Investigation of SCC characterizations incorporating supplementary cementitious materials

被引:6
|
作者
Alipour, Pedram [1 ]
Behforouz, Babak [2 ]
Mohseni, Ehsan [3 ]
Zehtab, Behnam [4 ]
机构
[1] Islamic Azad Univ, Dept Civil Engn, UAE Branch, Dubai, U Arab Emirates
[2] Islamic Azad Univ, Dehaghan Branch, Dept Civil Engn, Dehaghan, Iran
[3] Univ Newcastle, Sch Architecture & Built Environm, Newcastle, NSW, Australia
[4] Islamic Azad Univ, Dehaghan Branch, Young Researchers & Elite Club, Dehaghan, Iran
关键词
composite materials; material characterisation; nanoparticles; SELF-COMPACTING CONCRETE; HIGH-PERFORMANCE CONCRETE; RICE HUSK ASH; FLY-ASH; DURABILITY PROPERTIES; MECHANICAL-PROPERTIES; COMPRESSIVE STRENGTH; MINERAL ADMIXTURES; CONSOLIDATING CONCRETE; HARDENED PROPERTIES;
D O I
10.1680/jemmr.18.00024
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
An experimental study was performed to assess the effect of metakaolin (MK) as supplementary cementitious material (SCM) and limestone powder (LP) and nano-titanium dioxide (nano-TiO2; NT) as mineral admixtures on the pore structure, mechanical and durability of self-compacting concrete (SCC). The fresh-state performance was evaluated by slump flow, T-50, V-funnel and L-box tests, and hardened-state parameters were analyzed through compressive strength, water absorption, density and electrical resistivity. Furthermore, permeability was analyzed by means of the rapid chloride permeability test and mercury intrusion porosimetry. Using additives resulted in improvement of rheological properties and compressive strength. Additionally, the density and electrical resistivity of SCC mixes containing SCMs and NT were higher than those of the control SCC mixes. Low absorption and permeability values were obtained for the mixes containing 5% NT. The results herein reported suggest that replacement ratios of 15% MK, 10% LP and 5% NT can be considered as optimal from a cost-benefit point of view in terms of fresh- and hardened-state performance. Predictive models for the hardened properties analyzed have been obtained using artificial neural networks (ANNs). ANNs have been proven an appropriate alternative method for predicting the hardened properties of SCC mixes.
引用
收藏
页码:492 / 507
页数:16
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