High-speed Railway In China Research Articles

Research on High-Speed Railway Subgrade Design Method Based on Energy Dissipation and Dynamic Stability Characteristics

The subgrade structure of high-speed railways is an important foundation for the safe and smooth operation of high-speed trains, and the scientific design of the subgrade structure provides a fundamental guarantee of its durability and technical economy. As, in the development of high-speed railways in China, higher speeds are being pursued, more requirements have been put forward for the dynamic stability of subgrade structures. To address this issue, this article focuses on the control requirements for the long-term stability of subgrade deformation, and various design methods for high-speed railway subgrade structures are presented. Considering the energy dissipation and dynamic stability characteristics of subgrade filling materials, the dynamic performance of coarse-grained soil filling materials in the bottom layer and graded crushed stones in the surface layer are revealed. The methods for determining the values of dynamic parameters such as the dynamic modulus and damping ratio are provided. Based on the dynamic shakedown theory, the stress–strain hysteresis characteristics of fillers and the variation law of dissipated energy are revealed. The correlation between unit volume dissipated energy and shakedown state under cyclic loading conditions is identified. A criterion for determining the critical shakedown state of high-speed railway subgrade structures based on equivalent unit volume dissipated energy is proposed, and a method for determining the design threshold of dynamic stress and dynamic strain is also proposed. The results show that the shakedown design critical values of equivalent unit volume dissipated energy in the bottom and surface layers of the foundation were between 0.0103~0.0133 kJ/m3 and 0.0121~0.0149 kJ/m3, respectively. The critical dynamic strain range was 0.8 × 10−3~1.3 × 10−3. On this basis, a high-speed railway subgrade design method based on energy dissipation and dynamic shakedown characteristics was developed. The results can provide theoretical support for the design of high-speed railway subgrade structures with different filling material alternatives and control standards.

Overview of China high-speed railway earthquake early-warning system

Overview of China high-speed railway earthquake early-warning system

Incipient Fault Detection and Recognition of China Railway High-Speed (CRH) Suspension System Based on Probabilistic Relevant Principal Component Analysis (PRPCA) and Support Vector Machine (SVM)

As a crucial component of CRH (China Railway High-speed) trains, the safety and stability of the suspension system are of paramount importance to the overall vehicle system. Based on the framework of probabilistic relevant principal component analysis (PRPCA), this paper proposes a novel method for incipient fault diagnosis in the CRH suspension system using PRPCA and support vector machine (SVM). Firstly, simulation data containing multiple types of fault information are obtained from the Simpack2018.1-Matlab2016a/Simulink co-simulation platform. Secondly, the nonlinear PRPCA approach, based on the Wasserstein distance, is employed for fault detection and data preprocessing in the suspension system. Furthermore, SVM is used for fault recognition, and the F1-Measure index is utilized for a comprehensive evaluation to assess the fault diagnosis performance more intuitively. Finally, based on the comparison results with traditional principal component analysis (PCA) and SVM-based methods, the proposed incipient fault diagnosis method demonstrates superior efficiency in fault detection and recognition. However, the proposed method is not very sensitive to sensor faults, and the performance of sensor fault diagnosis needs to be further improved in subsequent research.

Multi-parameter optimization based on a surrogate model for improving vehicle dynamics and reducing wheel wear in high-speed EMUs

With the continuous development of China's high-speed railways, the problems of vehicle dynamics and wheel wear in high-speed electric multiple units (EMUs) are becoming increasingly prominent, and the suspension parameters of these vehicles have a significant effect on improving vehicle dynamics performance and reducing wheel wear. This paper first establishes a vehicle dynamics model using the Ultra-Latin Hypercube sampling method to select suspension parameters, and targets the improvement of vehicle dynamics performance and the reduction of wheel wear, and finally a Kriging Surrogate Model-Compression Particle Swarm Optimization (KSM-CPSO) model used to optimise the suspension parameters. The vehicle dynamics and wheel wear of the before and after optimisation are analysed. The results showed that the use of optimised suspension parameters effectively increased the vehicle's critical speed. The optimised parameters further improved the vehicle's ride index, safety index, and reduced the lateral force of the wheel axle. In addition, the optimised parameters suppressed the lateral vibration of the vehicle. When the wear mileage reached 200,000 km, the wheel wear depths before and after optimisation were 1.086 and 0.9806 mm, respectively. Therefore, the optimisation of the suspension parameters can effectively improve the vehicle dynamics performance and subsequently reduce wheel wear.

