Predicting Compressive Strength of Self-Compacting Concrete Using Machine and Deep Learning Models

This paper discusses the compressive strength prediction for self-compacting concrete (SCC) by a host of machine learning (ML) and deep learning (DL) models is discussed in this research work. Random Forest (RF), Keras Regressor (KR), Extremely Randomized Trees (ERT), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), and Category Boosting (CB) are some of the many ensemble methods until now. In addition, the ability of several models to predict the compressive strength of SCC was examined with generalized additive models like Gradient Boosting Regressor and Neural Networks based on Keras. Twenty papers constituted the dataset, which was divided into three subsets for validation, testing, and training. The principal input parameters utilized in model building are superplasticizers, cement, water, fine aggregates, coarse aggregates, and mineral admixtures. To check the accuracy of each model developed, some performance indicators were chosen, like R², RMSE, MAE, and MAPE, which measure how accurately a model predicts compressive strength. The best predictive accuracy was found for the models under test in GB with R² = 5.12, MSE = 26.23, and MAE = 4.13, whereas Keras Regressor also performed very well with R² = 0.6948, RMSE = 0.0832, and MAE = 0.0569. These results thus establish that the GB and KR models can prove to be good resources for predictive efficiency in determining the compressive strength of SCC, exhibiting great potential for machine learning and deep learning methodologies applied to concrete materials.