Approaches to training multiclass semantic image segmentation of damage in concrete

This paper addresses the problem of creating a large quantity of high-quality training segmentation masks from scanning electron microscopy (SEM) images. The images are acquired from concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica reaction (ASR), a leading cause of deterioration, cracking and loss of capacity in much of the nation's infrastructure. The target damage classes in concrete SEM images are defined as paste damage, aggregate damage, air voids and no damage. We approached the SEM segmentation problem by applying convolutional neural network (CNN)-based methods to predict the damage classes due to ASR for each image pixel. The challenges in using the CNN-based methods lie in preparing large numbers of high-quality training labelled images while having limited human resources. To address these challenges, we designed damage- and context-assisted approaches to lower the requirements on human resources. We then evaluated the accuracy of CNN-based segmentation methods using the datasets prepared with these two approaches. LAY DESCRIPTION: This work is about automated segmentation of Scanning Electron Microscopy (SEM) images taken from core and prism samples of concrete. The segmentation must detect several damage classes in each image in order to understand properties of concrete-made structures over time. The segmentation problem is approached with an artificial network (AI) based model. The training data for the AI model are created using damage- and context-assisted approaches to lower the requirements on human resources. The access to all training data and to a web-based validation system for scoring segmented images is available at https://isg.nist.gov/deepzoomweb/data/concreteScoring.