Bridge frequency identification in city bus monitoring: A coherence-PPI algorithm

•A new Coherence-PPI method for practical drive-by bridge inspection is proposed.•Effects of road roughness, noise, and vehicle parameter variations are eliminated.•The algorithm encourages measurement variability to filter bridge frequencies.•The method is validated through numerical studies and field tests using city buses. Recently, drive-by-bridge inspection methods have attracted substantial scholarly interest; however, their practical implementation has encountered significant challenges. In engineering practice, even simply extracting bridge frequencies from ordinary or commercial vehicles appears to be difficult; components related to factors such as road roughness often dominate vehicle vibration responses. This study proposes a novel coherence-PPI (Prominent Peak Identification) algorithm based on the Bayesian framework and applies it to city bus bridge monitoring to extract bridge frequencies. The fundamental idea is to recognize the bridge frequency as a common vibration component across various vehicle runs. The algorithm comprises the following three steps: First, coherences were computed for all vehicle runs to interpret the signal relationships. Second, a Bayesian framework was established to statistically determine the threshold that can maximize the occurrence of bridge frequency. Third, the prominent peaks (PPs) were selected based on the threshold, and their distribution was counted to identify the fundamental frequency of the bridge. In contrast to the previous studies that focused on avoiding differences (e.g., by trying to reduce variation, driving in the same lane, and using the same speed), this methodology encourages the introduction of variability in drive-by measurements to filter bridge frequencies, rendering it particularly compelling for practical engineering applications. The proposed methodology was validated through numerical studies, including the Monte Carlo method, and field tests using city buses. The results demonstrated that the method can effectively eliminate the effects of road roughness, environmental noise, and vehicle parameter variations and accurately identify the bridge frequency.