Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network

Autor: Hamid Reza Tavakoli, Omid Lotfi Omran, Saman Soleimani Kutanaei, Masoud Falahtabar shiade

Publikováno v: Latin American Journal of Solids and Structures, Vol 11, Iss 6, Pp 966-979

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, fle

Externí odkaz: https://doaj.org/article/9ec6bd0b91e7412096b666583bad2284

Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network

Autor: Hamidreza Tavakoli, Saman Soleimani Kutanaei, Omid Lotfi Omran, Masoud Falahtabar shiade

Publikováno v: Latin American Journal of Solids and Structures, Vol 11, Iss 11, Pp 1906-1923
Latin American Journal of Solids and Structures, Volume: 11, Issue: 11, Pages: 1906-1923, Published: 2014
Latin American Journal of Solids and Structures v.11 n.11 2014
Latin American journal of solids and structures
Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
instacron:ABCM

In this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::262f246ba41cf97d18a484fed1003be1
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014001100002&lng=en&tlng=en