Analysing concrete microscopic images is difficult because of its highly heterogeneous composition and the different scales involved. This article presents an open‐source deep learning‐based algorithm dedicated to air‐void detection in concrete microscopic images. The model, whose strategy is presented alongside concrete compositions information, is built using the Mask R‐CNN model. Model performances are then discussed and compared to the manual air‐void enhancement technique. Finally, the selected open‐source strategy is exposed. Overall, the model shows a good precision (mAP = 0.6452), and the predicted air void percentage agrees with experimental measurements highlighting the model's potential to assess concrete durability in the future.

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Sustainability concerns related to the CO2 emissions of Portland cement production led to the exploitation of some by-products as replacement materials, such as slag. This requires a good understanding of the blended cementitious materials at the microscale to fully explain the observed behavior of the structures at the macroscale. The nanoindentation technique was used to assess the effect of slag incorporation on the micromechanical properties at the early age through the study of four cement pastes with different replacement ratios. Then, the nanoindents were observed using scanning electron microscopy to address the issues related to nanoindentation data deconvolution. The results show that the hydration products are intimately intermixed and that the boundary condition of indented areas must be considered when assessing the properties of individual phases to reduce the measurement variability. In addition, the incorporation of slag was found to cause a decrease in hydration products' elastic modulus and creep properties due to the gel porosity increase.

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