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.
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.
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.