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Correlating properties of superplasticised paste, mortar and concrete
C. Jayasree and Ravindra Gettu
Characterising superplasticised paste is the first step in designing high performance concrete, as superplasticisers are responsible for workability, mechanical properties and durability. This paper analyses the flow behaviour of paste with four types of superplasticisers using Marsh cone test. For understanding the influence of superplasticisers, tests have been performed on mortars and concretes also. The study shows the saturation dosages of superplasticiser obtained from pastes and mortars are comparable, but a slightly higher dosage is required in concretes. The loss of workability in paste, mortar and concrete depends on the superplasticiser type. For satisfactory workability in concrete at 60 minutes, instead of arbitrarily dosing the superplasticiser, the saturation dosage corresponding to this time should be used. The study also shows that adding superplasticiser increases the compressive strength because of better compaction. The results prove that tests on pastes can be the basis for optimising flow behaviour, setting and strength in high performance concretes.
Estimating compressive strength
Prashant D. Ramugade
Although the many factors that affect concrete strength are well known, their collective effect on the strength is still not fully understood. This subject is therefore useful to expand the state-of-the-art-knowledge. In this paper, an attempt has been made to establish a simplified relationship among various factors that influence compressive strength. The paper presents ready-to-use formulae and networks to estimate 3 days, 7 days, and 28 days strengths of concrete cubes, immediate after casting or at an early age for different combinations of cementitious materials such as Ordinary Portland Cement (OPC), Blended Cement (Portland Pozzolana Cement [PPC] and Portland Slag Cement [PSC]), Fly Ash, Slag, and Micro Silica. The predictive tools such as Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) helped to develop the formulae and networks. Seven laboratories in Mumbai generated the data for analysing and validating the formulae and networks.
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