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

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.

Direct Analysis of a Steel Railway Bridge via Monitoring System of an Instrumented Structure

“…The data obtained from the measuring sensor using the SHM technique can be directly used to analyze the health condition of the railway bridge structure. Many researchers make observations to get real data by applying low-cost instrumentations installed on the rail bridge to determine vertical and transverse deformation [23]- [27]. This study suggests that transverse deformation can provide an assessment of the condition of a railway bridge.…”

Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection

“…These sensors record measurable quantities of bridge response caused by structural excitation, for example, excitation from a passing train or a vehicle‐bridge strike. Specific to bridge strike detection, previous research efforts have used threshold‐based approaches to classify strikes versus trains (Byrne, 2009; Fu et al., 2022; Yang, 2015). Threshold‐based classification systems are susceptible to event misclassification; if the threshold is too high, low severity strikes may be undetected, and if the threshold is too low, the system may confuse a passing train with a strike.…”

An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection

“…The study monitored the displacements and triggered an alarm once the set threshold was exceeded, allowing engineers to carry out visual inspection of the bridge. Fu et al [ 16 ] recently developed a wireless monitoring system for rapid condition of long-span bridges subjected to impacts. However, all of these strategies involve the use of predetermined thresholds to detect impacts.…”