Regional subsidence monitoring and prediction along high-speed railways based on PS-InSAR and LSTM

The rapid construction of high-speed railways in China has forced more and more routes to pass through slow subsidence zones caused by groundwater extraction or underground mining. It is crucial to carry out deformation monitoring and prediction those areas along high-speed railways to ensure the safe operation. Twenty-nine periods of Sentinel-1A data between Taiyuan South Station and Taigu East Station were first processed using the PS-InSAR technique, and the ground subsidence sequences at ten typical points were selected. Then the Variational modal decomposition (VMD) was combined with the Adaptive Boosting Algorithm (AdaBoost), and the VMD-LSTMAda-LSTMAda model was constructed by combining the long short-term memory (LSTM) model for the prediction of regional ground subsidence along the high-speed railway. The results show that the cumulative subsidence within the 200 m buffer of the line centreline is − 37.58 − 89.19 mm, and the annual average subsidence rate is − 26.57 − 82.63 mm/y. The proposed model can reduce the complexity of the deformation sequence and combines with AdaBoost to improve the prediction accuracy of the LSTM model. It performs well in terms of RMSE, MAE, MAPE, and R2 at the two feature points (3703: RMSE = 0.82 mm, MAE = 0.25 mm, MAPE = 6.31%, and R2 = 0.94; 522: RMSE = 1.32 mm, MAE = 0.46 mm, MAPE = 4.92%, R2 = 0.95). The model improves the accuracy of regional subsidence prediction along the high-speed railway.

Does transportation infrastructure contribute to urban entrepreneurship? Evidence from the high-speed railway in China

High-speed railways have a profound impact on entrepreneurship. Adopted China's city panel data, we use a time-varying DID model to estimate the influence of the high-speed railways on urban entrepreneurship and study its mechanism. The analysis results reveal that high-speed railways have improved the entrepreneurial vitality of cities. The mechanism test suggests that high-speed railway promotes entrepreneurship activities by accelerating human capital flow, strengthening producer services agglomeration, and promoting market integration. A range of robustness tests including a parallel trend test, placebo test, and PSM-DID model are used to ensure the reliability of the empirical results. Heterogeneity analysis and spatial analysis are used to deepen the research. Heterogeneity analysis reveals that high-speed railway construction is likely to motivate entrepreneurship in eastern cities, big cities, and cities with high-level business environments. The spatial analysis results show that the opening of the high-speed railways promotes the entrepreneurial level of surrounding cities. The research results help to evaluate the economic benefits of high-speed railway network expansion, and provide decision-making references for stimulating urban entrepreneurship.

Research on the Calculation Method and Influence Law of High-speed Railway Line Sound Source Propagation Attenuation Caused by Building Sheltering

Abstract In recent years, China’s rail transit system, particularly the high-speed railway network, has experienced rapid development. However, the issue of noise pollution along these railways has become increasingly severe. The accurate prediction of sound levels at specific points near buildings is challenging due to complex factors such as differences in line height, distance, and angle relative to the building. To address this challenge and improve predictions of sound propagation attenuation caused by building shelter from high-speed railway line sources, this study derives an integral expression for diffraction attenuation based on Curze and Meakawa’s point source acoustic diffraction theory. Numerical calculations are conducted to investigate the sound propagation attenuation patterns under different position relationships between buildings and railway lines. Field tests are also carried out along a high-speed railway in China. The findings are as follows: (1) The calculated results show a difference of less than 0.5 dB compared to measured values, indicating that they accurately reflect sound attenuation caused by building shelter. (2) The angle between the building and the railway as well as the distance between the sound point and the edge of the building significantly influence building shelter attenuation; with angle being most prominent among them within a frequency band of 500~2000 Hz resulting in an attenuation range of 0.6~15 dB variation should be considered when predicting noise levels at sensitive points or designing vibration and noise reduction measures accordingly. (3) Different distances between buildings and railway lines result in additional effects on attenuations.

