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The standard petrography test method for measuring air voids in concrete(ASTM C457) requires a meticulous and long examination of sample phasecomposition under a stereomicroscope. The high expertise and specializedequipment discourage this test for routine concrete quality control. Though thetask can be alleviated with the aid of color-based image segmentation,additional surface color treatment is required. Recently, deep learningalgorithms using convolutional neural networks (CNN) have achievedunprecedented segmentation performance on image testing benchmarks. In thisstudy, we investigated the feasibility of using CNN to conduct concretesegmentation without the use of color treatment. The CNN demonstrated a strongpotential to process a wide range of concretes, including those not involved inmodel training. The experimental results showed that CNN outperforms thecolor-based segmentation by a considerable margin, and has comparable accuracyto human experts. Furthermore, the segmentation time is reduced to mereseconds.

Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis