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An optimized strategy for the enhancement of microbially induced calcium precipitation including spore viability ensurance, nutrient selection and O 2 supply was developed. Firstly, an optimal yeast extract concentration of 5 g/L in sporulation medium was determined based on viable spore yield and spore viability. Furthermore, the effects of certain influential factors on microbial calcium precipitation process of H4 in the presence of oxygen releasing tablet (ORT) were evaluated. The results showed that CaO 2 is preferable to other peroxides in improving the calcium precipitation by H4. H4 strain is able to precipitate a highly insoluble calcium at the CaO 2 dosage range of 7.5-12.5 g/L, and the most suitable spore concentration is 6 × 10 8 spores/ml when the spore viability (viable spore ratio) is approximately 50%. Lactate is the best carbon source and nitrate is the best nitrogen source for aerobic incubation. This work has laid a foundation of ternary self-healing system containing bacteria, ORT, and nutrients, which will be promising for the self-healing of cracks deep inside the concrete structure.

Particle size and morphological/shape properties ensure the reliable and sustainable use of all aggregate skeleton materials placed as constructed layers in transportation applications. The composition and packing of these aggregate assemblies rely heavily on particle size and morphological properties, which affect layer strength, modulus, and deformation response under vehicular loading and therefore facilitate the quality assurance/quality control (QA/QC) process. Aggregate imaging systems developed to date for size and shape characterization, however, have primarily focused on measurement of separated or slightly contacting aggregate particles. Development of efficient computer vision algorithms is urgently needed for image-based evaluations of densely stacked (or stockpile) aggregates, which requires image segmentation of a stockpile for the size and morphological properties of individual particles. This paper presents an innovative approach for automated segmentation and morphological analyses of stockpile aggregate images based on deep learning techniques. A task-specific stockpile aggregate image dataset is established from images collected from various quarries in Illinois. Individual particles from the stockpile images are manually labeled on each image associated with particle locations and regions. A state-of-the-art object detection and segmentation framework called Mask R-CNN is then used to train the image segmentation kernel, which enables user-independent segmentation of stockpile aggregate images. The segmentation results show good agreement with ground-truth labeling and improve the efficiency of size and morphological analyses conducted on densely stacked and overlapping particle images. Based on the presented approach, stockpile aggregate image analysis promises to become an efficient and innovative application for field-scale and in-place evaluations of aggregate materials.