Method for quantifying the reaction degree of slag in alkali-activated cements using deep learning-based electron microscopy image analysis.

In this paper, we present a methodology for measuring the reaction degree of ground granulated blast furnace slag (GGBFS) in alkali-activated cements using neural network based image analysis. The new methodology consists of an image analysis routine in which the segmentation of the back scattered electron (BSE) (SEM) images is based on a deep learning U-net. This methodology was applied to and developed for NaOH-activated slag cements and validated against independently measured XRD results. In a next step the developed method was applied to NaOH-Na SO -activated systems, to check the broader applicability. The neural networks based image analysis results were shown to correlate well with the XRD results. Once the model was trained, it segmented images fast and accurately. Furthermore, the model trained on the NaOH-activated systems was readily applicable on NaOH-Na SO -activated system indicating that the model generalises well. As such, the developed methodology and models can be more performant and robust than conventional threshold-based image segmentation. The method's accuracy, replicability and transferability make it a promising tool for material analysis and characterisation.