A clustering-based strategy for automated structural modal identification

Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long-term monitoring, such as the Rio-Niteroi Bridge in Brazil, the former Z24 Bridge in Switzerland and the Millau Bridge in France. In fact, some structures are continuously monitored to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, that is, natural frequencies, damping rations and mode shapes. Therefore, the development and validation of tools for the automatic modal identification during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters' estimates. This work proposes a novel methodology to perform, automatically, the modal identification based on the modes' estimates data generated by any parametric system identification method. To assess the proposed methodology, several tests are conducted using numerically generated signals, as well as experimental data obtained from a simply supported beam and from a motorway bridge.