Rosario Ceravolo

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 usefulness of a semiactive control to reduce the overturning vulnerability of a rigid block on a rigid plane under earthquake excitation. The proposed feedback law is used to set the stiffness of restraints placed at the 2 lower corners of the block. The performance of the semiactive control is numerically validated by subjecting the block to 100 recorded accelerograms. Specific simulations are performed to study the effect of different anchorage design parameters on the utility of the control.

This paper deals with a high-sensitivity method for the assessment of damage in high-strength fibres exposed to UV radiation. A recently developed experimental testing machine, based on an optical measurement system and electro-magnetic driving force, was used to characterize fibre materials. Stiffness, damping, and non-linearity were measured on several Kevlar® fibre samples previously exposed to UV light for different lengths of time. The results show that UV radiation increases the material non-linearity by amounts which can be clearly observed even at low vibration amplitudes.

This research evaluates, at microscopic level, the failure mechanisms of pultruded glass fibre reinforced polymers (GFRP) column elements after they have been subjected to temperature stress and compressive load. A scanning electron microscope (SEM) performed the investigation, with the GFRP samples cut into small sized specimens and treated with specific coatings. The analysed samples exhibited particular features, which have been interpreted with the support of fractography.

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.

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs).

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.

Modal expansion techniques are typically used to expand the experimental modal displacements at sensor positions to all unmeasured degrees of freedom. Since in most cases, sensors can be attached only at limited locations in a structure, an expansion is essential to determine mode shapes, strains, stresses, etc. throughout the structure which can be used for structural health monitoring.

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.