Hidden emotional trends on social media regarding the Thailand–China high-speed railway project: a deep learning approach with ChatGPT integration

Leveraging sentiment analysis on social media reveals hidden emotional trends by providing deep insights into the public’s collective opinions and feelings because social media can reflect most opinions based on true feelings. However, there is no time-series analysis of Thai opinions on social media regarding government projects in Thailand. Therefore, the focus of this research is to survey and dynamically analyze sentiment on social media platforms, particularly YouTube, regarding the Thailand–China High-Speed Rail Project (Thai–China HSR Project). This is a major project involving an investment of over 500 billion baht, a collaborative effort between the Thai and Chinese governments aimed at developing transportation infrastructure. It leads the public concerns of Thai people about the potential economic and social impacts. The deep learning (DL) technique was used for this analysis by the Bidirectional Encoder Representations from Transformers (BERT) model, together with WangchanBERTa, a Thai language model specifically designed to analyze opinions from Thai comments received from YouTube from July 2015 to January 2024. The analysis reveals that citizens’ sentiments are primarily neutral, with more negative than positive sentiments with the passing years. Results also indicate that the data volume has increased since 2019 and peaked in 2022. Negative comments peaked in January 2022, and based on our analysis, such negative comments followed news covering the opening of the China–Laos Railway on December 1, 2021. Video contents were related to the Thai–China HSR Project, which provides comparative reviews and analysis of government operations. Additionally, we explore the possibility of integrating AI technology based on the ChatGPT 4 model to support data interpretation. Although the AI model can interpret data quickly and efficiently serve as a convenient method for users, the results lack the in-depth analysis that should be done by experts in the field. The proposed framework could be useful to government agencies and can be used to quickly and efficiently survey and track public opinion. The results can be used for policy planning, strategic decision-making, and improving communications to better inform citizens about projects and operations, leading to the country’s sustainable economic and social development.

Support Vector Machine-Based Fault Diagnosis under Data Imbalance with Application to High-Speed Train Electric Traction Systems

The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction systems under data imbalance is not only theoretically important but also crucial for ensuring vehicle safety. Firstly, when addressing the data imbalance issue, the fault diagnosis mechanism based on support vector machines tends to prioritize the majority class when constructing the classification hyperplane. This frequently leads to a reduction in the recognition rate of minority-class samples. To tackle this problem, a self-tuning support vector machine is proposed in this paper by setting distinct penalty factors for each class based on sample information. This approach aims to ensure equal misclassification costs for both classes and achieve the objective of suppressing the deviation of the classification hyperplane. Finally, simulation experiments are conducted on the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform using three different imbalance ratios to address the data imbalance issue. The experimental results demonstrate consistent misclassification costs for both the minority- and majority-class samples. Additionally, the proposed self-tuning support vector machine effectively mitigates hyperplane deviation, further confirming the effectiveness of this fault diagnosis mechanism for high-speed train electric traction systems.

Integrated optimization of train stop planning and seat allocation for high-speed railways based on active management for dynamic demand

ABSTRACT Proactive demand management methods can achieve a trade-off between supply and demand for high-speed railways. Focusing on multi-type demand configurations, four active demand management methods were designed, two single-scenario and two multi-scenario methods. An optimization model of stop planning and seat allocation was constructed to minimize the difference between demand and supply, number of trains, and train-stop costs. The number of train stops, transport capacity, and train occupancy rates were considered. Particle swarm optimization and CPLEX were combined to handle this significant linear programming problem. A real case study of different demand scenarios based on the Hohhot-Beijing high-speed railway in China verified the feasibility of active demand management methods. The results indicate that integrated optimization can be justified with mixed load patterns. Compared with the original scheme, the optimized scheme reduced the number of trains by 23.4% and increased train occupancy rate by more than 36.7%.

PC-Kriging-powered parallelizing Bayesian updating for stochastic vehicle-track dynamical system with contact force measurements and Gaussian process discrepancy model

