TY - JOUR
T1 - xImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts
AU - Fu, Yuguang
AU - Zhu, Yaoyu
AU - Hoang, Tu
AU - Mechitov, Kirill A.
AU - Spencer, Billie F.
N1 - The authors gratefully acknowledge the support of this research by NSF SBIR under Grant #1913947, Nazarbayev University Research Fund under Grant #SOE2017003, Youth Project Foundation of CCCC #2021-ZJKJ-QNCX03, Academician Project Foundation of CCCC #YSZX-03-2021-01-B, ZJU-UIUC Institute Research under Grant #ZJU083650, Federal Railroad Administration under Grant #DTFR53-17-C00007, MOE Tier 1 under Grant #RG121/21.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
KW - bridge impact detection
KW - rapid condition assessment
KW - wireless smart sensors
KW - structural health monitoring
KW - artificial neural network
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U2 - 10.3390/s22155701
DO - 10.3390/s22155701
M3 - Article
C2 - 35957256
SN - 1424-8220
VL - 22
JO - Sensors
JF - Sensors
IS - 15
M1 - 5701
ER -