2020
DOI: 10.1016/j.ceramint.2020.05.074
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Mechanical properties and prediction of fracture parameters of geopolymer/alkali-activated mortar modified with PVA fiber and nano-SiO2

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Cited by 172 publications
(58 citation statements)
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“…It should be noted that the concrete containing SRA required a higher AE dosage to obtain the same air content than the one without SRA. Besides, the addition of PVA fiber influenced the workability of concrete, because the fibers would absorb a certain amount of water during the mixing process of concrete 27,39 . As a consequence, to achieve the target slump, the superplasticizer dosage of concrete containing PVA fiber was set to 0.9 wt% of binder, while those in other concretes were set to 0.7 wt% of binder.…”
Section: Raw Materialsmentioning
confidence: 99%
“…It should be noted that the concrete containing SRA required a higher AE dosage to obtain the same air content than the one without SRA. Besides, the addition of PVA fiber influenced the workability of concrete, because the fibers would absorb a certain amount of water during the mixing process of concrete 27,39 . As a consequence, to achieve the target slump, the superplasticizer dosage of concrete containing PVA fiber was set to 0.9 wt% of binder, while those in other concretes were set to 0.7 wt% of binder.…”
Section: Raw Materialsmentioning
confidence: 99%
“…Generally, the compressive strength and abrasion resistance of concrete increase with the silica fume content 6,7,9,13 . The profound improvements in these properties of concrete resulting from the silica fume addition are primarily due to the fact that the silica fume with a great surface area can participate in the pozzolanic reactions very rapidly 9,16,17 . However, the addition of silica fume would prone to induce an obvious volume shrinkage, thus increasing the early cracking risk of concrete, especially when the silica fume replacement for cement is larger than 8 % 6,9,15 .…”
Section: Introductionmentioning
confidence: 99%
“…Accordingly, in recent years, numerous mathematical predictions and optimisation models have been developed within the research community, including artificial neural networks (ANNs) [ 25 , 26 , 27 ], metaheuristic algorithms [ 28 , 29 ], genetic expression programming (GEP) [ 30 , 31 , 32 , 33 ], adaptive neuro-fuzzy inference systems (ANFIS) [ 34 , 35 , 36 ], and response surface methodology (RSM) [ 37 , 38 , 39 ]. Among them, RSM is one of the best statistical techniques used for data optimization [ 40 ].…”
Section: Introductionmentioning
confidence: 99%