2023
DOI: 10.1017/dce.2023.5
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Probabilistic selection and design of concrete using machine learning

Abstract: Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and … Show more

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Cited by 2 publications
(1 citation statement)
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“…Therefore, the PF content in PFRC specimens does not exceed 0.75 vol.% in this study. In engineering practice, the mix proportion of concrete is commonly expressed in mass per cubic meter (kg/m 3 ) [ 28 ]. Therefore, in this study, the PF mass dosages of PFRC specimens were 2.3 kg/m 3 (0.25 vol.%), 4.6 kg/m 3 (0.50 vol.%), and 6.9 kg/m 3 (0.75 vol.%), respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, the PF content in PFRC specimens does not exceed 0.75 vol.% in this study. In engineering practice, the mix proportion of concrete is commonly expressed in mass per cubic meter (kg/m 3 ) [ 28 ]. Therefore, in this study, the PF mass dosages of PFRC specimens were 2.3 kg/m 3 (0.25 vol.%), 4.6 kg/m 3 (0.50 vol.%), and 6.9 kg/m 3 (0.75 vol.%), respectively.…”
Section: Methodsmentioning
confidence: 99%