Journal
CONSTRUCTION AND BUILDING MATERIALS
Volume 184, Issue -, Pages 229-247Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2018.06.219
Keywords
Compressive strength; High performance concrete; Support vector machine; Enhanced cat swarm optimisation
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Funding
- Australian Research Council Research Hub for Nanoscience Based Construction Materials Manufacturing (NANOCOMM) [IH150100006]
- Roads and Maritime Services (RMS)
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Concrete strength (CS) is one of the most important performance parameters that are crucial in the design of concrete structure. The reliable prediction of strength can reduce the cost and time in design and avoid the waste of materials caused by a large number of mixture trials. In this study, a novel predictive model is put forward to predict the CS of high performance concrete (HPC) using support vector machine (SVM) approach, which has benefits of nonlinear mapping, high robustness and great generalisation capacity. In the proposed model, the input variables include the contents of water, cement, blast furnace slag, fly ash, super plasticiser, coarse and fine aggregates and curing age, which produces the CS of HPC as the output. In order to improve the model performance, a type of enhanced cat swarm optimisation (ECSO) is adopted to optimise the key parameters of SVM. Finally, the model is trained and evaluated using a total of 1761 data records, which are collected from existing literatures. The results indicate that the proposed SVM-based model exhibits better recognition ability and higher prediction accuracy than other commonly used models, and it can be considered as an effective method to predict the CS property of HPC in infrastructure practice. (C) 2018 Elsevier Ltd. All rights reserved.
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