A new predictive model for compressive strength of HPC using gene expression programming

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@Article{journals/aes/MousaviAGAB12,

• author = "Seyyed Mohammad Mousavi and Pejman Aminian and Amir Hossein Gandomi and Amir Hossein Alavi and Hamed Bolandi",
• title = "A new predictive model for compressive strength of HPC using gene expression programming",
• journal = "Advances in Engineering Software",
• year = "2012",
• volume = "45",
• number = "1",
• pages = "105--114",
• month = mar,
• keywords = "genetic algorithms, genetic programming, gene expression programming, compressive strength, regression analysis, sensitivity analysis, prediction, high performance concrete",
• ISSN = "0965-9978",
• URL = "http://www.sciencedirect.com/science/article/pii/S0965997811002535",
• DOI = "doi:10.1016/j.advengsoft.2011.09.014",
• abstract = "In this study, gene expression programming (GEP) is used to derive a new model for the prediction of compressive strength of high performance concrete (HPC) mixes. The model is developed using a comprehensive database obtained from the literature. The validity of the proposed model is verified by applying it to estimate the compressive strength of a portion of test results that are not included in the analysis. Linear and nonlinear least squares regression analyses are performed to benchmark the GEP model. Contributions of the parameters affecting the compressive strength are evaluated through a sensitivity analysis. GEP is found to be an effective method for evaluating the compressive strength of HPC mixes. The prediction performance of the optimal GEP model is better than the regression models.",
• bibdate = "2011-12-31",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aes/aes45.html#MousaviAGAB12",

}

Genetic Programming entries for Seyyed Mohammad Mousavi Pejman Aminian A H Gandomi A H Alavi Hamed Bolandi

Citations