<i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts
Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is esse...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/15/5701 |
| _version_ | 1797412278843211776 |
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| author | Yuguang Fu Yaoyu Zhu Tu Hoang Kirill Mechitov Billie F. Spencer |
| author_facet | Yuguang Fu Yaoyu Zhu Tu Hoang Kirill Mechitov Billie F. Spencer |
| author_sort | Yuguang Fu |
| collection | DOAJ |
| description | Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events. |
| first_indexed | 2024-03-09T04:59:45Z |
| format | Article |
| id | doaj.art-222cc38158ad43fdaabcdb9c2a1546d6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:45Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-222cc38158ad43fdaabcdb9c2a1546d62023-12-03T13:01:09ZengMDPI AGSensors1424-82202022-07-012215570110.3390/s22155701<i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under ImpactsYuguang Fu0Yaoyu Zhu1Tu Hoang2Kirill Mechitov3Billie F. Spencer4School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, SingaporeCCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing 100088, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100191, ChinaEmbedor Technologies, Champaign, IL 61820, USADepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USABridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events.https://www.mdpi.com/1424-8220/22/15/5701bridge impact detectionrapid condition assessmentwireless smart sensorsstructural health monitoringartificial neural network |
| spellingShingle | Yuguang Fu Yaoyu Zhu Tu Hoang Kirill Mechitov Billie F. Spencer <i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts Sensors bridge impact detection rapid condition assessment wireless smart sensors structural health monitoring artificial neural network |
| title | <i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts |
| title_full | <i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts |
| title_fullStr | <i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts |
| title_full_unstemmed | <i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts |
| title_short | <i>xImpact</i>: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts |
| title_sort | i ximpact i intelligent wireless system for cost effective rapid condition assessment of bridges under impacts |
| topic | bridge impact detection rapid condition assessment wireless smart sensors structural health monitoring artificial neural network |
| url | https://www.mdpi.com/1424-8220/22/15/5701 |
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