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