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- W3132150370 abstract "Abstract Carbonaceous (e.g., limestone) and aluminosilicate (e.g., calcined clay) mineral additives are routinely used to partially replace ordinary portland cement in concrete to alleviate its energy impact and carbon footprint. These mineral additives—depending on their physicochemical characteristics—alter the hydration behavior of cement; which, in turn, affects the evolution of microstructure of concrete, as well as the development of its properties (e.g., compressive strength). Numerical, reaction-kinetics models—e.g., phase boundary nucleation-and-growth models; which are based partly on theoretically-derived kinetic mechanisms, and partly on assumptions—are unable to produce a priori prediction of hydration kinetics of cement; especially in multicomponent systems, wherein chemical interactions among cement, water, and mineral additives occur concurrently. This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs. Based on a database comprising hydration kinetics profiles of 235 unique systems—encompassing 7 synthetic cements and three mineral additives with disparate physicochemical attributes—a random forests (RF) model was rigorously trained to establish the underlying composition-reactivity correlations. This training was subsequently leveraged by the RF model: to predict time-dependent hydration kinetics of cement in new, multicomponent systems; and to formulate optimal mixture designs that satisfy user-imposed kinetics criteria." @default.
- W3132150370 created "2021-03-01" @default.
- W3132150370 creator A5000214465 @default.
- W3132150370 creator A5015580253 @default.
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- W3132150370 creator A5044493714 @default.
- W3132150370 creator A5070490597 @default.
- W3132150370 creator A5071918939 @default.
- W3132150370 date "2021-02-16" @default.
- W3132150370 modified "2023-10-18" @default.
- W3132150370 title "Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems" @default.
- W3132150370 cites W1534872837 @default.
- W3132150370 cites W1681758061 @default.
- W3132150370 cites W1688175730 @default.
- W3132150370 cites W1901616594 @default.
- W3132150370 cites W1971128104 @default.
- W3132150370 cites W1973365666 @default.
- W3132150370 cites W1974144787 @default.
- W3132150370 cites W1984730635 @default.
- W3132150370 cites W1987338684 @default.
- W3132150370 cites W1988195734 @default.
- W3132150370 cites W1991653918 @default.
- W3132150370 cites W1998523455 @default.
- W3132150370 cites W1999517659 @default.
- W3132150370 cites W2011192732 @default.
- W3132150370 cites W2011805013 @default.
- W3132150370 cites W2024678150 @default.
- W3132150370 cites W2027903984 @default.
- W3132150370 cites W2031553980 @default.
- W3132150370 cites W2035431830 @default.
- W3132150370 cites W2038808304 @default.
- W3132150370 cites W2041334294 @default.
- W3132150370 cites W2046590022 @default.
- W3132150370 cites W2048271631 @default.
- W3132150370 cites W2063588091 @default.
- W3132150370 cites W2069184982 @default.
- W3132150370 cites W2072342051 @default.
- W3132150370 cites W2074457476 @default.
- W3132150370 cites W2075187297 @default.
- W3132150370 cites W2078462763 @default.
- W3132150370 cites W2082425494 @default.
- W3132150370 cites W2082755308 @default.
- W3132150370 cites W2087041772 @default.
- W3132150370 cites W2087952374 @default.
- W3132150370 cites W2091374137 @default.
- W3132150370 cites W2091745132 @default.
- W3132150370 cites W2092524801 @default.
- W3132150370 cites W2092582124 @default.
- W3132150370 cites W2095328880 @default.
- W3132150370 cites W2116593735 @default.
- W3132150370 cites W2128969840 @default.
- W3132150370 cites W2140430652 @default.
- W3132150370 cites W2166765391 @default.
- W3132150370 cites W2195802272 @default.
- W3132150370 cites W2235026764 @default.
- W3132150370 cites W2328618981 @default.
- W3132150370 cites W2399528691 @default.
- W3132150370 cites W2400427052 @default.
- W3132150370 cites W2557155890 @default.
- W3132150370 cites W2601609165 @default.
- W3132150370 cites W2604224271 @default.
- W3132150370 cites W2605413632 @default.
- W3132150370 cites W2605864128 @default.
- W3132150370 cites W2734691712 @default.
- W3132150370 cites W2742077718 @default.
- W3132150370 cites W2745890884 @default.
- W3132150370 cites W2769719797 @default.
- W3132150370 cites W2775687667 @default.
- W3132150370 cites W2776584838 @default.
- W3132150370 cites W2788500979 @default.
- W3132150370 cites W2793861981 @default.
- W3132150370 cites W2802274879 @default.
- W3132150370 cites W2884430236 @default.
- W3132150370 cites W2893769615 @default.
- W3132150370 cites W2896458282 @default.
- W3132150370 cites W2908447666 @default.
- W3132150370 cites W2911964244 @default.
- W3132150370 cites W2922914159 @default.
- W3132150370 cites W2924820973 @default.
- W3132150370 cites W2933418103 @default.
- W3132150370 cites W2967762565 @default.
- W3132150370 cites W2968267211 @default.
- W3132150370 cites W2969642948 @default.
- W3132150370 cites W2970507526 @default.
- W3132150370 cites W2990313622 @default.
- W3132150370 cites W3002388359 @default.
- W3132150370 cites W3003541441 @default.
- W3132150370 cites W3003854791 @default.
- W3132150370 cites W3007109002 @default.
- W3132150370 cites W3008008902 @default.
- W3132150370 cites W3013827589 @default.
- W3132150370 cites W3023952732 @default.
- W3132150370 cites W3035639675 @default.
- W3132150370 cites W3038415525 @default.
- W3132150370 cites W3097683692 @default.
- W3132150370 cites W3120762161 @default.
- W3132150370 cites W4212883601 @default.
- W3132150370 cites W4362196192 @default.
- W3132150370 cites W4376635080 @default.