High-precision vehicle-track coupled dynamical models play a vital role in assessing the running safety of the vehicle, tracking fatigue behavior, and establishing a digital twin for high-speed railways. This study established a high-fidelity vehicle-track coupled dynamical model as a forward solver, which was then updated in a Bayesian inference framework based on the field tests of rail irregularities and wheel/rail normal contact forces. The prediction errors corresponding to different time steps were represented through a Gaussian process (GP) model characterized by a covariance function with the Gaussian kernel to incorporate the correlation between different time steps. To cope with the considerable expense of the likelihood function calculations with large-scale loop operations and the computational burden due to the large batch of repetitive evaluations of the high-fidelity forward model involved in the stochastic sampling from the posterior probability distribution, a metamodel-powered parallelizing stochastic sampling scheme was adopted. The posterior distribution could be formulated by replacing the expensive explicit model evaluation involved in the likelihood function with a Polynomial-Chaos-Kriging (PC-Kriging) predictor providing a surrogate mapping between the wheel/rail normal contact forces and physical parameters. Finally, a parallelizing computing scheme running across multiple CPU cores on high-performance computers was realized to sample from the vectorized formula of the emulator-powered posterior distribution. Field test data from a high-speed railway in China was adopted to demonstrate that the Bayesian inference scheme can quantify the uncertainties of the core physical parameters of the high-fidelity vehicle-track coupled dynamical system by efficiently trading off accuracy and computational efficiency.

The Impact of Transportation Infrastructure on Manufacturing Energy Intensity: Micro Evidence from High-Speed Railway in China

This paper examines the potential of infrastructure improvements, particularly the operation of High-speed railway (HSR), to enhance the energy efficiency of firms. By regarding the operation of the China’s HSR as an exogenous shock, this paper relies on a difference-in-differences estimation to isolate the causal effect of the infrastructure improvement on manufacturing firms’ energy intensity. This paper finds that the operation of HSR has improved the manufacturing firms’ energy intensity through the catalyzing the proliferation of advanced management techniques, and this effect gradually decreases with the increase in distance between the firm and the HSR station. Moreover, HSR indirectly decreases energy intensity by fostering technological innovation, decreasing debt financing costs, and extending debt financing magnitude. The study also uncovers heterogeneous effects of HSR connectivity on manufacturing firms’ energy intensity, which are more pronounced in smaller cities and industries characterized by high concentration and low agglomeration. Furthermore, the impact of HSR connectivity on reducing energy intensity is more substantial in state-owned and larger firms compared to non-state-owned and smaller firms. JEL Classification: Q43, O13, R41, L60

The impact of nozzle parameters and water injection pressure on the application effect of hydraulic punching enhanced permeability technology in high ground stress coal seam

High-stress coal seams are different from ordinary coal seams in terms of their low hardness, low permeability, and certain degree of fluidity, making them more difficult to enhance permeability. Hydraulic punching enhanced transmission technology can quickly create voids in coal seams, but existing hydraulic punching techniques are suitable for shallow coal seams, and the effect of creating voids is greatly reduced when applied to high-stress coal seams. To optimize the application effect of hydraulic punching enhanced transmission technology in high-stress coal seams, theoretical research was conducted on the parameters of hydraulic punching nozzles and injection water pressure. A simulation model was established using COMSOL numerical simulation software to optimize the nozzle parameters and explore the combined effect of nozzle parameters and injection water pressure on enhancing permeability. Field experiments were conducted in the Zhaotong Tunnel of the China high-speed railway to determine the optimal combination of nozzle parameters and injection water pressure, thereby increasing the overall permeability of the coal seam and improving gas extraction efficiency. The results indicate that: (1)Through theoretical calculations, numerical simulations, and experimental verification, it has been proven that the perforation effect of a jetting device with three the long oblique nozzle with an oblique length of 15 mm and a size of 3 mm × 6mm is the best among the comparative results under a water injection pressure of 5 MPa；(2)Compared to the perforation effect before optimization, the water consumption for perforation after optimization is reduced to 0.6 times that before optimization, and the perforation efficiency after optimization is 3.556 times that before optimization. The perforation efficiency has been effectively improved；(3)The similarity between the theoretical results, simulation results, and experimental results in this study is all above 85%. The consistency of the results provides mutual verification and has certain engineering guidance significance.

Does the clusters of high-speed railway network match the urban agglomerations? A case study in China

High-speed railways play a crucial role in intercity transportation, shaping the spatial interactions within urban agglomerations. However, a mismatch between transportation clusters and the spatial structure of urban agglomerations hampers regional economic integration. To address this issue, this paper proposes a community classification model, employing the Louvain algorithm, to analyze the aggregation of high-speed railway networks (HSRN). It compares central cities in city clusters with HSRN communities in relevant areas, examining the influence of central cities on urban agglomerations. The model is applied to a case study of China's high-speed railway network. The community classifications of HSRN are then compared with planned or future urban agglomerations at the national, provincial, and civic levels. The findings reveal that HSRN in China exhibits distinct clusters that align with the distribution of urban agglomerations. The existing central cities within these agglomerations provide good service levels in the HSRN network. However, HSRN communities within each urban agglomeration are overly scattered and should be integrated to reduce accessibility inequalities. This study offers suggestions for the development planning of HSRN and contributes to the economic integration of urban agglomerations.

