Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors

Abstract

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

Sensing skin technology for structural health monitoring: from proof-of-concept to field validation

“…More details about the field setup, sensing hardware, and modified CGI are reported in Ref. 21 Field data has been collected since late 2021, and a web interface is available to extract CGIs. Over the last few years, some field visits were necessary to fix power and noise issues, which could ultimately be minimize or eliminated through the commercial production of the sensing technology.…”

Applications of the Generalized Morse Wavelets: A Review

“…As can be appreciated from the previous studies, the analysis of structural systems has been performed through GMWs. Another example of this is shown in [73], where a wireless large-area strain sensor (WLASS) was used to measure largearea strain fatigue cracks of steel bridge structures under traffic loading. The CWT was used to analyze the WLASS data by setting the parameters of GMWs as follows γ = 1.5, and P 2 = 3.…”

A review on fatigue monitoring of structures

“…Moreover, strain measurements can be effective for crack detection because of the sensitivity of strain change to the opening and closing of a crack. However, sensors may not provide adequate information for fatigue crack monitoring since their small size hinders their ability to cover an adequate surface for areas that are prone to fatigue cracks (Taher et al ., 2022).…”