Abstract

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's outcomes—specifically the variable importance for the AAMs' 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's outcomes demonstrate a roadmap—incorporates composite chemo structural descriptors in ML models—that can be employed to design AAMs to achieve targeted compressive strength.

Department(s)

Electrical and Computer Engineering

Second Department

Civil, Architectural and Environmental Engineering

Third Department

Materials Science and Engineering

Comments

National Science Foundation, Grant 1661609

Keywords and Phrases

alkali-activated mortar; compressive strength; constraint theory; network ratio; random forests

International Standard Serial Number (ISSN)

1551-2916; 0002-7820

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2023 Wiley, All rights reserved.

Publication Date

01 Jun 2022

Download

Full Text Link

Share

 
COinS