Practice and Research of Structural Health Monitoring in High Speed Railway Station

2020 ◽  
Author(s):  
Chuanping Liu ◽  
Jian Jia
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Railway Station ◽  
High Speed Railway ◽  
Structural Health

Structural Health Monitoring of Large-Section Tunnel of Jingxiong High-Speed Railway Based on Fiber Bragg Grating Monitoring Technology

2020 ◽  
Vol 57 (21) ◽  
pp. 210603
Author(s):  
吴静红 Wu Jinghong ◽  
叶少敏 Ye Shaomin ◽  
张继清 Zhang Jiqing ◽  
赵青 Zhao Qing ◽  
张文轩 Zhang Wenxuan
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Bragg Grating ◽  
Health Monitoring ◽  
High Speed ◽  
Bragg Grating ◽  
Large Section ◽  
Monitoring Technology ◽  
High Speed Railway ◽  
Structural Health

Design and organisation of structural health monitoring system on high-speed railway bridge constructions

2017 ◽  
pp. 211-222 ◽  
Author(s):  
Andrei Belyi ◽  
Aleksei Baranovskii ◽  
Dmitrii Vorobiev ◽  
Kirill Dolinskii ◽  
Leonid Dyachenko ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
High Speed ◽  
Practical Importance ◽  
Point Of View ◽  
High Speed Railway ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Objective: Structural health monitoring actions designing for high-speed railway engineering constructions to decrease risk of loss consumer properties of bearing designs Methods: The analysis and accounting of the specific conditions and impacts inherent in constructions of high-speed railways, in total with the existing normative and regulatory documents, and also international background of design and development of structural health monitoring systems on bridge objects. The system approach to the solution of the formulated purpose which is expressed in preparation of the project decisions that are based on the integrated principles of functioning of developed structural health monitoring system. Results: Project decisions on structural health monitoring system of high-speed railway Moscow-Kazan-Yekaterinburg constructions were made. In particular, the actions for the monitoring organization on the unified designs of superstructures and piers (including their separate elements) are developed, considering both static and dynamic components of impacts and loadings being planned. The key parameters subject to monitoring are: absolute and relative designs shift; dynamic characteristics; the stressed-deformed condition of superstructure, piles, a rail and rail temperature. Practical importance: The actions, developed by authors and stated in the article, for design and organization of bridge constructions structural health monitoring on high-speed railways have no analogs in domestic practice so far. Cost efficiency and the unified approach to the proposed solutions were considered as of primary importance in the given study. From the practical point of view, in the future, these actions will make it possible to provide and support the set standard (project) levels of reliability, safety and durability of bridge constructions

scholarly journals Long-Term Structural Health Monitoring System for a High-Speed Railway Bridge Structure

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
You-Liang Ding ◽  
Gao-Xin Wang ◽  
Peng Sun ◽  
Lai-Yi Wu ◽  
Qing Yue
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
High Speed ◽  
Railway Bridge ◽  
High Speed Railway ◽  
Health Monitoring System ◽  
Structural Health ◽  
Mathematical Correlation ◽  
Correlation Models

Nanjing Dashengguan Bridge, which serves as the shared corridor crossing Yangtze River for both Beijing-Shanghai high-speed railway and Shanghai-Wuhan-Chengdu railway, is the first 6-track high-speed railway bridge with the longest span throughout the world. In order to ensure safety and detect the performance deterioration during the long-time service of the bridge, a Structural Health Monitoring (SHM) system has been implemented on this bridge by the application of modern techniques in sensing, testing, computing, and network communication. The SHM system includes various sensors as well as corresponding data acquisition and transmission equipment for automatic data collection. Furthermore, an evaluation system of structural safety has been developed for the real-time condition assessment of this bridge. The mathematical correlation models describing the overall structural behavior of the bridge can be obtained with the support of the health monitoring system, which includes cross-correlation models for accelerations, correlation models between temperature and static strains of steel truss arch, and correlation models between temperature and longitudinal displacements of piers. Some evaluation results using the mean value control chart based on mathematical correlation models are presented in this paper to show the effectiveness of this SHM system in detecting the bridge’s abnormal behaviors under the varying environmental conditions such as high-speed trains and environmental temperature.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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