  Cracking is a common failure mode in asphalt concrete (AC) pavements. Many
tests have been developed to characterize the fracture behavior of AC. Accurate
crack detection during testing is crucial to describe AC fracture behavior.
This paper proposed a framework to detect surface cracks in AC specimens using
two-dimensional digital image correlation (DIC). Two significant drawbacks in
previous research in this field were addressed. First, a multi-seed incremental
reliability-guided DIC was proposed to solve the decorrelation issue due to
large deformation and discontinuities. The method was validated using synthetic
deformed images. A correctly implemented analysis could accurately measure
strains up to 450\%, even with significant discontinuities (cracks) present in
the deformed image. Second, a robust method was developed to detect cracks
based on displacement fields. The proposed method uses critical crack tip
opening displacement ($\delta_c$) to define the onset of cleavage fracture. The
proposed method relies on well-developed fracture mechanics theory. The
proposed threshold $\delta_c$ has a physical meaning and can be easily
determined from DIC measurement. The method was validated using an extended
finite element model. The framework was implemented to measure the crack
propagation rate while conducting the Illinois-flexibility index test on two AC
mixes. The calculated rates could distinguish mixes based on their cracking
potential. The proposed framework could be applied to characterize AC cracking
phenomenon, evaluate its fracture properties, assess asphalt mixture testing
protocols, and develop theoretical models.
