Damage detection in hyperbolic cooling towers using vibration characteristics and artificial neural network
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Chathurangi Madusha Randiligama Sepala Mudiyanselage Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This research developed and applied a method based on vibration characteristics and Artificial Neural Network to detect, locate, and quantify damages in hyperbolic cooling towers. Hyperbolic cooling towers are large structures built to have long service lives. They are subjected to temperature variations, which along with material deterioration, with age and random actions, can inflict damage to the structure. The proposed method is easy to implement and can provide early warning of damage in the cooling tower to enable appropriate remedial action and prevent its collapse. This research will contribute to the safe and efficient operation of cooling towers.
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| ID Code: | 227918 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Thambiratnam, David, Chan, Tommy, & Fawzia, Sabrina |
| Keywords: | Hyperbolic Cooling Towers, Damage Prediction, Structural Health Monitoring, Vibration Characteristics, Vibration Based Damage Detection Technique, Absolute Change in Mode Shape Curvature, Artificial Neural Network, Component specific damage indices, Experimental Testing, Quantifying Damage |
| DOI: | 10.5204/thesis.eprints.227918 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Civil & Environmental Engineering |
| Institution: | Queensland University of Technology |
| Deposited On: | 22 Feb 2022 00:29 |
| Last Modified: | 22 Feb 2022 00:32 |
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