  Cement is the most used construction material. The performance of cement
hydrate depends on the constituent phases, viz. alite, belite, aluminate, and
ferrites present in the cement clinker, both qualitatively and quantitatively.
Traditionally, clinker phases are analyzed from optical images relying on a
domain expert and simple image processing techniques. However, the
non-uniformity of the images, variations in the geometry and size of the
phases, and variabilities in the experimental approaches and imaging methods
make it challenging to obtain the phases. Here, we present a machine learning
(ML) approach to detect clinker microstructure phases automatically. To this
extent, we create the first annotated dataset of cement clinker by segmenting
alite and belite particles. Further, we use supervised ML methods to train
models for identifying alite and belite regions. Specifically, we finetune the
image detection and segmentation model Detectron-2 on the cement microstructure
to develop a model for detecting the cement phases, namely, Cementron. We
demonstrate that Cementron, trained only on literature data, works remarkably
well on new images obtained from our experiments, demonstrating its
generalizability. We make Cementron available for public use.
