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title

Prediction of shear strength of RC beams using decision tree method

Credit to Download: 1 | Page Numbers 6 | Abstract Views: 435
Year: 2014
COI code: ICSAU02_0208
Paper Language: English

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Authors Prediction of shear strength of RC beams using decision tree method

  Iman Mansouri - Assistant Professor of Civil Engineering, Birjand University of Technology
  Sadegh Etedali - Assistant Professor of Civil Engineering, Birjand University of Technology

Abstract:

This paper reports the shear strength of reinforced concrete (RC) beams without stirrups predictive model based on the decision tree method. This method is capable to classify and predict categorical variables: its competitive advantage over other extensively used modeling techniques, such as regression method and artificial neural network (ANN) method, lies in the ability to generate accurate predictive models with explainable flowchart-like tree structures that make possible users to rapidly extract useful information. Another advantage of this methodology is that it can be utilized by users without requiring much computation knowledge. To show its applicability, the method is applied to estimate shear strength of RC beams. A set of published database containing 165 experimental test results is used to develop the model. The results reveal that the use of decision tree method can predict shear strength of RC beams accurately.

Keywords:

RC beam, Shear strength, Decision tree, Modeling

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https://www.civilica.com/Paper-ICSAU02-ICSAU02_0208.html
COI code: ICSAU02_0208

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Mansouri, Iman & Sadegh Etedali, 2014, Prediction of shear strength of RC beams using decision tree method, 2nd international congress of structure, architecture and urban development, تبريز, دبيرخانه دائمي كنگره بين المللي سازه ، معماري و توسعه شهري, https://www.civilica.com/Paper-ICSAU02-ICSAU02_0208.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Mansouri, Iman & Sadegh Etedali, 2014)
Second and more: (Mansouri & Etedali, 2014)
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Type: state university
Paper No.: 577
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