Probability Based Pavement Asset Management

Abstract

Deterministic Pavement Deterioration models are commonly used due to their relative simplicity, ease of use, and familiarity. The modelling techniques include straight-line extrapolation, S-shaped curves, polynomial constrained least squares, and logistic growth models. Some of the disadvantages of deterministic models include the facts that: (i) models do not take into account the uncertainties in pavement behaviour under variable traffic load, (ii) developing models requires an accurate and comprehensive dataset and (iii) ideally, all variables that affect pavement deterioration should be included in the models.

Traditionally, pavement deterioration models predict either an absolute condition value for a given pavement age, or the incremental change of the condition from one year to another. Modelling uncertainty requires the use of probabilistic techniques. Among probabilistic models, the Markov model is the most popular approach to modelling pavement performance as Markov probabilities can be derived from as little as two years of pavement condition data collection. A critical component of the Markov model is the transition probability matrix (TPM). A TPM represents the probability that a segment will stay in a specific condition for a specific year. Generally, the TPM is calculated based on the historical pavement condition data.

In the development of Key Performance Indicators (KPI) for Transport Infrastructure Ireland (TII), a number of pavement condition classes have been defined for each of the five TII subnetworks using the qualitative descriptors of Very Good, Good, Fair, Poor and Very Poor. Currently these condition classes are defined separately for the key pavement performance parameters of IRI, rut depth and LPV3. Estimations obtained using Markov transition probabilities are used to evaluate, probabilistically, the relative condition of a network as a function of time. The process makes possible the evaluation of the implication of alternative maintenance scenarios on network condition and to optimise budget spend as a function of time to maximise the condition of the network or to maintain the condition of the network above a prescribed limit, i.e. no more that 25% in Fair condition by Year X. Furthermore, alternative thresholds for condition states are analysed. TPMs are presented in the context of either homogenous and/or inhomogeneous chains, i.e. TPMﾒs varying as a function of the considered time step. The results of the work provides TII with an extremely powerful asset management tool for optimal lifecycle management of its >5000km pavement network.