Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate

The compressive strength of fiber-reinforced rubberized recycled aggregate concrete (FR3C) is an important performance indicator for its practical application and durability in the concrete industry. The constituents of this concrete exhibit distinct mechanical properties, particularly the waste rubber and fiber components. The mix design for desired compressive strength of such concrete is critical and depends on the constituents' internal complex relationships. Therefore, this study proposes machine learning-based models to predict the compressive strength and internal dependencies among the constituents of the FR3C's. Given this, a dataset consisting of 905 experimental data of various mix proportions were compiled to train and test thirteen different machine learning models, namely linear regression, ridge regression, lasso regression, support vector machine, k-nearest neighbors, artificial neural network, decision tree, random forest, AdaBoost, Voting Regressor, Gradient Boost, CatBoost, and XGBoost. The input characteristics were water/cement ratio (W/C), percentage of rubber, replacement level of recycled concrete aggregate (RCA), percentage of fiber, and its type. The results suggest that the CatBoost is the most accurate model for predicting the compressive strength of FR3C, with the highest coefficient of determination, lowest root mean squared error (RMSE), and low mean absolute error on the test data. Feature importance evaluation showed that w/c ratio, nominal aggregate size, and rubber (%) are important critical parameters that significantly influence the compressive strength of FR3C.

Automated summary

This study proposes machine learning-based models to predict the compressive strength of fiber-reinforced rubberized recycled aggregate concrete (FR3C) and explores the internal dependencies among its constituents. The results show that the CatBoost model has the highest prediction accuracy and the lowest errors.