Marica Pecorelli

The interest in automatic modal parameter extraction techniques has increased significantly in recent years because of the rising demand for Continuous Structural Health Monitoring (CSHM) of civil structures and infrastructures. The wider use of CSHM is related to its capability for early damage detection and therefore to its usefulness for planning maintenance and strengthening interventions.

A reliable and predictive model of an existing structure entails the use of model updating techniques, which are usually performed on the basis of operational modal analysis campaigns. In this paper, a new model calibration strategy is proposed that adopts a multiphysics approach to exploit data collected by both static and dynamic monitoring systems. More specifically, mechanical and temperature data are assimilated into the model through a thermoelastic updating.

The theory of cointegration, usually employed in econometric studies, has proved very powerful in the context of Structural Health Monitoring (SHM), where it can be used to distinguish operational and environmental changes of dynamic features from those related to the evolution of damage. The different nature of the effects imposed by operational and environmental variations on structural response required here an extension of the theory of cointegration from the linear to the nonlinear field. For this purpose, a nonlinear multivariate regression has been developed.