Development of New Rheological Models for Class G Cement with Nanoclay as an Additive Using Machine Learning Techniques
Zeeshan Tariq, Mobeen Murtaza, Mohamed Mahmoud
Abstract
). Experimental data generated were used for the training of feed-forward neural networks. The predicted values of the rheological properties from the trained model showed a good agreement when compared with the experimental values. The average absolute percentage error was less than 5% in both training and validation phases of modeling. A trend analysis was carried out to ensure that the proposed models can define the underlying physics. From the validation and the trend analysis, it was found that the new models can be used to predict cement rheological properties within the range of data set on which the models were trained. The proposed models are independent of laboratory-dependent variables and can give quick and real-time values of the rheological parameters.