Machine learning to predict properties of fresh and hardened alkali-activated concrete
نویسندگان
چکیده
Alkali-activated concrete (AAC) is widely considered to be a sustainable alternative Portland cement concrete. However, on account of extensive heterogeneity in composition the aluminosilicates, coupled with failure classical materials science approaches unravel underlying composition-property linkages, reliable prediction AAC's properties has remained infeasible. This paper presents random forest (RF) model predict two fly ash-based AACs that are important from compliance standpoint – slump flow; and compressive strength relation physiochemical attributes, curing conditions, mixing procedures concretes. Results show RF once meticulously trained, after its hyperparameters rigorously optimized able produce high fidelity predictions both new AACs. The also used quantitatively assess influence attributes process parameters properties. Outcomes this work present pathway optimization AACs'
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ژورنال
عنوان ژورنال: Cement & Concrete Composites
سال: 2021
ISSN: ['0958-9465', '1873-393X']
DOI: https://doi.org/10.1016/j.cemconcomp.2020.103863