• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.43-48
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• 2001

The use of fly ash in cement and concrete industries has many benefits including engineering, economic, and ecological aspects. However, it has a disadvantage of low strength development, especially at early ages. In this study, in order to overcome this problem, the early strength accelerating agent($NA_{2}$ $SO_{4}$) was selected and applied to the production of high strength concrete(HSC) containing fly ash. It was found that the compressive strength of fly ash concrete incorporating TEX>$NA_{2}$ $SO_{4}$ has greater than that of concrete containing fly ash only until 7 days after casting. From the microstructural point of view, ettringite increased and pores decreased in fly ash concrete incorporating TEX>$NA_{2}$ $SO_{4}$ , leading to the development of early age strength. It was also found that the velocity vs. strength relationship of HSC is considerably different from that of low-strength concrete(LSC). Therefore, in order to predict early age strength of HSC, a estimation equation different from that for LSC is needed.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.