Search Results (686)

To address the lack of comprehensive quality evaluation indicators for heat treatment after bilateral induction hardening of high-speed linear guide rails, this study draws on the concept of geometric tolerance to innovatively propose a quantitative evaluation indicator for the “symmetry” of the hardening layer depth profile, and conducts non-destructive evaluation research based on ultrasonic transverse wave backscattering technology. Aiming at the complex cross-sectional profile of the guide rail and the problem of anisotropic acoustic scattering, a multi-dimensional symmetry characterization framework driven jointly by “local pair-wise tolerance zone constraints” and a “global equivalent case depth metric” was established. This dual-driven evaluation framework effectively eliminates the evaluation loophole of “false symmetry” caused by the mutual cancellation of opposite positive and negative local deviations. By constructing an equivalent hardened layer model based on discrete feature point mapping, the interference of non-parallel complex curved surfaces on traditional continuous B-scan imaging is successfully circumvented, achieving stable characterization of the overall hardening layer coverage under specific process parameters. A 15 MHz water-immersed point-focusing ultrasonic transverse wave oblique incidence detection system was developed, paired with a self-designed spring-loaded passive conformal tracking clamping mechanism for continuous automated scanning. Experimental results demonstrate that the overall equivalent symmetry of the tested guide rail specimens remains above 98%. Verified by the metallographic Vickers hardness gradient method, the equivalent relative error between the ultrasonically measured case depth and the physical case depth is only 1.0% and 1.6%. This proves that this non-destructive evaluation method possesses excellent measurement accuracy and holds significant industrial value for online non-destructive monitoring. Full article

This paper proposes a novel data-driven K-Gap metric method based on the Koopman operator for the detection and isolation of multiplicative faults in high-speed train suspension systems. A systematic comparison is conducted with a data-driven K-Gap approach implemented through the fuzzy modeling framework. First, Takagi–Sugeno (T–S) theory is employed to extend the K-Gap metric for nonlinear dynamic modeling of the suspension system. Subsequently, the Koopman operator framework is introduced to lift the system states into a high-dimensional observable space, enabling a globally linear representation of the system. Building upon Koopman-based stable kernel representation (SKR), a more accurate K-Gap residual metric is constructed. Finally, a unified fault diagnosis scheme is developed with the K-Gap metric as the core indicator, and the two approaches are experimentally compared in terms of their performance in detecting and isolating multiplicative faults. The experimental results demonstrate that the Koopman-based method significantly outperforms the T–S fuzzy model in terms of residual separability, fault classification accuracy, and diagnostic stability, confirming its effectiveness and superiority for fault diagnosis in complex nonlinear systems. Full article

To address the complex and highly variable wheel–rail adhesion behavior on high-speed railway curves, this study establishes a numerical wheel–rail rolling contact model based on starved elastohydrodynamic lubrication (EHL) theory and Herschel–Bulkley rheological characteristics. The model validation yielded RMSE = 0.0228, MAE = 0.0217, MAPE = 11.80%, R² = 0.828, and a 95% confidence interval of the mean residual of −0.0298 to −0.0136. The study focuses on the initial operational phase after application, systematically quantifying the fluid-dynamic regulation mechanisms of water-based friction modifiers once a thin, starved lubricating film has been formed on the rail surface under curving conditions. By analyzing rail profiles (CHN60 and CHN60N), operating parameters, and track geometry, this study shows how adhesion behavior on curved track sections is governed by the coupled effects of contact mechanics and lubrication. As the outer rail superelevation increases from 0 to 70 mm, the adhesion coefficient decreases by approximately 15–25%, mainly because the reduced normal force shifts the wheel–rail interface toward the Stribeck transition regime. Increasing axle load from 14 t to 30 t reduces the dimensionless film thickness, but the enlarged contact area contributes to a more stable adhesion level, with an increase of about 12%. Compared with the CHN60 profile, the CHN60N profile exhibits better geometric conformity, producing a lubricating film that is 10–15% thicker and leading to a lower and more stable adhesion coefficient, decreasing from approximately 0.35 to 0.1. The results also identify a critical lateral displacement of around −4 mm, beyond which the contact radius becomes stable and the adhesion coefficient reaches a minimum plateau. These findings clarify the competing effects of fluid entrainment and metallic asperity contact, and provide quantitative guidance for friction management and friction modifier application on curved track sections. Full article

