  Previous research has showcased that the characterization of surface cracks
is one of the key steps towards understanding the durability of strain
hardening cementitious composites (SHCCs). Under laboratory conditions, surface
crack statistics can be obtained from images of specimen surfaces through
manual inspection or image processing techniques. Since these techniques
require optimal lighting conditions, proper surface treatment, and prior
(manual) selection of the correct region for proper inference, they are
strenuous and time-consuming. Through this work, we explored and tailored deep
convolutional networks (DCCNs) for the rapid characterization of cracks in SHCC
from various kinds of photographs. The results from the controlled study
suggest that the inference ability of the tailored DCCN (TDCNN) is quite good,
resilient against epistemic uncertainty, and tunable for completely independent
but adverse observations. From the crack pattern computed using TDCCN, average
crack width (ACW) and crack density (CD) can be calculated to facilitate
durability design and conditional assessment in a practical environment.
