Application of Artificial Neural Network and Genetic Programming in Civil Engineering

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InCollection{Samui:2016:CEECMTA.14,

• author = "Pijush Samuiy",
• title = "Application of Artificial Neural Network and Genetic Programming in Civil Engineering",
• booktitle = "Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications",
• publisher = "IGI Global",
• year = "2016",
• chapter = "14",
• pages = "360--368",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Gaussian Process Regression, GPR, Minimax Probability Machine Regression, MPMR",
• ISBN = "1-4666-9619-2",
• URL = "https://www.igi-global.com/chapter/determination-of-pull-out-capacity-of-small-ground-anchor-using-data-mining-techniques/144504",
• DOI = "doi:10.4018/978-1-4666-9619-8.ch014",
• abstract = "The determination of pull out capacity (Q) of small ground anchor is an imperative task in civil engineering. This chapter employs three data mining techniques (Genetic Programming [GP], Gaussian Process Regression [GPR], and Minimax Probability Machine Regression [MPMR]) for determination of Q of small ground anchor. Equivalent anchor diameter (Deq), embedment depth (L), average cone resistance (qc) along the embedment depth, average sleeve friction (fs) along the embedment depth, and Installation Technique (IT) are used as inputs of the models. The output of models is Q. GP is an evolutionary computing method. The basic idea of GP has been taken from the concept of Genetic Algorithm. GPR is a probabilistic non-parametric modeling approach. It determines the parameter from the given datasets. The output of GPR is a normal distribution. MPMR has been developed based on the principal mimimax probability machine classification. The developed GP, GPR, and MPMR are compared with the Artificial Neural Network (ANN). This chapter also gives a comparative study between GP, GPR, and MPMR models.",
• notes = "National Institute of Technology Patna, India",

}

Genetic Programming entries for Pijush Samuiy

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