A new intelligence fuzzy-based hybrid metaheuristic algorithm for analyzing the application of tea waste in concrete as natural fiber

•Black tea production.•Fermentation is a crucial processing technique.•Black tea fermentation on the basis of convolutional neural network (CNN). Concrete is an ecologically friendly substance in which green concrete is a revolutionary subject in concrete industry records. The emphasis of this study...

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Vydáno v:Computers and electronics in agriculture Ročník 190; s. 106420
Hlavní autoři: Cao, Yan, Zandi, Yousef, Rahimi, Abouzar, Wu, Yujia, Fu, Leijie, Wang, Qiangfeng, Denić, Nebojša, Amine Khadimallah, Mohamed, Milič, Momir, Paunović, Marija
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.11.2021
Elsevier BV
Témata:
Adaptive systems
agriculture
Algorithms
Carbon dioxide
cement
compression strength
Compressive strength
Concrete
Concrete industry
electronics
Flexural strength
Fuzzy logic
Fuzzy-based hybrid
Heuristic methods
Hybrid systems
manufacturing
modulus of rupture
Natural Fiber
Pollution abatement
pollution control
Reinforced concrete
tea
Tea Waste
wastes
water requirement
Tea Waste
Concrete
Natural Fiber
Fuzzy-based hybrid
ISSN:0168-1699, 1872-7107
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Shrnutí:•Black tea production.•Fermentation is a crucial processing technique.•Black tea fermentation on the basis of convolutional neural network (CNN). Concrete is an ecologically friendly substance in which green concrete is a revolutionary subject in concrete industry records. The emphasis of this study was on the impact of Tea Waste (CA) as natural fiber onhardened concrete characteristics as a substitution for cement. Taking account of CO2 generation in cement manufacture, green concrete decreases emissions of CO2 into environmental production to an environmentally-friendly technique of pollution prevention. C1, C2, C3, C4 were then made of concrete of a class of 1, 2, 3, 4 kg reinforced in 1 m3 mix, then prepared and molded 4 distinct concrete types. After 28 days, the flexural and compressive strength tests have been carried out in a proper curing environment on the concrete. The findings were evaluated using the adaptive inference system neuro-fuzzy (ANFIS) to accurately predictthe concrete flexural and compressive strengthwith low error rates. Finally, the reinforced concrete with tea wastes raised the water requirement, but reduced the compressive and flexural strength compared to control mix, however, it was raised while using up to 5.4 kg waste in 1 m3 concrete. The result showed that up to 5 kg of tea wastes in 1 m3 of concrete could also be utilized as natural fibers. Also, ANFIS could show its outperformance in analyzing test results in predicting the compressive and flexural strength.
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ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2021.106420