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xImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts.
Fu, Yuguang; Zhu, Yaoyu; Hoang, Tu; Mechitov, Kirill; Spencer, Billie F.
Afiliação
  • Fu Y; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.
  • Zhu Y; CCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing 100088, China.
  • Hoang T; Department of Civil Engineering, Tsinghua University, Beijing 100191, China.
  • Mechitov K; Palo Alto Research Center, Palo Alto, CA 94304, USA.
  • Spencer BF; Embedor Technologies, Champaign, IL 61820, USA.
Sensors (Basel) ; 22(15)2022 Jul 29.
Article em En | MEDLINE | ID: mdl-35957256
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.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Computadores / Inteligência Artificial Tipo de estudo: Health_economic_evaluation / Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Computadores / Inteligência Artificial Tipo de estudo: Health_economic_evaluation / Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article