Architecture Commons^™

Machine Learning Enabled Closed-Form Models To Predict Strength Of Alkali-Activated Systems, Taihao Han, Eslam Gomaa, Ahmed Gheni, Jie Huang, Mohamed Elgawady, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

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 …