The accuracy of structural models significantly depends on the availability of input design parameters such as the dimensions of resisting elements, material properties, joint connection details, loading conditions, etc. When dealing with old structures, a lot of this information is not accessible, which leads to the introduction of many uncertainties into the model. In the last few decades, several structural health monitoring (SHM) techniques have been used to compensate such lack of data and perform more reliable structural analyses. However, SHM might result in costly and time-consuming surveys that can be as well affected by uncertainties due to environmental effects, measurement noise, and signal processing errors. This paper discusses and introduces a straightforward SHM procedure to collect the fundamental information to achieve a better accuracy and reliability of the finite element model of the considered building. The methodology starts with the acquisition of structural data from available construction drawings and Building Information Modeling (BIM) to create a preliminary finite element model. Then a series of non-destructive tests are performed using thermal camera, sclerometer, pachometer to identify the fundamental structural parameters. In addition, environmental noise measured through accelerometers is used to determine the principal frequencies of the structure. Harmonic excitations are then horizontally applied to resisting elements using a vibrodyne. The proposed methodology was tested on an RC school building located in Melzo (Milan, Italy). Statistical and signal processing techniques are then utilized to interpret the acquired signals and consequently update and calibrate the structural model for the vulnerability assessment. The processed signals revealed substantial differences with the original FE model. Thus, rigidity, mass and damping were modified accordingly.

Dynamic characterization and vulnerability assessment of a school building in Italy / Cardoni, A.; Noori, A. Z.; Marasco, S.; Domaneschi, M.; Villa, V.; Ansari, F.; Caldera, C.; Cimellaro, G. P.. - STAMPA. - 1:(2019), pp. 459-466. ((Intervento presentato al convegno 12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 tenutosi a Stanford University, usa nel 2019.

Dynamic characterization and vulnerability assessment of a school building in Italy

Cardoni A.;Marasco S.;Domaneschi M.;Villa V.;Caldera C.;Cimellaro G. P.
2019

Abstract

The accuracy of structural models significantly depends on the availability of input design parameters such as the dimensions of resisting elements, material properties, joint connection details, loading conditions, etc. When dealing with old structures, a lot of this information is not accessible, which leads to the introduction of many uncertainties into the model. In the last few decades, several structural health monitoring (SHM) techniques have been used to compensate such lack of data and perform more reliable structural analyses. However, SHM might result in costly and time-consuming surveys that can be as well affected by uncertainties due to environmental effects, measurement noise, and signal processing errors. This paper discusses and introduces a straightforward SHM procedure to collect the fundamental information to achieve a better accuracy and reliability of the finite element model of the considered building. The methodology starts with the acquisition of structural data from available construction drawings and Building Information Modeling (BIM) to create a preliminary finite element model. Then a series of non-destructive tests are performed using thermal camera, sclerometer, pachometer to identify the fundamental structural parameters. In addition, environmental noise measured through accelerometers is used to determine the principal frequencies of the structure. Harmonic excitations are then horizontally applied to resisting elements using a vibrodyne. The proposed methodology was tested on an RC school building located in Melzo (Milan, Italy). Statistical and signal processing techniques are then utilized to interpret the acquired signals and consequently update and calibrate the structural model for the vulnerability assessment. The processed signals revealed substantial differences with the original FE model. Thus, rigidity, mass and damping were modified accordingly.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2818138