Prediction of Mechanical Strength by Using an Artificial Neural Network and Random Forest Algorithm
Kamal Upreti, Manvendra Verma, Meena Agrawal, Jatinder Garg, Rekha Kaushik, Chinmay Agrawal, Divakar Singh, Rajamani Narayanasamy
Abstract
Geopolymer concrete could be the best alternative to ordinary Portland cement concrete due to its higher performance in any severe condition. It reduces the carbon footprints to a very higher level. Machine learning methods are the future of the construction industry because it predicts the mechanical strengths of concrete mix design on the basis of their constituents without destructive test conduction. This study is aimed at developing the models to predict the mechanical strengths and validate them with the actual results. After the experimental investigation, we found the results of the mechanical (including compressive, splitting tensile, and flexural tensile) strength. The M2 mix of geopolymer concrete got the highest mechanical strengths whereas the M5 mix gets the lowest mechanical strengths among all the mix designs. The machine learning methods ANN (artificial neural network) and random forest are used to develop the models based on mixed experimental results. Mechanical strength results are taken as outputs, and mixed constituents are taken as inputs for training and testing. The performance of predicted results is checked based on R 2 , MAE (mean absolute error), RMSE (relative mean square error), RAE (relative absolute error), and RRSE (root‐relative square error). Random forest models show the best prediction to the ANN models because it shows the negligible error between actual and predicted values. The R 2 value is 1 of 12 predicted results out of 15 by the use of random forest methods. So it is most suitable to predict the strength of geopolymer concrete based on their constituent’s material quantity.