Chloride transport and thermoactivated modification of sustainable cement-based materials with high-content waste concrete powder

Hu, Ruihan; Shen, Jiaxin; Ma, Zhiming

doi:10.1016/j.jmrt.2023.02.048

Cited by 9 publications

(6 citation statements)

References 49 publications

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“…The average relative error precision of compressive strength and flexural tensile strength of RCP concrete are both level 2, and the precision of the a posteriori difference ratio are all level 1. As a result, it is verified that the GM (1,4) prediction model has high accuracy, and the model can be applied to the prediction of mechanical properties of RCP concrete and related experiments for reference.…”

Section: Gm (1 N) Modelmentioning

confidence: 80%

“…Since the beginning of the 21st century, there has been rapid global urbanization, resulting in significant amounts of construction and demolition (C&D) waste [1][2][3]. For example, the United States and the European Union generate approximately 700 million tons and 850 million tons of C&D waste annually, respectively [4].…”

Section: Introductionmentioning

confidence: 99%

“…They further investigated the effect of pore structure on the compressive strength of SCC through the theory of gray correlation entropy. In addition, a GM (1,3) prediction model for concrete compressive strength was established. The above studies contribute to our understanding of the relationship between pore structure and concrete strength.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, low-field NMR analysis is used to investigate the pore structure characteristics of the concrete. A GM (1,4) gray prediction model is then established to correlate the mechanical properties of the concrete with the pore structure parameters. The primary objective of this study is to increase the resource utilization rate of RCP while reducing the environmental pollution associated with traditional cement production.…”

Section: Introductionmentioning

confidence: 99%

“…The posterior error ratios are 0.189 and 0.255, respectively, and the accuracy is one level. The established predictive GM (1,4) model can accurately predict the mechanical properties of RCP concrete.…”

mentioning

confidence: 99%

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Gray Model Study of Strength and Pore Structure of Recycled Concrete Powder (RCP) Concrete Based on Low-Field NMR Technology

Hou,

Yu,

Zhang

et al. 2023

Materials

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In order to improve the resource utilization of recycled concrete powder (RCP), this study aimed to investigate the effect of RCP admixture, curing age, and alkali excitation on the strength of RCP concrete. In addition, the pore structure characteristics of RCP concrete were analyzed in combination with low-field NMR. Furthermore, a gray predictive GM (1, 4) model was established to predict the mechanical properties of the concrete based on the pore structure parameters, especially the compressive and flexural tensile strengths. The results of the study indicate that the mechanical properties, namely compressive strength and flexural strength, of RCP concrete exhibit an initial increase followed by a subsequent decrease with increasing RCP content at 3 d, 7 d, and 28 d curing ages. In particular, the concrete exhibits the highest mechanical properties when the RCP content reaches 10%. As the curing age increases, the RCP gradually achieves full hydration, resulting in further refinement of the concrete pores and a denser structure, which subsequently improves the mechanical properties. In addition, the strength growth rate of alkali-excited recycled concrete (ARC) showed a continuous increase, indicating that alkali excitation increasingly improved the mechanical properties of the concrete. Furthermore, the study accurately predicted the mechanical properties of RCP concrete by using GM (1, 4) prediction models for its compressive strength and flexural tensile strength using pore characteristic parameters.

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Section: Gm (1 N) Modelmentioning

confidence: 80%