Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite

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Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two ess...

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Bibliographic Details
Main Authors:	Sun, Junbo, Wang, Y., Yao, X., Ren, Z., Zhang, G., Zhang, C., Chen, X., Ma, W., Wang, Xiangyu
Format:	Journal Article
Language:	English
Published:	MDPI 2021
Subjects:	Science & Technology Physical Sciences Technology Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics random forest beetle antennae search activation methodology machine learning flexural strength COMPRESSIVE STRENGTH CONCRETE KNOWLEDGE FRAMEWORK BEHAVIOR SEARCH SILICA SAND
Online Access:	http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90922

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