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American Society for Testing and Materials

Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming.

The paper presents a rational mix design methodology to produce fly ash and ground granulated blast furnace slag (GGBS) based geopolymer concrete under ambient temperature curing condition. Efforts are made to develop the mix design by integrating ACI strength versus water to cement ratio curve of normal concrete, absolute volume method and combined grading concept. The proposed mix design methdology is not only user friendly, but offers also an option to choose between the desired compressive strength and specific alkaline activator content, AAC, to binder solids, BS, ratio or vice-versa. Several samples have been cast and are subjected to experimental investigations in order to produce concrete of required strength and properties. Strength attained by the geopolymer concrete for similar AAC to BS ratio that mimics water to cement ratio of normal concrete has found to be significantly high, in the range from 66 to 32 MPa for AAC to BS ratio from 0.4 to 0.8, respectively. Attempts are also made to propose a modified strength versus AAC to BS ratio curve based on the obtained test results. Finally, the mix design methodology has been depicted in the form of a flow chart, and its usefulness is illustrated with the help of an example.

Mix design development of fly ash and ground granulated blast furnace slag based geopolymer concrete

ANN prediction of cement mortar compressive strength, influence of cement strength class

THE STRENGTH OF CONCRETE MADE FROM SOUND, DURABLE MATERIALS DEPENDS UPON THE EXTENT TO WHICH THE SOLID PARTICLES ARE BONDED TOGETHER AND THE AMOUNT OF SOLID MATERIALS IN THE CONCRETE. THE BOND DEPENDS UPON THE PROPORTION OF CEMENT IN A GIVEN QUANTITY OF CONCRETE AND THE AMOUNT OF MIXING WATER. USING THESE BASIC PRINCIPLES SEVERAL METHODS FOR DESIGN ARE AVAILABLE. IN ANY CASE, THE MAINTENANCE OF A CONSTANT RATIO OF CEMENT TO MIXING WATER IS ESSENTIAL TO UNIFORMITY OF RESULTS. THE NEED FOR ACCURATE MEASUREMENT OF ALL MATERIALS IS EMPHASIZED AND ADEQUATE METHODS ARE DESCRIBED. AN EXAMPLE OF THE USE OF THE MORTAR-VOID METHOD OF DESIGNING TO MEET AN UNUSUAL CONDITION IS INCLUDED.

Design of concrete mixtures

Compressive strength prediction of Portland cement concrete with age using a new model

Self compacting concrete (SCC) is a highly flowable type of concrete that spreads into form without the need of mechanical vibration. This paper presents a comparative study between two methodologies which have been applied on two different data sets of SCC mixtures, which were gathered from the literature, using artificial neural network (ANN). The two methodologies aim to get the best prediction accuracy for the SCC ingredients using the 28-day compressive strength and slump flow diameters as inputs of the ANN. In the first methodology, the ANN model is constructed as a multi input – multi output neural network with the six ingredients as outputs. In the second methodology, the ANN model is constructed as a multi input – single output neural network where the six ingredient outputs are predicted separately from six different neural networks of multi input – single output type. Also, the influence of the mixes homogeneity on the prediction accuracy is investigated through the second data set. The results demonstrate the superiority of the second methodology in terms of accuracy of the predicted outputs. Moreover, the uniformity of the training data assures the accuracy of the predicted ingredients.

Metwally Abd Allah Abd Elaty

Papers

Compressive strength prediction of Portland cement concrete with age using a new model

Predicting the ingredients of self compacting concrete using artificial neural network

Performance of Portland cement mixes containing silica fume and mixed with lime-water

Flow properties of fresh concrete by using modified geotechnical Vane shear test

Optimization of Geopolymer Concrete by Principal Component Analysis