Gaetano Miraglia

One of the main problems concerning the field of Structural Health Monitoring (SHM) is the unavai...lability of data from different structural conditions. This is especially true for civil structures, where the collection of data from different damage states is often infeasible or economically inconvenient, particularly when dealing with architectural heritage structures.

This paper investigates the use of rank aggregation strategies for the finite element model calibration of monitored masonry structures subjected to earthquakes. Ranking is used to obtain optimal results from several competing optimization strategies, with the final aim of establishing a numerical model of reference to support the existing monitoring systems installed on the structures.

Bayesian model calibration techniques are commonly employed in the characterization of nonlinear dynamic systems, as they provide a conceptual and effective framework to deal with model uncertainties, experimental errors and procedure assumptions. This understanding has resulted in the need to introduce a model discrepancy term to account for the differences between model-based predictions and real observations.

Within the context of civil structures, a monitoring system supported by an intelligent diagnostic features extraction allows to keep under observation the overall health state of a building. In most cases, the diagnostic features are influenced by Environmental and Operational Variations (EOVs) which cause fluctuations that can be confused with the appearance of damage, or worse, hide it.

The identification of hysteretic degrading systems exposed to nonstationary loading is a paramount research topic, especially in the case of structures subjected to ground motion excitations. In this paper, the data recorded by a masonry building are used to detect the presence of seismic damage. To this aim, a parametric nonlinear identification is performed by adopting a Bouc– Wen-type multiple oscillator model. Starting from the results of the identification process, a damage index based on the degrading stiffness matrix is defined.

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.

One of the main drawbacks of using entropy-based indicators for damage detection is known to be their sensitivity to the energy introduced into the system. Indeed, energy supply can lead to a more deterministic behavior of the structure and thus to a reduction of the entropy. As a solution to these issues, in this paper an indicator based on two measures of spectral entropy is proposed to assess the occurrence of damage in masonry buildings, even in the presence of an external unmeasured input (e.g. minor seismic event).

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.