Machine Learning Enabled Closed-Form Models to Predict Strength of Alkali-Activated Systems

Alkali-activated mortar (AAM) is an emerging eco-friendly construction material, which can complement ordinary Portland cement (OPC) mortars. Prediction of properties of AAMs -- albeit much needed to complement experiments -- is difficult, owing to substantive batch-to-batch variations in physicochemical properties of their precursors (i.e., aluminosilicate and activator solution). In this study, a machine learning (ML) model is employed; and it is shown that the model -- once trained and optimized -- can reliably predict compressive strength of AAMs solely from their initial physicochemical attributes. Prediction performance of the model improves when multiple compositional descriptors of the aluminosilicate are combined into a singular, composite chemostructural descriptor (i.e., network ratio and number of constraints); thus, reducing the degrees of freedom. Through interpretation of the ML model\u27s outcomes -- specifically the variable importance for the AAMs\u27 compressive strength -- a simple, easy-to-use, closed-form analytical model is developed. Results demonstrate that the analytical model yields predictions of compressive strength of AAMs without scarifying much accuracy compared to the ML model. Overall, this study\u27s outcomes demonstrate a roadmap -- incorporates composite chemostructural descriptors in ML models -- that can be employed to design AAMs to achieve targeted compressive strength