The timely identification of undesirable cracks, raveling and rutting conditions is a critical step in pavement management at network level. To date many models have been developed for forecasting pavement condition. The most popular of them in developing countries is the World Bank developed model HDM-4. This paper summarizes the implementation of a pavement condition prediction methodology using Artificial Neural Network (ANN) for three individual ANN models to forecast cracking, raveling and rutting in low volume roads (LVR) in India. Road inventory data as well as five cycles of pavement performance data (pre-monsoon, post-monsoon and during winter season) including various pavement distresses, subgrade characterization and traffic data have been collected from 61 in-service LVR pavement sections in 2004, 2005 and 2006 and each individual ANN model was tested using this data. The modelling results suggest that the ANN models developed in the study satisfactorily forecast future cracking, ravelling and rutting. The performance of the ANN models is also compared with calibrated HDM-4 models using LVR validation sections in the study area. (a) For the covering entry of this conference, please see ITRD abstract no. 0612AR242E.
Samenvatting