Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model

In this research work, the levernberg Marquardt back propagation neural network was adequately trained to understand the relationship between the ۲۸th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Data used for the study were generated experimentally. A total of a hundred and fourteen (۱۱۴) training data set were presented to the network. Eighty (۸۰) of these were used for training the network, seventeen (۱۷) were used for validation, and another seventeen (۱۷) were used for testing the network's performance. Six (۶) data set were left out and later used to test the adequacy of the network predictions. The outcome of results of the created network was close to that of the experimental efforts. The lowest and highest correlation coefficient recorded for all data samples used for developing the network were ۰.۹۰۱ and ۰.۹۸۴ for the test and training samples respectively. These values were close to ۱. T-value obtained from the adequacy test carried out between experimental and model generated data was ۱.۴۳۷. This is less than ۲.۰۶۴, which is the T values from statistical table at ۹۵% confidence limit. These results proved that the network made reliable predictions. Maximum compressive strength achieved from experimental works was ۳۰.۸۳N/mm۲ at a water-cement ratio of ۰.۵۶۲ and a percentage replacement of ordinary portland cement with hydrated lime of ۱۸.۷۵%. Generally, for hydrated lime to be used in making structural concrete, ordinary portland cement percentage replacement with hydrated lime must not be up to ۳۰%. With the use of the developed artificial neural network model, mix design procedure for hydrated lime cement concrete can be carried out with lesser time and energy requirements, when compared to the traditional method. This is because, the need to prepare trial mixes that will be cured, and tested in the laboratory, will no longer be required.

Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model Keywords:

Hydrated Lime , Compressive Strength , Artificial Neural Network , Ordinary Portland Cement