Key technologies of earthquake early warning system for China’s high-speed railway

PurposeThe purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approachThe paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.FindingsThe study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.Originality/valueThe paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

A Georeferenced Dataset for Mapping and Assessing Subgrade Defects in China’s High-Speed Railways

China has the world’s longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance.

Proximity still matters in research collaboration! Evidence from the introduction of new airline routes and high-speed railways in China

Proximity still matters in research collaboration! Evidence from the introduction of new airline routes and high-speed railways in China

Analysis of overvoltage impact on shell-type on-board traction transformer by multi-physics field simulation

With the rapid development and increasing demands for the high-speed railway in China, it is of great significance to examine the defects of the high-speed train and ensure the safety of the operating railway. In the actual operation of the high-speed train, there is a split-phase section to transit the train to the next substation power supply arm. In this particular stage, operational overvoltage would occur as the VCB and the pantograph switch on and off, which threatens the safety of the operation of high-speed trains and contributes to the breakdown of transformer windings, which is indispensable in transformer operation. In this work, a finite element model of shell type on board traction transformer is constructed and multi-physics field simulation results, including electromagnetic field, temperature field, and flow velocity field, are analyzed to investigate the potential risky spots in the practical operation of high-speed train under overvoltage impulse, which could be supportive in maintaining the devices and detecting the winding defects.

Has the opening of high-speed railway in China promoted urban low-carbon transformation along the route: re-estimation based on staggered DID robust estimators.

Considering the opening of high-speed railway (HSR) as a quasi-natural experiment, this paper explores the emission reduction effect of HSR opening and its mechanisms and heterogeneity characteristics based on the panel data of 272 cities in China from 2006 to 2019 by staggered DID model and the mediating effect model. It is found that (1) the opening of HSR significantly reduces the carbon emission level of cities, and the conclusion still holds after controlling the endogeneity issue and a series of robustness tests; (2) Bancon decomposition and negative weight diagnostics both show that the estimated average treatment effect bias is small and the robust estimators are more robust; (3) by reducing production costs and promoting inter-regional industrial division of labor, the opening of HSR promotes intra-industrial structural transformation and inter-industrial structural transformation, respectively, thus reducing urban carbon emissions. At the same time, HSR achieves urban carbon emission reduction by promoting free movement of population and human capital agglomeration; and (4) further study finds that there is regional heterogeneity in the emission reduction effect of HSR opening, and the carbon reduction effect is mainly reflected in large and medium-sized cities, near provincial capitals, non-resource-based cities, and cities with rich educational resources, which indicates that the location of future HSR should be combined with the city's own characteristics and be tailored to the city.

Digital twin for multi-scenario emergency of railway passenger stations

Emergency disposal is a critical aspect for railway stations to ensure safety. This requires the implementation of emergency plan simulations and cost-effective immersive drills. In the paper, we incorporate a set of disposals for events into emergency processes and model the personnel, supplies, and equipment to create multiple emergency scenarios. Additionally, we introduce a digital twin-based solution for multiple scenarios of emergencies. This solution completely restores the key components of a station in the information space and provides an immersive way for emergency disposals. The four-dimensional model used in the solution simulates and interacts with the station, and it is composed of the details of passenger station physical entity, multi-scenario emergency virtual entity, digital twin connection, and emergency twin service. The digital twin for versatile emergency events such as fire disaster, natural disaster (e.g., flood, earthquake), social security incident, and public health event have been constructed using the model. The solution was tested at Qinghe, a station during Beijing-Zhangjiakou high-speed railway in China. By utilizing the actual operation data of Qinghe, we validated the multi-scenario emergency drills, simulated the corresponding emergency disposal plans, and assessed the proposed solution from three aspects: the fidelity of the real-world simulation, coverage of the multiple emergency scenarios, and the user-friendliness. The evaluations indicate that the proposed solution attains good score and the acceptable level of system usability scale.