Damage identification and condition assessment of building structures using frequency response functions and neural networks
Bandara, Arachchillage Rupika Priyadarshani (2013) Damage identification and condition assessment of building structures using frequency response functions and neural networks. PhD thesis, Queensland University of Technology.
This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Chan, Tommy, Thambiratnam, David, & Perera, Nimal|
|Keywords:||Artificial Neural Network, Frequency Response Functions, Principal Component Analysis, Damage detection, Damage Location Identification|
|Divisions:||Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Institution:||Queensland University of Technology|
|Deposited On:||08 Jul 2013 06:09|
|Last Modified:||08 Sep 2015 03:01|
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