Effects of elevated temperature on the tensile properties of NS-modified self-consolidating engineered cementitious composites and property optimization using response surface methodology (RSM)

作者: Bashar S. Mohammed , Bitrus Emmanuel Achara , Mohd Shahir Liew , W.S. Alaloul , Veerendrakumar C. Khed

DOI: 10.1016/J.CONBUILDMAT.2019.02.033

关键词: PozzolanCompressive strengthMicrostructureCementitiousComposite materialUltimate tensile strengthMaterials scienceScanning electron microscopeResponse surface methodologyPorosity

摘要: Abstract This paper discusses the influence of elevated temperature on tensile properties (tensile strength, strain, and energy absorption) microstructure nano-silica-modified self-consolidating engineered cementitious composites (NS-modified SC-ECC) mixtures. Response surface methodology (RSM) was utilized to develop a design matrix predictive models perform multi-objective optimization for mixtures prepared with polyvinyl alcohol (PVA) fiber at 0.5%, 1%, 1.5%, 2% volume fractions NS 0%, 2%, 3%, 4% weight materials temperatures 23 °C, 100 °C, 200 °C, 300 °C, 400 °C. To investigate certain characteristics NS-modified SC-ECC mixtures, residual compressive properties, were assessed up 400 °C 1 h. Microstructure examined using scanning electron microscopy before after had been exposed temperatures; accessible porosity pore size distribution cumulative measured mercury intrusion (MIP) validate behaviors strength The results show that improve due pozzolanic reactivity by utilizing calcium hydroxide (CH) released from cement hydration produce more calcium-silicate-hydrate (C-S-H) gel, which refines fiber-matrix aggregates-matrix interfacial transition zones (ITZ). is further facilitated curing C-S-H improving interactions 200 °C. PVA improves behavior prevents explosive spalling even incorporation 2%. responses considered in RSM absorption. established showed high degrees correlation between independent variables. Optimized could be achieved volume, 1.3% NS, 125.7 °C. absolute deviations (error) experimental data theoretical (predicted) models’ absorption 2.83%, 4.4%, 1.68%, respectively.

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