2021
DOI: 10.1016/j.conbuildmat.2020.122114
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Effect of ZSM-5 waste dosage on the properties of autoclaved aerated concrete

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Cited by 28 publications
(11 citation statements)
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“…The SEM image illustrates the needle-like 1.1 nm tobermorite crystals in the reference sample ( Figure 5 a), plate-like 1.1 nm tobermorite crystals in sample with raw waste ( Figure 5 b), and lath-like in sample with calcined waste ( Figure 5 c). The literature review demonstrates that the addition of some industrial wastes can significantly change not only the properties of ACC but also the morphology of calcium silicate hydrates, especially 1.1 nm tobermorite [ 9 , 15 , 55 ]. In recent years, many studies investigated the influence of different types of industrial waste on the synthesis and morphology of 1.1 nm tobermorite [ 56 , 57 , 58 , 59 ].…”
Section: Resultsmentioning
confidence: 99%
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“…The SEM image illustrates the needle-like 1.1 nm tobermorite crystals in the reference sample ( Figure 5 a), plate-like 1.1 nm tobermorite crystals in sample with raw waste ( Figure 5 b), and lath-like in sample with calcined waste ( Figure 5 c). The literature review demonstrates that the addition of some industrial wastes can significantly change not only the properties of ACC but also the morphology of calcium silicate hydrates, especially 1.1 nm tobermorite [ 9 , 15 , 55 ]. In recent years, many studies investigated the influence of different types of industrial waste on the synthesis and morphology of 1.1 nm tobermorite [ 56 , 57 , 58 , 59 ].…”
Section: Resultsmentioning
confidence: 99%
“…Current trends in the construction and the production of building materials aim to reduce the consumption of energy and natural resources in all production processes. Although AAC production technology is characterized by relatively low consumption of raw materials and energy, research is being carried out to replace traditionally used raw materials with industrial wastes [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”
Section: Introductionmentioning
confidence: 99%
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“…Firstly, the input layer reads the pixel matrix of the input image and outputs to a convolutional layer with multiple feature surfaces. There are multiple neurons for each feature surface, and the input of each neuron node is the convolution block of the previous network, and using the convolution block in the convolution layer can extract the depth information of the image and obtain higher-dimensional feature information [17]. The convolutional layer is to perform a convolution operation on the input original image to obtain the underlying features contained in the image.…”
Section: Image Processing Technology Based On Cnnmentioning
confidence: 99%