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Surface reinforcement of recycled aggregates (RAs) by geopolymer and quantifying its morphological characteristics by machine learning

Zhengfa Chen, Jiahao Zhang,Shuang Cindy Cao,Yan Song, Zhaoyan Chen

JOURNAL OF BUILDING ENGINEERING(2024)

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Abstract
Recycled concrete aggregate (RCA) and recycled brick aggregate (RBA), derived from construction and demolition waste (C&DW), are increasingly used in concrete materials to conserve natural resources. In certain places, RBA, typically derived from C&DW, originally made with minerals that absorb water easily, dating back to the last century, exhibit higher water absorption and poorer mechanical properties compared to RCA. This study investigates the use of fly ashbased geopolymer paste (FGP) as a surface reinforcement coating for pre-treated RCA (PRCA) and pre-treated RBA (PRBA). Image processing and machine learning are employed to quantify the morphological characteristics of RAs. The results demonstrate improved morphology and altered distribution of morphological characteristic indexes for PRCA and PRBA. Physical and mechanical tests reveal the highest improvement in PRCA and PRBA occurs at a SiO2/Na2O molar ratio (Ms) of 1.4. Apparent density increases by 2.3 % and 2.6 %, and crushing value decreases by 21.6 % and 22.2 %, respectively. However, affected by the paste type, coating thickness, and geopolymerization reaction, the water absorption increases by 2.1 % and 5.8 %. XRD, TG-DTG, SEM, and LF-NMR tests indicate the formation of N(C)-A-S-H on RAs' surface by FGP, enhancing their microstructure.
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Key words
K-means clustering algorithm,C4.5 decision tree algorithm,Surface reinforcement,Morphological characteristics of RAs,LF-NMR
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