An improved prediction of high-performance concrete compressive strength using ensemble models and neural networks

Traditional methods for proportioning of high-performance concrete (HPC) have certain shortcomings, such as high costs, usage constraints, and nonlinear relationships. Implementing a strategy to optimize the mixtures of HPC can minimize design expenses, time spent, and material wastage in the construction sector. Due to HPC's exceptional qualities, such as high strength (HS), fluidity and resilience, it has been broadly used in construction projects. In this study, we employed Generalized Regression Neural Network (GRNN), Nonlinear AutoRegressive with exogenous inputs (NARX neural network), and Random Forest (RF) models to estimate the Compressive Strength (CS) of HPC in the first scenario. In contrast, the second scenario involved the development of an ensemble model using the Radial Basis Function Neural Network (RBFNN) to detect inferior performance of standalone model combinations. The output variable was the 28 Days CS in MPa, while the input variables included slump (S), water-binder ratio (W/B) %, water content (W) kg/m3, fine aggregate ratio (S/a) %, silica fume (SF)%, and superplasticizer (SP) kg/m3. An RF model was developed by using R Studio; GRNN and NARX-NN models were developed by using the MATLAB 2019a toolkit; and the pre- and post-processing of data was carried out by using E-Views 12.0. The results indicate that in the first scenario, the Combination M1 of the RF model outperformed other models, with greater prediction accuracy, yielding a PCC of 0.854 and MAPE of 4.349 during the calibration phase. In the second scenario, the ensemble of RF models surpassed all other models, achieving a PCC of 0.961 and MAPE of 0.952 during the calibration phase. Overall, the proposed models demonstrate significant value in predicting the CS of HPC.