Abstract

Thick asphalt pavements with open-graded friction course (OGFC) are exposed to top-down cracking (TDC), a distress consisting of longitudinal cracks that initiate on the pavement surface close to the wheel path and propagate downwards. The main objective of this study is to develop a semi-empirical model for the prediction of TDC depth evolution for such pavements. For this purpose, a series of cores were taken from different Italian motorway pavements affected by TDC and analyzed in the laboratory. Cracked cores taken from the wheel path area were analyzed to determine TDC depth, whereas intact cores taken from the middle of the lane (not affected by traffic loadings) were tested to obtain the volumetric and mechanical properties of the OGFC mixture. The proposed model, developed on the basis of the results already available in literature and on the findings of the laboratory investigation, predicts the evolution of TDC depth as a function of the applied traffic loadings (in terms of 12-ton fatigue equivalent single axle loads, i.e., ESALs). The model is sigmoidal with a maximum TDC depth assumed equal to 150 mm. The shape parameter of the sigmoidal function depends on the indirect tensile strength (ITS) of the OGFC mixtures (which takes into account indirectly also the volumetrics and stiffness of the OGFC), whereas the evolutive translation factor depends on the age of the OGFC mixture. After excluding some outliers, the model was able to predict the measured TDC depth very well. Moreover, in-situ observations allowed a preliminary validation of the proposed model. This model can be used in pavement management systems (PMSs) to plan surface repairs due to TDC in a timely manner, thus minimizing pavement damage and maintenance costs. • The semi-empirical model predicts TDC depth as a function of the traffic loads. • The model is sigmoidal with a maximum TDC depth assumed equal to 150 mm. • The model parameters depend on the properties and the age of the OGFC. • In-situ observations provide a preliminary validation of the model. • The model can be used in a PMS to identify pavement areas where TDC is critical.

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