Giorgia Coletta

One key issue in the Structural Health Monitoring (SHM) of buildings is the influence of the soil on the dynamics of the system. The lack of accurate information on soil-structure interaction represents a source of significant uncertainty and generates difficulties in assessing the state of structural health. In this respect, satellite data could represent a valuable tool for soil knowledge. This paper presents the first study of satellite data coming from the environmental Copernicus program of the European Space Agency (ESA) for the alternative application in the field of SHM.

An advantageous opportunity in the context of Structural Health Monitoring (SHM) is represented b...y the use of satellite remote sensing data, which are becoming increasingly relevant in many areas with different purposes thanks to their wide temporal and spatial availability, versatility and low cost.

The Peridynamic (PD) theory is a modern nonlocal (nonlinear, elastic /inelastic, with-out/with me...mory) theory able to deal with long-range forces and discontinuity in materials. For this reason the theory is suitable for the monitoring of masonry structures.

This paper presents the results of the dynamic tests conducted on a historical reinforced concrete arch bridge located in the Tuscan-Emilian Apennines, in the province of Parma (Italy). The design of the sensors location was determined in order to investigate the possible separation into bodies operated by the joints between the different spans. The ambient vibration data allowed the dynamic characterization of the 3-span arch bridge with the total length of 146 m and 18 m in width.

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.

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 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.