Development of Pervious Concrete Pavement Performance Models Using Expert Opinions

Abstract

Pervious concrete pavement (PCP) is of significant importance in the field of stormwater management in terms of reducing runoff volume. Stormwater managers should initially ensure that PCP adequately performs over time to be able to implement it in a stormwater management system. Performance models are intended to predict the performance of an asset over its service life. To develop a performance model commonly long-term performance data are essential. No performance model has been developed for PCP to date because PCP long-term performance data are rarely available. In such a case, expert knowledge is an alternative method to collect data for developing a performance model. This research aims to develop performance models for PCP for the first time by using an integrated Markov chain technique (combination of homogenous and non-homogenous techniques) through incorporation of expert knowledge. Both deterministic and stochastic approaches are applied to build up Markov models by using expected values and the Latin hypercube simulation technique, respectively. Both approaches provide consistent results although the stochastic Markov model provides more detailed results. Short-term experimental field data are also incorporated to validate the Markov performance models.