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Numerical and operational modal analyses of the "Ponte delle Torri", Spoleto, Italy

Autor(en):






Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: Structural Analysis of Historical Constructions (SAHC) 2016, Leuven, Belgium, 13-15 September 2016
Veröffentlicht in:
Jahr: 2016
Abstrakt:

The Ponte delle Torri is a large historical construction that connects Colle Sant'Elia with Mount Monteluco in Spoleto, Italy. It was probably built in the XIII century, possibly on Etruscan or Roman ruins. The bridge superstructure is made up of a pedestrian deck, provided with a water canal on one side, supported by lancet arcades and stone piers known as “towers”. As testified by recent documentation and on-site inspections, the bridge's state of damage is rather widespread and extended, urging local and national institutions to start suitable structural monitoring activities, which were included within the RoMA (Resilience enhancement of a Metropolitan Area) project. The project comprises many monitoring techniques (high-resolution photogrammetry, 3D laser scanning, FBG monitoring, satellite InSAR, etc.) to assess the bridge cracks pattern and its structural health by a multidisciplinary approach that permits mutual validation. In particular, the present paper describes the numerical and operational modal analyses performed for the study of the structure and the methodology for the validation of the implemented Finite Element Models (FEMs). As a first step for the study of the bridge, the modal analysis of a preliminary FEM was executed in order to have an idea of the dynamic behavior of the structure and to guide the design of the on-site sensors positions. In such positions ambient noise was measured with velocimeters. Then, more refined FEMs were constructed and validated. In particular, mono-, bie tri-dimensional FEMs were generated through different numerical techniques and their resulting modal analyses were compared with the results of Operational Modal Analysis (OMA) techniques applied to the recorded velocimeters data.

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  • Reference-ID
    10073403
  • Veröffentlicht am:
    02.10.2016
  • Geändert am:
    29.05.2022
 
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