New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Babanajad:2017:AES,

• author = "Saeed K. Babanajad and Amir H. Gandomi and Amir H. Alavi",
• title = "New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach",
• journal = "Advances in Engineering Software",
• year = "2017",
• ISSN = "0965-9978",
• DOI = "doi:10.1016/j.advengsoft.2017.03.011",
• URL = "http://www.sciencedirect.com/science/article/pii/S096599781630566X",
• abstract = "The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength ( sigma 1) to mix design parameters and principal stresses ( sigma 2, sigma 3), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions.",
• keywords = "genetic algorithms, genetic programming, Artificial intelligence, Gene expression programming, Triaxial, Machine learning, Computer-aided, Strength model",

}

Genetic Programming entries for Saeed K Babanajad A H Gandomi A H Alavi

Citations