Improving estimates of asset condition using historical data

Regularly updating the estimated conditions of the asset base is important when managing infrastructure networks. This is usually done using a sampling programme in which some or all of the assets are inspected over a specified time horizon. Commonly, only a proportion of assets are inspected in each year. Therefore, the asset managers need to be able to use this updated but partial knowledge to get the best possible view of the condition of the whole asset base. This paper considers approaches to doing this in the situation where the asset conditions are ordinal, meaning that the condition measurements fall into discrete ordered categories. The performances of several straightforward algorithms are analysed in terms of how good the predictions are. It is concluded that an ordinal logistic approach gives the best results, but a linear regression model gives acceptable results and has the advantage of being easier to implement.