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- W4285247165 abstract "The present paper describes the application of artificial neural networks (ANN) as one of the artificial intelligence (AI) approaches for predicting the compressive strength of fly ash-based geopolymer concrete. In the proposed model, amount of fly ash, alkaline solution (combination of sodium hydroxide and sodium silicate solution), coarse aggregate content, fine aggregate content, molarity, water content, superplasticizer, curing time, temperature and age was considered as the ten input parameters, while the compressive strength was the output parameter. Modelling has been carried out using MATLAB, and training and validation processes are carried out for the corresponding mix proportions. Model performance was observed by changing the number of neurons in the hidden layers. The performance of this model was evaluated using a set of three metrics, including correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE). With the less amount of data, by changing the neurons, best results could be obtained. The predicted results are near agreement with the experimental results, and ANN acts as a promising tool for the prediction of compressive strength with tiny errors." @default.
- W4285247165 created "2022-07-14" @default.
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- W4285247165 date "2022-01-01" @default.
- W4285247165 modified "2023-09-27" @default.
- W4285247165 title "Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using AI Approach" @default.
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- W4285247165 doi "https://doi.org/10.1007/978-981-16-8496-8_2" @default.
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