Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response
Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Vibration |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-631X/4/2/28 |
| _version_ | 1797534097579442176 |
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| author | Md Riasat Azim Mustafa Gül |
| author_facet | Md Riasat Azim Mustafa Gül |
| author_sort | Md Riasat Azim |
| collection | DOAJ |
| description | Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis. |
| first_indexed | 2024-03-10T11:24:50Z |
| format | Article |
| id | doaj.art-63093aa247ee4dd3bcfe79460c36a457 |
| institution | Directory Open Access Journal |
| issn | 2571-631X |
| language | English |
| last_indexed | 2024-03-10T11:24:50Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vibration |
| spelling | doaj.art-63093aa247ee4dd3bcfe79460c36a4572023-11-21T19:44:53ZengMDPI AGVibration2571-631X2021-05-014242244310.3390/vibration4020028Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain ResponseMd Riasat Azim0Mustafa Gül1Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaDepartment of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaRailway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.https://www.mdpi.com/2571-631X/4/2/28railway truss bridgesstructural health monitoringdamage detection frameworkoperational acceleration responseoperational strain response |
| spellingShingle | Md Riasat Azim Mustafa Gül Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response Vibration railway truss bridges structural health monitoring damage detection framework operational acceleration response operational strain response |
| title | Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response |
| title_full | Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response |
| title_fullStr | Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response |
| title_full_unstemmed | Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response |
| title_short | Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response |
| title_sort | development of a novel damage detection framework for truss railway bridges using operational acceleration and strain response |
| topic | railway truss bridges structural health monitoring damage detection framework operational acceleration response operational strain response |
| url | https://www.mdpi.com/2571-631X/4/2/28 |
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