  The standard petrography test method for measuring air voids in concrete
(ASTM C457) requires a meticulous and long examination of sample phase
composition under a stereomicroscope. The high expertise and specialized
equipment discourage this test for routine concrete quality control. Though the
task can be alleviated with the aid of color-based image segmentation,
additional surface color treatment is required. Recently, deep learning
algorithms using convolutional neural networks (CNN) have achieved
unprecedented segmentation performance on image testing benchmarks. In this
study, we investigated the feasibility of using CNN to conduct concrete
segmentation without the use of color treatment. The CNN demonstrated a strong
potential to process a wide range of concretes, including those not involved in
model training. The experimental results showed that CNN outperforms the
color-based segmentation by a considerable margin, and has comparable accuracy
to human experts. Furthermore, the segmentation time is reduced to mere
seconds.
