Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification
Moving vehicles equipped with various types of sensors can efficiently monitor the health conditions of a population of transportation infrastructure such as bridges. This paper presents a mobile crowdsensing framework to identify dense spatial-resolution bridge mode shapes using sparse drive-by mea...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2023
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| Online Access: | http://purl.org/au-research/grants/arc/FT190100801 http://hdl.handle.net/20.500.11937/96043 |
| _version_ | 1848766081270808576 |
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| author | Peng, Zhen Li, Jun Hao, Hong Yang, Ning |
| author_facet | Peng, Zhen Li, Jun Hao, Hong Yang, Ning |
| author_sort | Peng, Zhen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Moving vehicles equipped with various types of sensors can efficiently monitor the health conditions of a population of transportation infrastructure such as bridges. This paper presents a mobile crowdsensing framework to identify dense spatial-resolution bridge mode shapes using sparse drive-by measurements. The proposed method converts mode shape identification into a physical-informed optimization problem with two objective function terms. The first objective minimises the mode shape identification error based on the fact that the ratio of a specific order mode shape value at any two locations is time-invariant. Since the bridge mode shape should be globally smooth even when the local stiffness is discontinuous, the smoothness of the identified mode shape is introduced as the second objective. The feasibility and advantages of the proposed model are verified numerically and through large-scale experimental studies. Numerical results demonstrate that the proposed method can efficiently identify bridge mode shapes with a desirable accuracy. The adverse effects of road roughness and measurement noise on the mode shape identification accuracy are substantially suppressed by introducing crowdsensing and making use of collected responses over multiple trips. The applicability of the proposed method for bridges having varying cross sections and multiple spans is also studied. A series of drive-by tests with different vehicle masses and speeds are conducted on a large-scale footbridge. The experimental results verify that the proposed method can accurately identify the bridge mode shapes and is robust to vehicle mass and speed variation. The identification accuracy of large-scale bridge mode shapes using crowdsensing drive-by measurements is demonstrated in this study. |
| first_indexed | 2025-11-14T11:45:28Z |
| format | Journal Article |
| id | curtin-20.500.11937-96043 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:45:28Z |
| publishDate | 2023 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-960432024-11-08T02:18:21Z Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification Peng, Zhen Li, Jun Hao, Hong Yang, Ning Moving vehicles equipped with various types of sensors can efficiently monitor the health conditions of a population of transportation infrastructure such as bridges. This paper presents a mobile crowdsensing framework to identify dense spatial-resolution bridge mode shapes using sparse drive-by measurements. The proposed method converts mode shape identification into a physical-informed optimization problem with two objective function terms. The first objective minimises the mode shape identification error based on the fact that the ratio of a specific order mode shape value at any two locations is time-invariant. Since the bridge mode shape should be globally smooth even when the local stiffness is discontinuous, the smoothness of the identified mode shape is introduced as the second objective. The feasibility and advantages of the proposed model are verified numerically and through large-scale experimental studies. Numerical results demonstrate that the proposed method can efficiently identify bridge mode shapes with a desirable accuracy. The adverse effects of road roughness and measurement noise on the mode shape identification accuracy are substantially suppressed by introducing crowdsensing and making use of collected responses over multiple trips. The applicability of the proposed method for bridges having varying cross sections and multiple spans is also studied. A series of drive-by tests with different vehicle masses and speeds are conducted on a large-scale footbridge. The experimental results verify that the proposed method can accurately identify the bridge mode shapes and is robust to vehicle mass and speed variation. The identification accuracy of large-scale bridge mode shapes using crowdsensing drive-by measurements is demonstrated in this study. 2023 Journal Article http://hdl.handle.net/20.500.11937/96043 10.1016/j.engstruct.2023.116515 http://purl.org/au-research/grants/arc/FT190100801 https://creativecommons.org/licenses/by/4.0/ Elsevier fulltext |
| spellingShingle | Peng, Zhen Li, Jun Hao, Hong Yang, Ning Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| title | Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| title_full | Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| title_fullStr | Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| title_full_unstemmed | Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| title_short | Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| title_sort | mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification |
| url | http://purl.org/au-research/grants/arc/FT190100801 http://hdl.handle.net/20.500.11937/96043 |