Abstract

Mix segregation is generally described as localized non-uniform zones of mix that do not conform to the original Job Mix Formula in gradation or asphalt content. Segregation may cause field performance issue such as rutting but no good trend between segregation and field/laboratory rut depth is observed. In this paper, a field road with segregation was monitored and those reported parameters used to detect segregation were collected. The segregated areas and the control non-segregated areas were identified by visual assessment. Rut depth were measured within one year after the road was opened to traffic. Rut depth from segregated areas and non-segregated areas are compared to evaluate the effect of segregation on rutting performance. The possibility of using different parameters such as pavement texture and construction temperature to detect segregation is also analyzed. Acknowledging that the rut depth may be affected by multiple parameters, a statistical Partial Least Squares (PLS) regression method is applied to select predictor variables that have the most significant effect on rut depth, and to develop rut depth prediction model. Results indicate that rut depth from segregated areas (visual) are statistically higher than rut depth from the non-segregated areas (visual). It is also found that pavement texture and construction temperature are the potential factors to detect segregation since they are correlated well with both visual assessment and rut depth. The developed rut depth prediction model shows good predictability and is well validated by considering segregation (mean texture depth), climatic effect (aging days), material property (HMA layer moduli), and pavement structure (entire HMA thickness).

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