SciELO - Scientific Electronic Library Online

vol.11 número11Stress intensity factors for an inclined and/or eccentric crack in a finite orthotropic laminaDynamic responses and damages of water-filled cylindrical shell subjected to explosion impact laterally índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados




Links relacionados


Latin American Journal of Solids and Structures

versão On-line ISSN 1679-7825


TAVAKOLI, Hamid Reza; OMRAN, Omid Lotfi; SHIADE, Masoud Falahtabar  e  KUTANAEI, Saman Soleimani. Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network. Lat. Am. j. solids struct. [online]. 2014, vol.11, n.11, pp.1906-1923. ISSN 1679-7825.

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. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perception (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength.

Palavras-chave : Fiber; Self-compacting concrete; Nano-silica; mechanical properties; artificial neural network.

        · texto em Inglês     · Inglês ( pdf )


Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons