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The improvised construction techniques and utilization of industrial wastes in manufacturing concrete play a major role in sustainability. The artificially manufactured aggregates are gaining importance in the present era. The use of fibers as secondary reinforcement is greatly pronounced. Sintered fly ash aggregate concrete and normal aggregate concrete with and without basalt fiber with 28 days compressive strength of 30 Mpa were cast and tested. The stress–strain curve of the lightweight concrete has a lower modulus of elasticity when compared with the normal aggregate concrete. A simple linear relationship has been developed between the mechanical properties using regression analysis. The water absorption and void ratio had a direct relationship with the sorptivity and ponding of concrete. The strength and durability aspects of the lightweight aggregate concrete had better agreement with the requirements of the structural lightweight concrete. Strict adherence to codal provisions with respect to strength and durability can be made for improvised behavior.

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In this study, Artificial Neural Network (ANN) model is constructed to predict the compressive strength, split tensile strength and flexural strength of lightweight aggregate concrete made of sintered fly ash aggregate. An empirical relationship between the compressive strength, split tensile strength, and flexural strength was developed and compared with that of experimental results. The models were formulated based on results obtained from laboratory experiments. The variables considered in the study are the quantity of cement and water-cement ratio. Feed forward neural network and Levenberg-Marquardt back propagation algorithm were used for training algorithm in ANN. Amongst the total data, approximately 70% of the data was considered for training, 15% for testing and the remaining 15% has been considered for validation. The developed models had more accuracy with minimum error and had a higher correlation with the correlation coefficients of 0.916 and 0.955 were obtained for the training and testing data of compressive strength prediction, 0.949 and 0.937 respectively for split tensile strength prediction, 0.926 and 0.928 respectively for prediction of flexural strength. The models were compared with the experimental data's, and the results were discussed.