Abstract:
Flushing is a defect which has a damaging effect on the functional performance of chipseal pavements. This study aimed to develop techniques to effectively identify and assess flushed pavements. Samples from flushed chipseal pavements were subjected to wheeltracking, and specimens from wheeltracked samples were analysed using image analysis techniques to calculate the volume of air voids. Data analysis was performed on pavement condition data to develop a model to predict the initiation and progression of flushing. A direct relationship was found between air void volume reduction and flushing. The data analysis revealed that the factors that contributed most to flushing were surface thickness, surface age, rut depth and grade of aggregates. The developed flushing initiation model had an accuracy of 74% and the flushing progression model was robust at predicting the quantity of flushing. The study resulted in identifying effective methods for assessing flushing on chipseal pavements.
