Size effect in self consolidating concrete beams with and without notches

The aim of this study is to obtain the fracture characteristics of low and medium compressive strength self consolidating concrete (SCC) for notched and un-notched plain concrete beams by using work of fracture G(F) and size effect model G(f) methods and comparing them with those of normal concrete and high performance concrete. The results show that; (i) with an increase in compressive strength, G(F) increases and G(f) decreases; (ii) with an increase in depth of beam, the decrease in nominal stress of notched beam is more when compared with that of a notchless beam.

Simplified Mixture Design for Production of Self-Consolidating Concrete

In this work, a methodology to achieve ordinary-, medium-, and high-strength self-consolidating concrete (SCC) with and without mineral additions is proposed. The inclusion of Class F fly ash increases the density of SCC but retards the hydration rate, resulting in substantial strength gain only after 28 days. This delayed strength gain due to the use of fly ash has been considered in the mixture design model. The accuracy of the proposed mixture design model is validated with the present test data and mixture and strength data obtained from diverse sources reported in the literature.

Filling Ability and Passing Ability of Self-Consolidating Concrete

This paper reviews statistical models obtained from a composite factorial design study, which was carried out to determine the influence of three key parameters of mixture composition on filling ability and passing ability of self-consolidating concrete (SCC). This study was a part of the European project “Testing SCC”- GRD2-2000-30024. The parameters considered in this study were the dosages of water and high-range water-reducing admixture (HRWRA), and the volume of coarse aggregates. The responses of the derived statistical models were slump flow, T50 , T60, V-funnel flow time, Orimet flow time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 minutes after adding the first water. The models are valid for mixtures made with 188 to 208 L/m3 (317 to 350 lb/yd3) of water, 3.8 to 5.8 kg/m3 (570 to 970 mL/100 kg of binder) of HRWRA, and 220 to 360 L/m3 (5.97 to 9.76 ft3/yd3) of coarse aggregates. The utility of such models to optimize concrete mixtures and to achieve a good balance between filling ability and passing ability is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mixture parameters on slump flow, T50 , T60 , V-funnel flow time, Orimet flow time, and blocking ratio. The paper also illustrates the various trade-offs between the mixture parameters on the derived responses that affected the filling and the passing ability.

Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach

Self-consolidating concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work conditions and also reduce the impact on the environment by elimination of the need for compaction. This investigation aimed at exploring the potential use of the neurofuzzy (NF) approach to model the fresh and hardened properties of SCC containing pulverised fuel ash (PFA) as based on experimental data investigated in this paper. Twenty six mixes were made with water-to-binder ratio ranging from 0.38 to 0.72, cement content ranging from 183 to 317 kg/m3 , dosage of PFA ranging from 29 to 261 kg/m3 , and percentage of superplasticizer, by mass of powder, ranging from 0 to 1%. Nine properties of SCC mixes modeled by NF were the slump flow, JRing combined to the Orimet, JRing combined to cone, V-funnel, L-box blocking ratio, segregation ratio, and the compressive strength at 7, 28, and 90 days. These properties characterized the filling ability, the passing ability, the segregation resistance of fresh SCC, and the compressive strength. NF model is constructed by training and testing data using the experimental results obtained in this study. The results of NF models were compared with experimental results and were found to be quite accurate. The proposed NF models offers useful modeling approach of the fresh and hardened properties of SCC containing PFA.

Transport Properties of Self-Consolidating Concrete

The permeability of concrete is influenced by the porosity and the interconnectivity of the pores in the cement paste and the microcracks in concrete, especially in the interface of paste-aggregate. The movements of gases, liquids, and ions through concrete is important because of their interactions with concrete constituents, including pore water, which can alter the integrity of concrete directly and indirectly, leading to the deterioration of structures. This study reports the findings from an investigation carried out to study the effect of the mixture variations on the durability of medium- and high-strength self-consolidating concrete (SCC). The mixture variations studied include the type of mineral admixtures, such as limestone powder (LSP) and pulverized fuel ash (PFA), and viscositymodifying admixtures (VMA) for both medium- and high-strength SCC. Air permeability, water permeability, capillary absorption, and chloride diffusivity were used to assess the durability of SCC mixtures in comparison with normal, vibrated concretes. The results showed that SCC mixtures, for medium- and high-strength grades using PFA followed by LSP, have lower permeability properties compared with normal concretes. SCC made with VMA had a higher sorptivity, air permeability, and water permeability compared with other SCC mixtures, which can be attributed to higher watercement ratio (w/c) and lack of pore filling effect. An in-place migration coefficient was obtained using the in-place ion migration test. This was used to compare the potential diffusivity of different concretes. The results indicated that SCC, for both grades of strength, made with PFA showed much lower diffusivity values in comparison with other mixtures, whereas the SCC mixtures with VMA showed higher diffusivity.