Abstract
Concrete mix proportioning is one of the critical process and it involves a lot of precautionary measures to arrive at the right proportions of ingredients like cement, aggregate, water, and admixtures. Even though there are technical specifications that are managed mix proportionating, the procedure is not totally in the realm of science. Due to imprecise codal provisions, impreciseness, and fuzziness involved in the various stages of mix proportioning. This paper reviews the various data mining and machine learning techniques developed by the researchers for making concrete mix design for various codal provisions more realistic and scientific.
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