Crowdsensing-based automated operational modal analysis for indirect bridge structural health monitoring

In this study, an automated identification procedure for crowdsensing-based indirect Bridge Structural Health Monitoring (iBSHM) is presented. The scope is to estimate the modal parameters of a cycle-pedestrian bridge using only acceleration data collected by smartphones installed on board. The proposed method introduces several innovations. First, natural frequencies are identified using the Stochastic Subspace Identification (SSI) algorithm. Second, the method enables the estimation of damping ratios, which are typically neglected in existing crowdsensing applications. Third, it uses the Singular Value Decomposition (SVD) step within the SSI framework to extract singular vectors corresponding to dominant frequencies, thereby isolating the modal components of the signal and enabling the estimation of mode shapes. The proposed identification procedure is experimentally tested and validated with data from a real footbridge in Bologna (Italy). The field test was carried out with multiple passages of a commercial bicycle, using a single smartphone installed on board. The obtained results are compared with those from a previous test conducted with the same experimental setup and case study, but using a different analysis methodology. Satisfactory comparability and repeatability of the results were achieved. • A new automated approach is proposed for indirect SHM. • Modal parameters are estimated statistically through crowdsensing. • The SVD step in SSI is used to extract singular vectors. • The identified parameters are compared with a state-of-the-art technique. • The proposed methodology shows repeatability in the field.