Large-scale infrastructure projects routinely divide construction responsibilities across several specialized contractors whose scopes are simultaneously independent and mutually reliant, producing boundary zones where design changes, remedial obligations, and contractual disputes repeatedly emerge. This study examines how interface-related risks are distributed between civil construction contractors and core system contractors, drawing on fieldwork conducted within the Taiwan High Speed Rail (THSR) project. Questionnaire surveys were administered to 38 practitioners who held direct THSR involvement or comparable experience in analogous multi-party infrastructure projects, yielding a valid response rate of 63.3%. Using a weighted influence scoring approach, 37 risk factors grouped into seven interface categories were evaluated. Results show that Variation (top-ranked item A07, score 290/300), Care of Works (B01, 284/300), and Cooperation and Coordination (D04, 285/300) represent the three most consequential risk domains. Analysis of risk-initiation patterns indicates that project owners are the predominant source of variation-related risks, while core system contractors most frequently trigger care-of-works incidents. Structured comparison of FIDIC (1995), AIA/A201 (1997), NEC/ECC (1995), ENAA (1996), and THSR contractual documents uncovers a shared shortcoming: no examined standard form contains explicit provisions tying risk liability to the party whose conduct gave rise to the risk. Evidence from three documented THSR dispute cases is consistent with the survey data and illustrates the real-world consequences of this drafting gap. On this basis, the study proposes a risk-trigger principle as a potential framework for improving interface risk allocation in future multi-party infrastructure contracts. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

To address the complex dynamic behavior of four-axis precision motion platforms under high-speed and high-acceleration conditions, as well as the difficulty of traditional modeling methods in balancing accuracy and efficiency, this paper proposes a data/model-driven dynamic modeling and analysis method that integrates generalized receptance coupling substructure analysis (GRCSA) with artificial intelligence (AI) algorithms. Based on the GRCSA theory, the initial analytical framework of the dynamic model of the precision motion platform is established, and the frequency response functions (FRFs) of the substructure and interface are preliminarily obtained. On this basis, the nonlinear prediction model of the dynamic parameters of the interface driving direction is established by using the AI algorithm, enabling fast and accurate prediction of the dynamic characteristics of the interface under different servo control parameters in the guide rail driving direction. Finally, based on the data/model-driven dynamic modeling and analysis method, the interface control parameters are optimized. The interface and substructure parameters are modified to reduce the prediction error of the FRFs from 3.50% to 2.47%. This method can achieve the prediction error of the dynamic characteristics of the interface under different control parameters of about 2.5%. Full article

This paper presents a comparative analysis of selected economic indicators within the Italian railway passenger transport sector during the 2010–2024 period. Characterized by high-speed rail (HSR) saturation and advanced market liberalization, the Italian railway system serves as a reference model for investigating structural shifts within mature transport networks. The study aims to quantify the dynamics of transport performance through a synthesis of multiple analytical dimensions: passenger volume, transport performance (passenger-kilometers), modal split, average transport distances, and indicators of general and dynamic population mobility. The methodological framework is based on the application of chain and base indices, enabling the precise identification of cyclical fluctuations, exogenous disruptions (primarily the impact of the COVID-19 pandemic), and the subsequent degree of systemic resilience. The analysis suggests a significant shift in demand composition after 2014, characterized by an expansion of short- and medium-distance segments alongside a transformation in travel behavior. The research findings determine the correlation between infrastructure investment and the actual positioning of rail transport within a multimodal system. This work provides an analytical foundation for strategic planning in transport policy and sustainable mobility within the context of European transport integration. Moreover, these insights are practically applicable for transport operators and planners in forecasting demand, optimizing network capacity, and enhancing infrastructure resilience against future exogenous shocks. Full article

