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Computers and Concrete Volume 19, Number 6, June 2017 , pages 651-658 DOI: https://doi.org/10.12989/cac.2017.19.6.651 |
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An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming |
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Mauro Castelli, Leonardo Trujillo, Ivo Gonçalves and Aleš Popovič
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| Abstract | ||
| High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process. | ||
| Key Words | ||
| high performance concrete; concrete strength; genetic programming; local search; semantics | ||
| Address | ||
| Mauro Castelli: NOVA IMS, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal Leonardo Trujillo: Tree-Lab, Instituto Tecnológico de Tijuana, Tijuana B.C., 22500, México Ivo Gonçalves: 1) NOVA IMS, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal 2) Department of Informatics Engineering, CISUC, University of Coimbra, 3030-290, Coimbra, Portugal Aleš Popovič: 1) NOVA IMS, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal 2) Faculty of Economics, University of Ljubljana, Kardeljeva Ploščad 17, 1000, Ljubljana, Slovenia | ||