Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modeling its behavior represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data. © 2013 Elsevier Ltd. All rights reserved.

Castelli, M., Vanneschi, L., Silva, S. (2013). Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. EXPERT SYSTEMS WITH APPLICATIONS, 40(17), 6856-6862 [10.1016/j.eswa.2013.06.037].

Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators

CASTELLI, MAURO
;
VANNESCHI, LEONARDO
;
2013

Abstract

Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modeling its behavior represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data. © 2013 Elsevier Ltd. All rights reserved.
Articolo in rivista - Articolo scientifico
genetic programming
English
6856
6862
7
Castelli, M., Vanneschi, L., Silva, S. (2013). Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. EXPERT SYSTEMS WITH APPLICATIONS, 40(17), 6856-6862 [10.1016/j.eswa.2013.06.037].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/62198
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