Thermal and mechanical characteristics of cement nanocomposites

Jalal, Mostafa; Mansouri, Esmaeel

doi:10.1515/nano.0044.00012

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“…Several studies have been conducted on cement replacement by various pozzolans and industrial by-products in different types of concrete. These supplementary cementitious materials include nanomaterials such as SiO 2 , 11,12 ZnO 2 , 13 TiO 2 , 14,15 fly ash, 16,17 silica fume, 18,19 and some other types of pozzolans that can help reduce the cement content, and hence lead to a more sustainable construction. Since concrete is a complicated composite structure, prediction of its behavior and properties is not easy, needing predictive models and more sophisticated tools than simple empirical relationships.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite

Jalal

2019

Proceedings of the Institution of Mechanical Engineers, Part L:

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Rubberized concrete containing waste tire rubber, silica fume, and zeolite cured in different curing conditions has been investigated in this paper. For this purpose, coarse aggregates were partially replaced by different percentages of waste rubber chips, namely 10% and 15%, and silica fume and zeolite were incorporated into the binder to replace 10% of cement mass. Different mixes were made and cured in two different conditions, namely in water and air with relative humidity of 100% and 50%, respectively. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. In order to simulate and predict the compressive strength of the rubberized cement composite, the influencing parameters were considered as cement content, silica fume, zeolite, rubber percentage, relative humidity, and age of the samples. Then, adaptive neuro-fuzzy inference system was employed to develop a prediction model for compressive strength of the concrete. Six variables were introduced into the adaptive neuro-fuzzy inference system model as inputs and the compressive strength was considered as the output. Prediction results and performance criteria were determined for various datasets including training, validation, testing, and all data. Parametric study of the adaptive neuro-fuzzy inference system models was also conducted to investigate the effect of each variable on the compressive strength of the rubberized concrete. Based on the correlations and errors obtained from the model, it was found that the proposed adaptive neuro-fuzzy inference system model can be a robust tool for predicting the behavior of complex composite materials such as rubberized concrete.

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