Abstract
Many numerical computation methods have been devised and put to use in a variety of science and technology disciplines in the twenty-first century. Artificial neural networks (ANN) have recently gained popularity as a method for forecasting the compressive strength of concrete. This study is based on the prediction of the compressive strength of geopolymer concrete (a combination of Flyash and ground granulated blast furnace slag). The study used experimental data on compressive strength at 28 days. The ANN models were trained using a set of eight input parameters (say Flyash, GGBS, coarse aggregate, fine aggregate, sodium hydroxide solution, sodium silicate, super-plasticizer and extra water) and one output parameter (say compressive strength). The model was trained using two different algorithms Levenberg–Marquardt (trainlm) and Scaled Conjugate Gradient (trainscg). The training parameters for all the models were kept constant, either the model is trained using trainlm or trainscg. TANSIG and PURELIN are two transfer functions majorly used in model training. The comparison was done among the models trained using three different algorithms. The comparison was done based on the R-value (correlation coefficient), SSE, R2, and RSME. The research concluded that N7LM, the feed-forward backpropagation network trained using trainlm as the training function, TANSING (transfer function in hidden layer) and PURELIN (transfer function in the output layer) showed the best result. Whereas N2SCG gave the best result for which the training function is trainscg, the transfer function for both the hidden and output layer is TANSIG.
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US drafts the manuscript. NG specifies the technical works and MV provides the finishing touch in the manuscript. All authors reviewed the manuscript before submission.
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Sharma, U., Gupta, N. & Verma, M. Prediction of the compressive strength of Flyash and GGBS incorporated geopolymer concrete using artificial neural network. Asian J Civ Eng 24, 2837–2850 (2023). https://doi.org/10.1007/s42107-023-00678-2
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DOI: https://doi.org/10.1007/s42107-023-00678-2