Data mining for lifetime prediction of metallic components
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
A user driven data mining process model and learning system
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
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We attempt to develop a practical decision support system for the damage assessment of structural corrosion. This system aims to aid inexperienced inspectors to judge whether a certain bridge should be repaired or not. For this purpose, it is attempted to apply the neural network technique for the damage assessment. The learning ability of the neural network is useful to save the working time and load necessary in the inspection and analysis.