Chuang Wang, Jiawang Zhan, Yujie Wang, Xinxiang Xu, Zhihang Wang
{"title":"通过稀疏测量利用车辆诱发的振动响应对铁路桥梁下部结构进行状态监测和定量评估","authors":"Chuang Wang, Jiawang Zhan, Yujie Wang, Xinxiang Xu, Zhihang Wang","doi":"10.1155/2024/3271106","DOIUrl":null,"url":null,"abstract":"<div>\n <p>Bridge substructure failure has been responsible for numerous recorded bridge collapses, particularly for small- and medium-span bridges, so it is crucial to effectively monitor the performance of the bridge substructures for efficient maintenance and management. The current vibration-based approaches for quantitatively evaluating bridge substructures rely on in-situ experiments with a multitude of sensors or impact vibration test, making it challenging to implement long-term online monitoring. This paper proposes an accurate, low cost, and practicable method to achieve online quantitative monitoring of railway bridge substructures using only one vibration sensor and operational train-induced vibration responses. The newly derived flexible-base Timoshenko beam models, along with the random decrement technique and Levenberg–Marquardt–Fletcher algorithm, are employed to identify the modal parameters and quantitatively assess the condition of bridge substructures. The proposed method is numerically verified through an established 3D train-bridge-foundation coupling system considering different damage scenarios. In addition, a real-world application is also conducted on the 2<sup>nd</sup> Songhua River bridge in the Harbin–Dalian high-speed railway, aiming at examining the effectiveness and robustness of the method in condition monitoring of bridge substructure under a complete freeze-thaw cycle. The results indicate that the proposed methodology is effective in extracting the modal parameters and monitoring the state evolution of the bridge substructures, which offers an efficient and accurate strategy for condition monitoring and quantitative evaluation of railway bridge substructures.</p>\n </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2024 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/3271106","citationCount":"0","resultStr":"{\"title\":\"Condition Monitoring and Quantitative Evaluation of Railway Bridge Substructures Using Vehicle-Induced Vibration Responses by Sparse Measurement\",\"authors\":\"Chuang Wang, Jiawang Zhan, Yujie Wang, Xinxiang Xu, Zhihang Wang\",\"doi\":\"10.1155/2024/3271106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>Bridge substructure failure has been responsible for numerous recorded bridge collapses, particularly for small- and medium-span bridges, so it is crucial to effectively monitor the performance of the bridge substructures for efficient maintenance and management. The current vibration-based approaches for quantitatively evaluating bridge substructures rely on in-situ experiments with a multitude of sensors or impact vibration test, making it challenging to implement long-term online monitoring. This paper proposes an accurate, low cost, and practicable method to achieve online quantitative monitoring of railway bridge substructures using only one vibration sensor and operational train-induced vibration responses. The newly derived flexible-base Timoshenko beam models, along with the random decrement technique and Levenberg–Marquardt–Fletcher algorithm, are employed to identify the modal parameters and quantitatively assess the condition of bridge substructures. The proposed method is numerically verified through an established 3D train-bridge-foundation coupling system considering different damage scenarios. In addition, a real-world application is also conducted on the 2<sup>nd</sup> Songhua River bridge in the Harbin–Dalian high-speed railway, aiming at examining the effectiveness and robustness of the method in condition monitoring of bridge substructure under a complete freeze-thaw cycle. The results indicate that the proposed methodology is effective in extracting the modal parameters and monitoring the state evolution of the bridge substructures, which offers an efficient and accurate strategy for condition monitoring and quantitative evaluation of railway bridge substructures.</p>\\n </div>\",\"PeriodicalId\":49471,\"journal\":{\"name\":\"Structural Control & Health Monitoring\",\"volume\":\"2024 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/3271106\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Control & Health Monitoring\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/3271106\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Control & Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/3271106","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Condition Monitoring and Quantitative Evaluation of Railway Bridge Substructures Using Vehicle-Induced Vibration Responses by Sparse Measurement
Bridge substructure failure has been responsible for numerous recorded bridge collapses, particularly for small- and medium-span bridges, so it is crucial to effectively monitor the performance of the bridge substructures for efficient maintenance and management. The current vibration-based approaches for quantitatively evaluating bridge substructures rely on in-situ experiments with a multitude of sensors or impact vibration test, making it challenging to implement long-term online monitoring. This paper proposes an accurate, low cost, and practicable method to achieve online quantitative monitoring of railway bridge substructures using only one vibration sensor and operational train-induced vibration responses. The newly derived flexible-base Timoshenko beam models, along with the random decrement technique and Levenberg–Marquardt–Fletcher algorithm, are employed to identify the modal parameters and quantitatively assess the condition of bridge substructures. The proposed method is numerically verified through an established 3D train-bridge-foundation coupling system considering different damage scenarios. In addition, a real-world application is also conducted on the 2nd Songhua River bridge in the Harbin–Dalian high-speed railway, aiming at examining the effectiveness and robustness of the method in condition monitoring of bridge substructure under a complete freeze-thaw cycle. The results indicate that the proposed methodology is effective in extracting the modal parameters and monitoring the state evolution of the bridge substructures, which offers an efficient and accurate strategy for condition monitoring and quantitative evaluation of railway bridge substructures.
期刊介绍:
The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications.
Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics.
Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.