| Author: | Wei, Yuanhao |
| Title: | A bayesian probabilistic approach for structural damage detection |
| Advisors: | Ni, Y. Q. (CEE) |
| Degree: | M.Sc. |
| Year: | 2018 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Structural health monitoring Structural analysis (Engineering) Structural failures -- Investigation |
| Department: | Faculty of Construction and Environment |
| Pages: | xv, 240 pages : color illustrations |
| Language: | English |
| Abstract: | This study aims to develop a structural health monitoring (SHM) method enabled by Sparse Bayesian Learning and the Relevance Vector Machine (RVM) on the basis of structural response signals. The structural response signals are generated by a simulated four degrees-of-freedom structure under seismic loads. And the structural damage is characterized by the reduction of structural stiffness. This study includes the following steps: (1) obtaining the frequency response function (FRF) of the simulated structure through modal analysis; (2) determining the frequency bands of the structure which are sensitive to damage; (3) getting and normalizing the structure dynamic response signals; (4) formulating frequency-domain health condition indexes (HCI) via a linear transformation; (5) establishing regression models about the real and imaginary parts of HCI when the simulated structure is undamaged based on the relevance vector machine (RVM) and the sparse Bayesian learning; (6) quantitative analysis of residuals between predicted HCI and actual HCI. If the deviation between the actual HCI and the predicted HCI is not apparent, it is believed that the structure has no damage. If the predicted HCI deviate considerably from the actual HCI, the damage is identified, and mostly the deviation increases with damage degree of the structure. By analyzing the simulated structures under different damage conditions, the effectiveness of this method in structural damage detection is verified. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022244147403411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 20.96 MB | Adobe PDF | View/Open |
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