Aram Cornaggia

Dr. Aram Cornaggia

University of Bergamo, Bergamo, Lombardy, Italy
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2015 in Mechanics Research Communications
Verified
Journal Article
Closed access
VBV. Buljak
V. Buljak
GCGiuseppe Cocchetti
Giuseppe Cocchetti
Aram CornaggiaAram Cornaggia
Aram Cornaggia
GMG. Maier
G. Maier

23 May 2022 in Infrastructures
Verified
Journal Article
Open access
PDF file
Aram CornaggiaAram Cornaggia
Aram Cornaggia
RFRosalba Ferrari
Rosalba Ferrari
MZMaurizio Angelo Zola
Maurizio Angelo Zola
EREgidio Rizzi
Egidio Rizzi
Carmelo GentileCarmelo Gentile
Carmelo Gentile
The paper is part of a case study concerning the structural assessment of a historical infrastructure in the local territory, a road three-span reinforced concrete arch bridge over a river, built by the end of World War I (1917). The purpose of the paper is twofold: first, in-situ acquired response data are systematically analysed by specific signal processing techniques, to form a devoted methodological procedure and to extract useful information toward possible interpretation of the current structural conditions; second, the deciphered information is elaborated, in view of obtaining peculiar conceptualisations of detailed features of the structural response, as meant to achieve quantitative descriptions and modelling, for final Structural Health Monitoring (SHM) and intervention purposes. The proposed methodology, integrating self-implemented and adapted classical signal processing methods, and refined techniques, such as Wavelet analysis and ARMA models, assembles a rather general, systematic methodological approach to signal processing, highlighting the capability to extract useful and fundamental information from acquired response data, also endowed of a non-stationary character, toward final structural interpretation, identification and modelling, thus enabling for developing a reliable and effective SHM platform, on strategic ageing infrastructures. For the present case study, non-stationary characteristics of the response signals are revealed and flattened out, to identify the underlying fundamental frequencies of the infrastructure and to advance particular interpretations of its current structural behaviour, in forming an enlarging structural consciousness of the bridge at hand.


1 April 2017 in Engineering Structures
Verified
Journal Article
Closed access
VBV. Buljak
V. Buljak
GCGiuseppe Cocchetti
Giuseppe Cocchetti
Aram CornaggiaAram Cornaggia
Aram Cornaggia
GMG. Maier
G. Maier

10 November 2023 in Packaging Technology and Science
Verified
Journal Article
Closed access
Tomasz GajewskiTomasz Gajewski
Tomasz Gajewski
Jakub GrabskiJakub Grabski
Jakub Grabski
Aram CornaggiaAram Cornaggia
Aram Cornaggia
Tomasz GarbowskiTomasz Garbowski
Tomasz Garbowski

5 December 2023 in Materials
Verified
Journal Article
Open access
PDF file
Tomasz GarbowskiTomasz Garbowski
Tomasz Garbowski
Aram CornaggiaAram Cornaggia
Aram Cornaggia
Maciej ZaborowiczMaciej Zaborowicz
Maciej Zaborowicz
SSSławomir Sowa
Sławomir Sowa
Reinforced concrete bridges deteriorate over time, therefore displaying a regular need for structural assessment and diagnosis. The reasons for their deterioration are often the following: (a) intensive use, (b) very dynamic loads acting for long periods of time, (c) and sometimes chemical processes that damage the concrete or lead to corrosion of the reinforcement. Assuming the hypothesis that both the stiffness of the material and its density change over time, these parameters shall be identified, preferably in a non-destructive way, in different locations of the investigated structure. Such task is expected to be possibly exerted by means of one or more tests, which must not be laborious or cause the bridge to be out of service for a long time. In this paper, an attempt is made to prepare a procedure based on dynamic tests supplemented with several static measurements, in order to identify the largest number of parameters in the shortest possible time, within an inverse analysis methodology. The proposed procedure employs a popular algorithm for minimizing the objective function, i.e., trust region in the least square framework, as part of the inverse analysis, where the difference between measurements made in situ and those calculated numerically is minimized. As a result of the work performed, optimal sets of measurements and test configurations are proposed, allowing the searched parameters to be found in a reliable manner, with the greatest possible precision.