Journal
AUTOMATION IN CONSTRUCTION
Volume 24, Issue -, Pages 52-60Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2012.02.001
Keywords
High performance concrete; Compressive strength; Hierarchical data mining; Classification; Regression
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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. (C) 2012 Elsevier B.V. All rights reserved.
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