Intelligent Modeling and Prediction of Elastic Modulus of Concrete Strength via Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/swarm/GandomiATY13,

• author = "Amir Hossein Gandomi and Amir Hossein Alavi and T. O. Ting and Xin-She Yang",
• title = "Intelligent Modeling and Prediction of Elastic Modulus of Concrete Strength via Gene Expression Programming",
• booktitle = "Proceedings of the 4th International Conference on Advances in Swarm Intelligence, ICSI 2013, Part {I}",
• year = "2013",
• editor = "Ying Tan and Yuhui Shi and Hongwei Mo",
• volume = "7928",
• series = "Lecture Notes in Computer Science",
• pages = "564--571",
• address = "Harbin, China",
• month = jun # " 12-15",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Gene expression programming, Tangent elastic modulus, Normal and High strength concrete, Compressive strength, Formulation",
• isbn13 = "978-3-642-38702-9",
• bibdate = "2013-05-23",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/swarm/icsi2013-1.html#GandomiATY13",
• DOI = "doi:10.1007/978-3-642-38703-6_66",
• size = "8 pages",
• abstract = "The accurate prediction of the elastic modulus of concrete can be very important in civil engineering applications. We use gene expression programming (GEP) to model and predict the elastic modulus of normal-strength concrete (NSC) and high-strength concrete (HSC). The proposed models can relate the modulus of elasticity of NSC and HSC to their compressive strength, based on reliable experimental databases obtained from the published literature. Our results show that GEP can be an effective method for deriving simplified and precise formulations for the elastic modulus of NSC and HSC. Furthermore, the comparison study in the present work indicates that the GEP predictions are more accurate than other methods.",

}

Genetic Programming entries for A H Gandomi A H Alavi T O Ting Xin-She Yang

Citations