Abstract
Deterioration modelling is an important analytical component in risk-informed infrastructure asset management. Many asset managers find it very challenging because of its technicality, paucity of deterioration data and difficulty in model selection. Traditional approaches emphasised the mean deterioration trend and heeded too little the characterisation of uncertainty involved. This paper attempts to revert this trend and bring stochastic deterioration modelling back to focus. Following a systems approach, the author argues that deterioration modelling involves not only the data-driven process that asset managers have traditionally perceived, but also a system analysis that carries the empirical deterioration modelling at the level of performance data up to the level of the performance hierarchy at which decisions are made. In addition, deterioration modelling is an important and integral component of risk analysis, and therefore, the characterisation and quantification of aleatory uncertainty and epistemic uncertainty become an essential component of deterioration modelling. Moreover, deterioration data include not only hard data collected from inspection and condition assessment, but also soft data that can be gleaned from expert opinions, design manuals and professional judgements. Although mainly for water and waste water assets, the principles, guidelines and model selection flow chart are equally applicable to other infrastructure assets.
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