Train brake systems are characterized by strong friction and open-system features during the service process. Low environmental temperatures significantly affect the contact interface and the attrition characteristics of the braking frictional couple, thus intensifying friction-induced vibration and threatening operational safety. To elucidate the impact of environmental temperature on the frictional vibration characteristics of train brake systems, braking deceleration tests under different environmental temperatures were first conducted to obtain the evolution of vibration, noise, and friction coefficient with environmental temperature and brake disc rotational speed. Then, the Stribeck friction parameters under different environmental temperatures were identified using a genetic algorithm. On this basis, a brake system dynamic model was developed, incorporating disc–pad friction, wheel–rail adhesion, and the relative torsion between the brake disc and the wheelset, enabling accurate examination of the vibrational behaviour arising from friction under different environmental temperatures. And the dynamic relationship among environmental temperature, interface friction parameters, and vibration characteristics of the brake system during braking deceleration was elucidated. The findings indicate that as the environmental temperature decreases, the dynamic friction coefficient increases during the relatively high-speed braking phase, intensifying high-frequency unstable vibrations of the braking assembly. During the relatively low-speed braking phase, the friction coefficient exhibits an obvious negative-slope relationship with vehicle speed that means the friction coefficient increases as the speed decreases, and this negative slope effect is enhanced under low-temperature conditions. Consequently, it triggers intense stick–slip motion at the disc–pad interface and even severe vibrations of various components in the brake system, leading to a sudden increase in vibration intensity in the relatively low-speed range. Full article

Despite the widespread adoption of model predictive control (MPC) in railway research, the integration of intelligent learning mechanisms into train control systems operating under moving block signaling remains limited, particularly in approaches that preserve constraint satisfaction and industrial feasibility. To address this gap, this paper presents a novel learning-based model predictive control (LMPC) framework for high-speed train control under the moving block signaling principle. Moving block signaling dynamically enforces safe inter-train separation based on the absolute braking distance, imposing stringent safety, comfort, and performance constraints on train operation. The proposed LMPC exploits the repetitive nature of railway operations by progressively improving its control policy through the incorporation of historical operational data into the terminal set of the optimization problem. This learning capability enables the controller to optimize train behavior on a given line while pursuing different control objectives, namely maximum-speed operation for leading trains and minimum safe inter-train separation for following trains, in full compliance with signaling requirements, speed limits, actuator constraints, and comfort-related jerk bounds. Simulation results on a representative high-speed line show that, compared with a conventional non-learning MPC, the proposed LMPC achieves a measurable reduction in traction-related energy consumption while maintaining comparable speed profiles, travel times, and strict constraint satisfaction. These improvements are achieved through a single software-level modification of the train control algorithm, without requiring additional onboard hardware or infrastructure upgrades, positioning the proposed LMPC as a promising and practically viable solution for energy-efficient deployment in high-speed railway operations. Full article

With the integrated development of high-speed railways and urban underground rail transit, large high-speed railway station buildings are often seamlessly connected or even co-constructed with subway structures, forming a complex structural system that integrates high-speed rail, subway, and station buildings. To investigate the dynamic performance of such “ integrated station-bridge” station buildings constructed with subways, this paper takes Yichang North Station as an engineering case study and examines its vertical dynamic characteristics under multi-source train-induced loads. The station adopts a structural configuration where the station tracks are fully integrated with the station building, while the main lines are separated from it. To accurately simulate the entire process of train operation, this study established a refined “train-track-station” spatially coupled dynamics model that incorporates high-speed and subway trains, tracks, and the station structure. Based on this model, various operational scenarios were systematically analyzed, including high-speed trains passing at different speeds, parallel operation of multiple train lines, and combined operation of high-speed and subway trains. The results demonstrate that, when single or multiple high-speed train lines pass through the station at the design entry speed of 80 km/h, the vertical vibration acceleration of the elevated waiting level meets human comfort standards. The train-induced vibration response is transmitted and superimposed along the “column–beam–slab” path, resulting in localized acceleration peaks at the mid-span regions of beams and slabs directly above the tracks. Second, the impact of subway train operation alone on the vibration of the elevated level is significantly weaker than that of high-speed trains. Furthermore, under combined high-speed and subway train operations, the additional vibration contribution from subway trains shows a decreasing trend as the number of simultaneously operating high-speed train lines increases. The findings of this study validate the effectiveness of the structural design of Yichang North Station in terms of train operational safety and passenger waiting comfort. The revealed patterns of multi-source vibration transmission and superposition can provide important theoretical and numerical references for the dynamic optimization design and vibration control of similar integrated transportation hub structures. Full article

We present a 3D laser-scanning method for the fast, accurate dimensional inspection of large high-speed-rail precast box girders. The pipeline uses low-pass filtering plus sequential registration to suppress noise, and voxel filtering with curvature-aware enhancement to reduce point cloud size by 3–5× while preserving key geometry. Reconstruction employs K-nearest-neighbors and PCA to detect boundaries and curvature jumps, B-spline fitting with moving least squares for surface completion, and CSS corner detection to extract key dimensions at millimeter precision. Field tests report absolute errors ≤ 2.0 mm versus manual measurement, validating the method for automated, digital acceptance. Full article

