Mobile crowdsensing for structural health monitoring: field validation

A major challenge in bridge Structural Health Monitoring (SHM) is the cost associated with deploying and maintaining sensor systems. Although regular monitoring is essential for ensuring infrastructure safety and longevity, limited financial resources hinder widespread and continuous implementation. In this respect, mobile crowdsensing offers a promising low-cost solution by eliminating the need for dedicated sensing devices and their associated maintenance. Instead, it utilizes data collected from smartphones and other personal devices carried by everyday users. This paper presents a framework for processing crowdsensed vibration data for bridge monitoring applications. The framework incorporates qualityweighted signal processing and bootstrap resampling to extract natural frequency estimates from heterogeneous smartphone recordings. The efficacy and accuracy of the proposed method are demonstrated through application to a real pedestrian bridge, with results validated against a benchmark study conducted on the same structure using professional instrumentation. Overall, this research contributes to the development of a flexible and scalable framework for applying mobile crowdsensing in bridge structural health monitoring.