Machine Learning to Predict Properties of Fresh and Hardened Alkali-Activated Concrete
Alkali-activated concrete (AAC) is widely considered to be a sustainable alternative to Portland cement concrete. However, on account of extensive heterogeneity in composition of the aluminosilicates, coupled with the failure of classical materials science approaches to unravel the underlying composition-property linkages, reliable prediction of AAC's properties has remained infeasible. This paper presents a random forest (RF) model to predict two properties of fly ash-based AACs that are important from compliance standpoint – slump flow; and compressive strength – in relation to physiochemical attributes, curing conditions, and mixing procedures of the concretes. Results show that the RF model – once meticulously trained, and after its hyperparameters are rigorously optimized – is able to produce high fidelity predictions of both properties of new AACs. The model is also used to quantitatively assess the influence of physiochemical attributes and process parameters on the AAC's properties. Outcomes of this work present a pathway to optimization of AACs' properties.
E. Gomaa et al., "Machine Learning to Predict Properties of Fresh and Hardened Alkali-Activated Concrete," Cement and Concrete Composites, vol. 115, Elsevier, Nov 2020.
The definitive version is available at https://doi.org/10.1016/j.cemconcomp.2020.103863
Civil, Architectural and Environmental Engineering
Materials Science and Engineering
Electrical and Computer Engineering
Keywords and Phrases
Alkali-activated concrete; Compressive strength; Machine learning; Random forest; Slump flow
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier, All rights reserved.
04 Nov 2020