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智能无线系统可实现经济高效的冲击下桥梁状态快速评估

: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts.

机构信息

School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.

CCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing 100088, China.

出版信息

Sensors (Basel). 2022 Jul 29;22(15):5701. doi: 10.3390/s22155701.

DOI:10.3390/s22155701
PMID:35957256
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9371040/
Abstract

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.

摘要

桥梁被超高车辆或船只撞击是重大突发事件。由于其不可预测性,许多撞击事件未被察觉或报告,但它们可能导致结构失效或隐藏损伤,加速桥梁的长期退化。因此,始终在线监测对于部署系统至关重要,可以通过可靠检测此类事件和快速评估桥梁状况来提高桥梁安全性。

传统的使用有线传感器的桥梁监测系统由于其高昂的安装成本,对于广泛应用来说过于昂贵。在本文中,开发了一种智能无线监测系统作为具有成本效益的解决方案。它采用超低功耗、事件触发的无线传感器原型,能够按需进行高保真感测,而不会错过不可预测的撞击事件。

此外,所提出的系统采用了基于智能人工智能 (AI) 的框架,通过使用人工神经网络来快速评估桥梁。具体来说,它可以识别撞击位置并估计撞击的峰值力和冲量。所获得的撞击信息可用于提供对桥梁状况的早期估计,使桥梁工程师能够优先分配资源,及时检查更严重的撞击。

通过全尺寸现场测试验证了所提出的监测系统的性能。测试结果表明,所开发的系统能够捕捉到桥梁撞击的开始,提供高质量的同步数据,并对撞击事件下的桥梁进行快速损伤评估,实现了约 2 米的撞击定位误差、1 kN 的峰值力估计和 0.01 kN·s 的冲量估计。计划在未来进行长期部署,以证明其在实际撞击事件中的可靠性。

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本文引用的文献

1
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Sensors (Basel). 2018 Dec 18;18(12):4480. doi: 10.3390/s18124480.
2
The wreck of Amtrak's Sunset Limited: news coverage of a mass transport disaster.美国铁路客运公司日落有限公司列车失事:一场大规模运输灾难的新闻报道。
Disasters. 1997 Mar;21(1):4-19. doi: 10.1111/1467-7717.00041.
基于事件分类神经网络的快速铁路桥梁冲击检测方法。
Sensors (Basel). 2023 Mar 22;23(6):3330. doi: 10.3390/s23063330.