Concrete compressive strength analysis using a combined classification and regression technique

High performance concrete (HPC) is a complex composite material, and a model of its compressive strength must be highly nonlinear. Many studies have tried to develop accurate and effective predictive models for HPC compressive strength, including linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent reports that a hierarchical structure outperforms a flat one, this study proposes a hierarchical classification and regression (HCR) approach for improving performance in predicting HPC compressive strength. Specifically, the first-level analyses of the HCR find exact classes for new unknown cases. The cases are then entered into the corresponding prediction model to obtain the final output. The analytical results for a laboratory dataset show that the HCR approach outperforms conventional flat prediction models (LR, ANNs, and SVR). Notably, the HCR with a 4-class support vector machine in the first level combined with a single ANNs obtains the lowest mean absolute percentage error.

► Concrete compressive strength (CCS) is highly nonlinear
► This study proposes a hierarchical artificial intelligence for predicting CCS
► The analytical results show that the hybrid approach outperforms conventional flat prediction models
► The approach automates concrete mix design for compressive strength in civil construction.