Orientador: Vicente Lopes Junior / Resumo: Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory. / Mestre
| Identifer | oai:union.ndltd.org:UNESP/oai:www.athena.biblioteca.unesp.br:UEP01-000879661 |
| Date | January 2016 |
| Creators | Guimarães, Ana Paula Alves |
| Contributors | Universidade Estadual Paulista "Júlio de Mesquita Filho" Faculdade de Engenharia (Campus de Ilha Solteira). |
| Publisher | Ilha Solteira, |
| Source Sets | Universidade Estadual Paulista |
| Language | Portuguese |
| Detected Language | English |
| Type | text |
| Format | f. |
| Relation | Sistema requerido: Adobe Acrobat Reader |
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