Compressive Sensing for Operational Modal Analysis of a Prestressed Concrete Bridge

Many signals from structural monitoring scenarios exhibit sparsity in the frequency domain. This implies the potential application of Compressive Sensing (CS) techniques to minimize the amount of transmitted information. CS enables the retrieval of data vectors from a subset of the original entries, allowing the recovery of a previously sampled signal with significantly fewer samples than recommended by the Nyquist-Shannon theorem. With a reduced data flow, structural monitoring applications can rely on IoT solutions, generating various advantages such as less demanding installation and maintenance processes, reduced costs and decreased energy consumption. However, the applicability of CS techniques for structural dynamic identification purposes must still be investigated. Starting from these premises, an application of CS has been carried out using the response records of a prestressed concrete bridge monitored by IoT accelerometers. The modal properties of the bridge have been evaluated after applying a CS recovery technique and the results have been compared to the ones obtained using the original records. Different sampling frequencies of the compressed records have been used to find the best trade-off between data reduction and modal parameters accuracy.