Publicação
Automatic system identification algorithm for processing ambient vibration data
| Resumo: | When large quantities of data are acquired in long-term monitoring works, the use of automatic modal identification procedures is mandatory for the feasibility of real-time data interpretation, damage detection, model updating, or others. This paper presents an innovative algorithm for real-time remote processing the information recorded by ambient vibration tests. This algorithm aims at generating and interpreting the stabilization diagrams resultant from the application of parametric methods (such as the Stochastic Subspace Identification – SSI) to the collected time domain data. The proposed algorithm was validated in two stages: (i) considering numerical examples with artificially generated data and (ii) in a field test for tracking the stiffening process of concrete since early ages. The results of these two rounds of validation tests evidenced the high accuracy of the automatic estimations of this new algorithm and thus, the feasibility for its incorporation as a tool in future Structural Health Monitoring works. |
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| Autores principais: | Aguilar, Rafael |
| Outros Autores: | Ramos, Luís F.; Azenha, Miguel |
| Assunto: | System identification Modal analysis |
| Ano: | 2011 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | When large quantities of data are acquired in long-term monitoring works, the use of automatic modal identification procedures is mandatory for the feasibility of real-time data interpretation, damage detection, model updating, or others. This paper presents an innovative algorithm for real-time remote processing the information recorded by ambient vibration tests. This algorithm aims at generating and interpreting the stabilization diagrams resultant from the application of parametric methods (such as the Stochastic Subspace Identification – SSI) to the collected time domain data. The proposed algorithm was validated in two stages: (i) considering numerical examples with artificially generated data and (ii) in a field test for tracking the stiffening process of concrete since early ages. The results of these two rounds of validation tests evidenced the high accuracy of the automatic estimations of this new algorithm and thus, the feasibility for its incorporation as a tool in future Structural Health Monitoring works. |
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