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Improving Bridge Deterioration Modelling Using Rainfall Data from the Bureau of Meteorology

Huang, Q., Ong, K.L. and Alahakoon, D.

    appropriate maintenance is paramount. Often, au- thorities are faced with limited funding and available contractors who are able to carry out the maintenance checks and works. Therefore, a predictive model that can forecast the future state of a bridge component will enable the authority to prioritise and deploy re- sources to where it is most needed. The challenge faced in this paper is the requirement from the Victo- rian road authorities to develop an e ective predictive model. Prior attempts have been made by using dif- ferent techniques to construct an alternate predictive model but with limited results. The problem lie in the data itself. With data manually recorded by di er- ent contractors, it is noisy and erroneous. Attempts to data cleaning has led to little improvement in the overall model performance. Finally we turned to data augmentation to increase the proportion of reliable data. In our quest to do so, we ended up pulling rain- fall data from the BoM to augment the data provided by VicRoads. We consider rainfall data as a candidate for augmentation because literature in civil engineer- ing has correlated bridge component deterioration to the presence of water moisture. Since high rainfall contributes to increased deterioration, leveraging the rainfall information should lead to improved predic- tive performance. Initial experiments on the predic- tive performance of the baseline and \high rainfall" models suggest the viability of this approach.
Cite as: Huang, Q., Ong, K.L. and Alahakoon, D. (2015). Improving Bridge Deterioration Modelling Using Rainfall Data from the Bureau of Meteorology. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 161-167
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