Acknowledgement
The authors are grateful to Dr. Ki-Tae Park from KICT, South Korea. Dr K.T. Park has actively contributed in providing bridge information, which was used in the preliminary investigation in this paper.
International conference on construction engineering and project management (국제학술발표논문집)
- 2005.10a
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- Pages.533-538
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- 2005
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- 2508-9048(eISSN)
AN ARTIFICIAL NEURAL NETWORK MODEL FOR THE CONDITION RATING OF BRIDGES
- Jaeho Lee (School of Engineering, Griffith University PMB 50, Gold Coast Campus) ;
- Kamal Sanmugarasa (School of Engineering, Griffith University PMB 50, Gold Coast Campus) ;
- Michael Blumenstein (School of Information and Communication Technology, Griffith University PMB 50, Gold Coast Campus)
- Published : 2005.10.16
Abstract
An outline of an Artificial Neural Network (ANN) model for bridge condition rating and the results of a pilot study are presented in this paper. Most BMS implementation systems involve an extensive range of data collection to operate accurately. It takes many years to effectively implement a BMS using existing methodologies. This is due to unmatched data requirements. Such problems can be overcome by adopting the ANN model presented in this paper. The objective of the proposed model is to predict bridge condition ratings using historical bridge inspection data for effective BMS operation.
Keywords
- Bridge Management System (BMS);
- Condition Rating Model;
- BMS Implementation;
- Artificial Neural Networks (ANNs)