Ingår i: EPAM 2012: Malmö, Sweden, 5–7 September: 4th European pavement and asset management conference

The evolvement of pavement conditions is influenced by a variety of factors. The challenge when implementing a performance prediction model for a pavement structure is that data only exists for a small number of influencing factors. Moreover, only a small proportion of the data for influencing factors that does exist is accurate enough to be used in such a model. An even smaller proportion of influences for which a sufficient amount of accurate data is available can be described using state of the art parameters. For this reason, it is very difficult to approximate the evolvement of pavement conditions using models based on deterministic equations. In view of this fact, the evolvement of pavement conditions as a whole can (also) be viewed as a stochastic process. Here, performance can be described using probabilistic calculation models. The higher the number of road sections evaluated and the more homogenous these road sections are, the better the results will be. The basic principle behind this theory is that past evolvement determines future evolvement. In order to create a probabilistic model, the condition of the pavement has to be known at at least two points in time. Other information such as pavement structure or traffic volume is not required. The research on which this paper is based used condition assessment and evaluation data for about 4,000 km of German roads over a relatively short interval of about five years. The resulting condition change matrix shows the combined result of aging and maintenance measures taken by the responsible road administration. The matrix has to be modified, depending on the objective of investigation. Exponentiation of the matrix?where appropriate, the modified matrix?leads to a probability distribution function of the pavement condition. This function fully describes the pavement performance and can be used in pavement management systems in many different ways, e.g. to predict the service life of a pavement structure.