Data driven design of alkali-activated concrete using sequential learning
نویسندگان
چکیده
This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with total of 1367 formulations different types alkali-activated materials, including fly ash and blast furnace slag-based concrete their respective compressive strength CO2-footprint, were compiled from the literature to develop evaluate this approach. Utilizing data, comprehensive computational study was undertaken efficacy proposed material design methodologies, simulating laboratory conditions reflective real-world scenarios. The results indicate significant reduction in development time lower research costs enabled predictions machine learning. work challenges common practices data-driven materials. Our show, training data required may be much less than commonly suggested. Further, it is more important establish practical framework choose accurate models. can immediately implemented into applications translated advances development.
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ژورنال
عنوان ژورنال: Journal of Cleaner Production
سال: 2023
ISSN: ['0959-6526', '1879-1786']
DOI: https://doi.org/10.1016/j.jclepro.2023.138221