| Grant number: | 19/19684-3 |
| Support Opportunities: | Regular Research Grants |
| Start date: | May 01, 2020 |
| End date: | April 30, 2022 |
| Field of knowledge: | Engineering - Mechanical Engineering - Mechanics of Solids |
| Principal Investigator: | Samuel da Silva |
| Grantee: | Samuel da Silva |
| Host Institution: | Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil |
| City of the host institution: | Ilha Solteira |
| Associated researchers: | Americo Barbosa da Cunha Junior ; Michael Douglas Todd |
Abstract
Many points are still open in the literature for the detection and quantification of damage in structures assembled by bolted joints. The most challenging to overcome is to represent the nonlinear damping and hysteresis effect adequately using reduced models robust to uncertainties. The present project aims to introduce new approaches based on data-driven identification using Gaussian Processo Regression with Nonlinear AutoRegressive input eXogenous, named by GP-NARX, as well as applications of harmonic probing, harmonic balance, and Volterra series. The Bouc-Wen model, LuGre model, and Iwan model will be defined by describing hysteresis impacts in the bolted joints to recognize the fluctuations of nonlinear vibrations possible associated with damage. The step of damage detection and classification proposed will be based on hypothesis tests, and machine learning algorithms handling damage-sensitive features extracted from the hysteresis loop identified with new indices. All details, motivation, collaboration, expected results, scientific contributions, and support staff are described in this proposal. (AU)
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