Asset Optimiser implements a revolutionary multi-variate inductive algorithm based on statistical learning theory and supervised learning techniques to predict future condition states of asset elements and accurately represent the stochastic nature of deterioration process. Unlike other approaches, such as Markov chain, survival analysis or regression models, which assume that the deterioration process is known distribution function, our inductive algorithm does not assume any prior knowledge of the deterioration function and can efficiently account for the impact of a wide range of parameters.

Our deterioration modeling algorithm analyses historical inspection data and correlates a range of independent physical and operational variables such as age, traffic volume, design load, deck type, etc with condition and capacity degradation of asset elements to automatically infer or learn the distribution function that most closely captures the relationship between these variables and the condition state of the asset elements.