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Steel bridges are susceptible to fatigue damage under traffic loading, and many bridges operate with existing cracks. The discovery and long-term monitoring of those fatigue cracks are critical for safety evaluations. In previous studies, the ability of the soft elastomeric capacitor (SEC) sensor that measures large-area strain was validated for detecting and monitoring fatigue crack growth in a laboratory environment. In this study, the performance of the technology is evaluated for field applications, for which an approach for long-term monitoring of fatigue cracks is developed. The approach consists of an integrated system, termed the wireless large-area strain sensors (WLASS), for wireless data collection and storage and a signal processing algorithm for monitoring fatigue cracks with bridge response induced by traffic loading. In particular, the WLASS consists of soft elastomeric capacitors (SECs) combined with sensor boards to convert capacitance to a measurable change in voltage and a wireless sensing platform equipped with event-triggered sensing, wireless data collection, cloud storage, and remote data retrieval. A modified crack growth index (CGI) is developed through detection of peak-to-peak amplitudes of the wavelet transform. Using the measurements from the WLASS, the modified CGI is able to obtain the crack status under various loading events due to random traffic loads. The performance of the developed approach is validated using a steel highway bridge.
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