Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete

Concrete is the most often used material in the building sector. The use of ground-granulated blast-furnace slag (GGBFS) and recycled concrete aggregates (RCA) in concrete reduces its negative environmental impact. However, increasing the number of ingredients in concrete complicates the prediction of its compressive strength. This research aims to develop a machine learning model for predicting the compressive strength of eco-friendly concrete using Multivariate Adaptive Regression Splines (MARS). MARS is a well-known technique for creating predictive modeling equations using experimental data. A data set of 161 concrete specimens was obtained to train and evaluate the machine learning algorithms for this work. The findings of the MARS prediction algorithm were compared to those of the Support Vector Machine and the Random Forest regression algorithms (black-box models) using the training set. A hyperparameter tuning procedure was applied to find the optimal MARS, RF, and SVM model parameters. The results of 5-fold cross-validation indicate that the MARS optimal model has the highest coefficient of determination (R2), with a value equal to 0.889, and the lowest Root Mean Squared Error (RMSE) measure, with a value equal to 4.110 MPa. For our case, the performance metrics measures proved the superiority of the MARS model when compared with RF and SVM. The final MARS optimal prediction equations were built using a full training set and were then evaluated using unseen data (testing set). Our model has the potential to help civil engineers in designing durable infrastructures.