Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks
Abstract Vibration data from passing vehicles can theoretically be leveraged for indirect health monitoring (IHM) of bridges. However, vibration data collected from vehicles on bridges are often tainted with noise, including vehicle harmonics, road conditions, and environmental factors. Researchers...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Advances in Bridge Engineering |
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| Online Access: | https://doi.org/10.1186/s43251-024-00152-2 |
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| author | Omar Abuodeh Laura Redmond |
| author_facet | Omar Abuodeh Laura Redmond |
| author_sort | Omar Abuodeh |
| collection | DOAJ |
| description | Abstract Vibration data from passing vehicles can theoretically be leveraged for indirect health monitoring (IHM) of bridges. However, vibration data collected from vehicles on bridges are often tainted with noise, including vehicle harmonics, road conditions, and environmental factors. Researchers employ system identification (SI) techniques to extract pertinent bridge features from this noisy data yet face limitations due to user-defined parameters and validation with sparse datasets. This study leverages supercomputing and an automated postprocessing framework to identify testing protocols and vehicle parameters that enhance SI across the bridge network using Vehicle-Bridge Interaction (VBI) models. Vehicle properties for four vehicle classes are gathered from literature, while six bridge properties are derived from tested bridges and department of transportation records. Findings reveal that heavier and faster leading vehicles facilitate bridge frequency extraction. Challenges such as masking of bridge frequencies by road roughness and low-pass effects of vehicle suspension on bridges with higher natural frequencies are identified. Solutions include employing faster trailing vehicle speeds shift the road roughness frequency bands away from the bridge’s frequency and using heavier leading vehicles to enhance bridge response. In addition, an advanced signal processing technique, autonomous peak picking variational mode decomposition (APPVMD), successfully extracts bridge frequencies for problematic bridges. |
| format | Article |
| id | doaj-art-90456e08b0cb48deacef7cde60a700f2 |
| institution | DOAJ |
| issn | 2662-5407 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Advances in Bridge Engineering |
| spelling | doaj-art-90456e08b0cb48deacef7cde60a700f22025-08-20T02:48:16ZengSpringerOpenAdvances in Bridge Engineering2662-54072025-02-016114210.1186/s43251-024-00152-2Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networksOmar Abuodeh0Laura Redmond1Civil and Structural Engineering, Exponent IncGlenn Department of Civil Engineering, Clemson UniversityAbstract Vibration data from passing vehicles can theoretically be leveraged for indirect health monitoring (IHM) of bridges. However, vibration data collected from vehicles on bridges are often tainted with noise, including vehicle harmonics, road conditions, and environmental factors. Researchers employ system identification (SI) techniques to extract pertinent bridge features from this noisy data yet face limitations due to user-defined parameters and validation with sparse datasets. This study leverages supercomputing and an automated postprocessing framework to identify testing protocols and vehicle parameters that enhance SI across the bridge network using Vehicle-Bridge Interaction (VBI) models. Vehicle properties for four vehicle classes are gathered from literature, while six bridge properties are derived from tested bridges and department of transportation records. Findings reveal that heavier and faster leading vehicles facilitate bridge frequency extraction. Challenges such as masking of bridge frequencies by road roughness and low-pass effects of vehicle suspension on bridges with higher natural frequencies are identified. Solutions include employing faster trailing vehicle speeds shift the road roughness frequency bands away from the bridge’s frequency and using heavier leading vehicles to enhance bridge response. In addition, an advanced signal processing technique, autonomous peak picking variational mode decomposition (APPVMD), successfully extracts bridge frequencies for problematic bridges.https://doi.org/10.1186/s43251-024-00152-2Multiple-vehicle simulationSystem identificationBridge frequency extractionFinite elementVehicle-bridge interaction |
| spellingShingle | Omar Abuodeh Laura Redmond Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks Advances in Bridge Engineering Multiple-vehicle simulation System identification Bridge frequency extraction Finite element Vehicle-bridge interaction |
| title | Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks |
| title_full | Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks |
| title_fullStr | Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks |
| title_full_unstemmed | Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks |
| title_short | Investigation of multiple-vehicle scenarios to improve system identification for indirect health monitoring of bridge networks |
| title_sort | investigation of multiple vehicle scenarios to improve system identification for indirect health monitoring of bridge networks |
| topic | Multiple-vehicle simulation System identification Bridge frequency extraction Finite element Vehicle-bridge interaction |
| url | https://doi.org/10.1186/s43251-024-00152-2 |
| work_keys_str_mv | AT omarabuodeh investigationofmultiplevehiclescenariostoimprovesystemidentificationforindirecthealthmonitoringofbridgenetworks AT lauraredmond investigationofmultiplevehiclescenariostoimprovesystemidentificationforindirecthealthmonitoringofbridgenetworks |