A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete

Compressive strength determination of high-performance concrete (HPC) is necessary for its practical usage. However, experimental testing for this purpose is resource intensive and time-consuming. Recently, machine learning has emerged in this field, especially data-driven modeling. In this study, a novel hybrid model for the compressive strength prediction of HPC is developed using the cascade forward neural network (CFNN) and artificial bee colony (ABC) optimization. The hybrid model used the ABC optimization method to select the optimal architecture of the neural network. A comprehensive database of 2171 data points containing information about cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, and compressive strength as output variable is used to develop the model. Results indicated that the optimal neural network architecture selected by the ABC method consists of 2 layers and the developed model (CFNN-ABC) could accurately predict the compressive strength of HPC with correlation (R) and determination coefficients (R2) of 0.976 and 0.953, respectively. The feature importance of the model revealed that the cement and sand were more influential features as compared to the other features. The partial dependence analysis demonstrated the effect of variation in input parameters on the attained compressive strength. Furthermore, the model validation with previously developed models using performance indices showed that the proposed hybrid model outperformed other models in all performance indices including root mean square error (RMSE ∼ 4.04) and mean absolute error (MAE ∼ 3.10). Therefore, the present work provides a novel and efficient option to predict the compressive strength of HPC which can aid in the design of sustainable infrastructures without going through costly and time-intensive experimentation.