To enhance the overall capacity utilization of HSR networks and promote balanced capacity utilization across different lines, this study considers optimizing the capacity utilization of HSR networks by adjusting the travel routes of direct trains. Based on the arc-path modeling framework for multi-commodity network flows, the problem was formulated as a dual-objective mixed-integer linear programming model to minimize the total travel time and enhance the balance of capacity utilization across the railway network, with consideration of the unique train routes, the matching of paired train routes, section capacity, operating mileage, and maximum operating time per train trip limitations. Then, the model was transformed into a single-objective function using the weighted-sum approach. A case study based on actual data from China’s HSR network and train line plans in early 2024 was conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method can control the total travel time while significantly reducing the number of sections with over-utilized capacity and improve the balance of railway network capacity utilization. The method can thus provide decision support for the efficient utilization of HSR network capacity. Full article

This paper examines how local firm entry affects air pollution and whether the Air Pollution Prevention and Control Action Plan (APPCAP) changes this relationship. Using a county–month panel for 2010–2020, we match the Chinese Industrial and Commercial Enterprise Registration Database with county-level monthly PM2.5 data to measure new firm entry and its sectoral composition. To address the potential endogeneity of firm entry, we use the opening of high-speed rail as an instrumental variable. The results show that firm entry significantly increases county-level PM2.5 concentrations. This effect is highly heterogeneous across industries, with stronger pollution effects in sectors such as wholesale and retail, manufacturing, and accommodation and catering. We further find that the APPCAP significantly weakens the positive effect of firm entry on air pollution. Additional evidence suggests that the policy improves air quality not only by tightening environmental constraints, but also by shifting firm entry toward relatively cleaner industries. This paper explains the environmental consequences of local economic expansion from the perspective of incremental firm entry and provides new evidence on the joint role of environmental regulation and industrial restructuring in air pollution control. Full article

With the rapid development of rail transit, environmental vibrations caused by subway vehicle loads have garnered increasing attention. This study employs a three-dimensional finite element–infinite element coupling method to establish an integrated numerical model of the vehicle–track–tunnel–ground coupled system. The vehicle loads are obtained through the simulation of a physical vehicle model, incorporating the effects of track irregularities as excitation sources. Based on this model, the dynamic response characteristics of subway-induced vibrations within structural components and geological layers are systematically investigated. The results show that the vertical vibration response in the surrounding ground is most pronounced, with the vertical acceleration distribution following the pattern: tunnel bottom > tunnel crown > tunnel sides. Furthermore, high-frequency vibration components attenuate rapidly within one tunnel diameter. As vehicle speed increases, the vibration response in the surrounding ground significantly intensifies, indicating that dynamic effects are more pronounced under high-speed operation. Meanwhile, the vibration responses in far-field regions tend to converge. This study also finds that an acceleration amplification zone appears in the low-frequency band (0–5 Hz) during vibration propagation. Additionally, the near-field tunnel response exhibits energy concentration around 35 Hz before attenuation, which is significantly higher than the dominant frequency after propagation to the far field. These findings provide important insights for understanding the propagation mechanisms of subway-induced vibrations and offer a solid basis on which to develop effective vibration control strategies. Full article

With the rapid expansion of high-speed railways, maintaining track structural health is vital for modern railway systems. Although deep learning has improved defect detection, models still face problems such as varying defect scales, severe background noise (e.g., lubricant residues and ferruginous oxidation), and irregular defect boundaries. To solve these problems, we introduce a new network named Rail-Adaptive-RCNN (RA-RCNN). It uses a Large Selective Kernel (LSK) backbone to dynamically adjust the Effective Receptive Field (ERF) for capturing periodic corrugation. We also added an Efficient Multi-Scale Attention (EMA) module that purifies features by suppressing noise without lowering dimensions. Finally, combining Scylla-IoU (SIoU) Loss with K-means clustering optimizes the regression of odd-shaped defects. Our experiments indicate that RA-RCNN reaches a mean Average Precision (mAP_0.5) of 86.2%, outperforming the baseline Faster R-CNN by 8.8%. Corrugation detection specifically reached 91.4%. With a processing speed of 26 FPS, this method effectively meets the practical needs of real-time automated railway maintenance. Full article