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Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques

Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques

Pijush Samui
Copyright: © 2016 |Pages: 9
ISBN13: 9781466696198|ISBN10: 1466696192|EISBN13: 9781466696204
DOI: 10.4018/978-1-4666-9619-8.ch014
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MLA

Samui, Pijush. "Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques." Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 360-368. https://doi.org/10.4018/978-1-4666-9619-8.ch014

APA

Samui, P. (2016). Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques. In I. Management Association (Ed.), Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications (pp. 360-368). IGI Global. https://doi.org/10.4018/978-1-4666-9619-8.ch014

Chicago

Samui, Pijush. "Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques." In Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 360-368. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9619-8.ch014

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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 modelling 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.

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