20% validation accuracy. Ablation experiments demonstrated their superiority over the other four models with partial module deletions, achieving improvements in test accuracy of 3.24%, 3.08%, 4.16%, and 6.48%, respectively, compared to those models. This lightweight solution effectively overcomes the limitations of manual expertise dependency in conventional models and environmental sensitivity in visual methods. By synergizing LDP evolution analysis with deep learning, this framework provides a reliable approach for real-time rock grade prediction during tunnel advancement. Full article

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article

The operation of heavy-haul railway trains with large loads results in significant cracking issues in reinforced concrete beams. Atmospheric carbon dioxide, oxygen, and moisture from the atmosphere penetrate into the beam interior through these cracks, accelerating the carbonation of the concrete and the corrosion of the steel bars. The rust-induced expansion of steel bars further exacerbates the cracking of the beam. The interaction between environmental factors and beam cracks leads to a rapid decline in the durability of the beam. To address this issue, a multi-physics field coupling durability assessment method was proposed, considering concrete beam cracking, concrete carbonation, and steel bar corrosion. The interaction among these three factors is achieved through sequential coupling, using crack width, carbonation passivation time, and steel bar corrosion rate as interaction parameters. Using this method, the deterioration morphology and stiffness degradation laws of 8 m reinforced concrete beams under different load conditions, including those of heavy and light trains in heavy-haul railways, are compared and assessed. The analysis reveals that within a 100-year service cycle, the maximum relative stiffness reduction for beams on the heavy train line is 20.0%, whereas for the light train line, it is only 7.4%. The degree of structural stiffness degradation is closely related to operational load levels, and beam cracking plays a critical role in this difference. Full article

The extended operation of high-speed railways has led to an increased incidence of tunnel lining defects, with falling debris posing a significant safety threat. Within tunnels, single-train passage and trains-crossing events constitute the most frequent operational scenarios, both generating extreme aerodynamic environments that alter debris trajectories from free fall. To systematically investigate the aerodynamic differences and underlying mechanisms governing falling debris behavior under these two distinct conditions, a three-dimensional computational fluid dynamics (CFD) model (debris–air–tunnel–train) was developed using an improved delayed detached eddy simulation (IDDES) turbulence model. Comparative analyses focused on the translational and rotational motions as well as the aerodynamic load coefficients of the debris in both single-train and trains-crossing scenarios. The mechanisms driving the changes in debris aerodynamic behavior are elucidated. Findings reveal that under single-train operation, falling debris travels a greater distance compared with trains-crossing conditions. Specifically, at train speeds ranging from 250–350 km/h, the average flight distances of falling debris in the X and Z directions under single-train conditions surpass those under trains crossing conditions by 10.3 and 5.5 times, respectively. At a train speed of 300 km/h, the impulse of C_Fx and C_Fz under single-train conditions is 8.6 and 4.5 times greater than under trains-crossing conditions, consequently leading to the observed reduction in flight distance. Under the conditions of trains crossing, the falling debris is situated between the two trains, and although the wind speed is low, the flow field exhibits instability. This is the primary factor contributing to the reduced flight distance of the falling debris. However, it also leads to more pronounced trajectory deviations and increased speed fluctuations under intersection conditions. The relative velocity (CRV) on the falling debris surface is diminished, resulting in smaller-scale vortex structures that are more numerous. Consequently, the aerodynamic load coefficient is reduced, while the fluctuation range experiences an increase. Full article