Geology of Marseilles to Italy high‐speed TGV railway line compared to HS2 in Britain

Geology Today ◽  
2021 ◽  
Vol 37 (1) ◽  
pp. 23-30
Author(s):  
David A.G. Nowell
Keyword(s):  
High Speed ◽  
Railway Line

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

Typical Portal Structure for Single Track High Speed Railway Tunnel in Rugged Mountainous Area with High and Abrupt Slope

2014 ◽  
Vol 716-717 ◽  
pp. 342-346
Author(s):  
Xiao Jun Zhou ◽  
Bo Jiang ◽  
Yue Feng Zhou ◽  
Yu Yu
Keyword(s):  
High Speed ◽  
Southwest China ◽  
Earth Pressure ◽  
Mountainous Area ◽  
Single Track ◽  
Railway Tunnel ◽  
High Speed Railway ◽  
Railway Line ◽  
Bridge Abutment

On the basis of different landform and multifarious topography in rugged mountainous area in southwest China, typical tunnel portals for single track tunnels in a new high speed railway line have been presented in the paper. The portal comprises headwall, shed tunnel, bridge abutment and its support. Portal with headwall is suitable for tunnel to resist front earth pressure on high and abrupt slope. Shed tunnel is placed in front of headwall so as to prevent rockfall; its outward part is built into a flared one. Meanwhile, the installation of bridge and its abutment are also included in the portal according to landform in the paper.

scholarly journals Fault Diagnosis for Vehicle on-Board Equipments of High-Speed Railway Based on GA-ANFIS Model

2020 ◽  
Author(s):  
Linggang Kong ◽  
Shuo Li ◽  
Xinlong Chen ◽  
Hongyan Qin
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Principal Component ◽  
Weighting Factor ◽  
Anfis Model ◽  
High Speed Railway ◽  
Railway Line ◽  
Diagnosis Rate ◽  
Low Efficiency ◽  
Frequency Weighting

Vehicle on-board equipment is the most important train control equipment in high-speed railways. Due to the low efficiency and accuracy of manual detection, in this paper, we propose an intellectualized fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) network. Firstly, we collect the fault information sheets that are recorded by electrical personnel, using frequency weighting factor and principal component analysis (PCA) to realize the data extraction and dimension reduction; Then, in order to improve the fault diagnosis rate of the model, using genetic algorithm (GA) to optimize the parameters of the ANFIS network; Finally, using the fault data of a high-speed railway line in 2019 to test the model, the optimized ANFIS model can achieve 96% fault diagnosis rate for vehicle on-board equipments, which indicating the method is effective and accurate.

scholarly journals A Research and Application of the Processing Methods of Railway Measurement Based on Self-Mobile Scanning Measurement System

2020 ◽  
Author(s):  
Jiancai Zhang ◽  
Hang Mu ◽  
Feng Han ◽  
Shumin Han
Keyword(s):  
Measurement System ◽  
High Speed ◽  
Automatic Segmentation ◽  
Laser Scanner ◽  
Region Growing ◽  
Image Sensor ◽  
Operating Conditions ◽  
Secondary Development ◽  
Railway Line ◽  
Region Growing Segmentation

With the gradual improvement of China’s railway net, the opening of international railways as well as the continuous growth of railway operating mileage, the workload of remeasuring railways is increasing. The traditional methods of remeasuring railways can not meet current high-speed and high-density operating conditions anymore in terms of safety, efficiency and quality, so a safer and more efficient measurement method is urgently needed.This thesis integrated various sensors on a self-mobile instrument, such as 3D laser scanner, digital image sensor and GNSS_IMU, designing a set of intelligent and integrated self-mobile scanning measurement system. This thesis proposed region growing segmentation based on the reflection intensity of point cloud. Through the secondary development of CAD, the menu for automatic processing of self-mobile scanning measurement system is designed to realize rail automatic segmentation, extraction of rail top points, fitting of plane parameters of railway line, calculation of curve elements and mileage management.The results show that self-mobile scanning measurement system overcomes the shortcomings of traditional railway measurement to some extent, and realizes intelligent measurement of railways.

scholarly journals Probabilistic Safety Analysis of High Speed and Conventional Lines Using Bayesian Networks

2016 ◽  
Author(s):  
Zacarias Grande Andrade ◽  
Enrique Castillo Ron ◽  
Alan O'Connor ◽  
Maria Nogal
Keyword(s):  
Bayesian Network ◽  
High Speed ◽  
Human Error ◽  
Driver Behavior ◽  
Safety Evaluation ◽  
Safety Analysis ◽  
Human Errors ◽  
Maintenance Costs ◽  
Railway Line ◽  
Probabilistic Safety

A Bayesian network approach is presented for probabilistic safety analysis (PSA) of railway lines. The idea consists of identifying and reproducing all the elements that the train encounters when circulating along a railway line, such as light and speed limit signals, tunnel or viaduct entries or exits, cuttings and embankments, acoustic sounds received in the cabin, curves, switches, etc. In addition, since the human error is very relevant for safety evaluation, the automatic train protection (ATP) systems and the driver behavior and its time evolution are modelled and taken into account to determine the probabilities of human errors. The nodes of the Bayesian network, their links and the associated probability tables are automatically constructed based on the line data that need to be carefully given. The conditional probability tables are reproduced by closed formulas, which facilitate the modelling and the sensitivity analysis. A sorted list of the most dangerous elements in the line is obtained, which permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs substantially. The proposed methodology is illustrated by its application to several cases that include real lines such as the Palencia-Santander and the Dublin-Belfast lines.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3428

scholarly journals Model of the Multi-Criteria Analysis of the Infrastructure of the Railway Line at the Stage of Initiation of the Project of High-Speed Railway

2017 ◽  
Vol 20 (3) ◽  
pp. 107
Author(s):  
V V Zagrebin
Keyword(s):  
High Speed ◽  
High Speed Railway ◽  
Railway Line ◽  
Multi Criteria Analysis

Предлагается модель анализа текущего состояния существующей инфраструктуры железнодорожной магистрали с целью последующего принятия организационно-технических решений на этапе инициации проекта высокоскоростной железнодорожной магистрали. Описываются возможные современные способы реализации высокоскоростного сообщения и их отличия в проектных и организационных решениях. Представлены основные элементы инфраструктуры железнодорожной магистрали, отдельно выделены факторы, влияющие на увеличение скорости движения. Представлена методика анализа, этапы и последовательность его проведения, принцип определения объектов анализа, возможные способы сбора данных по объектам анализа, перечень отраслевых геоинформационных и автоматизированных систем сбора и обработки оперативной информации по текущему состоянию инфраструктуры и эксплуатационной работе, методика обработки, обобщения, систематизации полученных данных для последующего анализа. Разработан общий алгоритм комплексного анализа технических и эксплуатационных параметров магистрали в виде последовательной блок-схемы. Данная модель актуальна при проведении анализа состояния инфраструктуры для всех существующих железнодорожных магистральных линий на территории России при выборе оптимальных и эффективных проектных решений совместно с экономическим обоснованием в процессе разработки комплексных мероприятий по внедрению высокоскоростного сообщения.

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