Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network
| Autor: | Saman Soleimani Kutanaei, Masoud Falahtabar shiade, Omid Lotfi Omran, Hamidreza Tavakoli |
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| Rok vydání: | 2014 |
| Předmět: |
Toughness
Materials science Aerospace Engineering Ocean Engineering mechanical properties chemistry.chemical_compound Nano-silica Flexural strength General Materials Science Fiber Composite material lcsh:QC120-168.85 Civil and Structural Engineering Cement Polypropylene business.industry Mechanical Engineering Fracture mechanics Structural engineering Volume (thermodynamics) chemistry Mechanics of Materials Self-compacting concrete Automotive Engineering Fracture (geology) lcsh:Descriptive and experimental mechanics lcsh:Mechanics of engineering. Applied mechanics lcsh:TA349-359 business artificial neural network |
| Zdroj: | Latin American Journal of Solids and Structures, Vol 11, Iss 6, Pp 966-979 Latin American Journal of Solids and Structures, Volume: 11, Issue: 6, Pages: 966-979, Published: NOV 2014 Latin American Journal of Solids and Structures v.11 n.6 2014 Latin American journal of solids and structures Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) instacron:ABCM |
| ISSN: | 1679-7825 |
| DOI: | 10.1590/s1679-78252014000600004 |
| Popis: | The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC. |
| Databáze: | OpenAIRE |
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