Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2972
Title: Estimation of Torsional Moment of Reinforced Concrete Beams with Machine Learning Algorithms
Other Titles: Makine Öğrenmesi Algoritmaları ile Betonarme Kirişlerin Burulma Momenti Tahmini
Authors: Doğan, Gamze
Keywords: beam torsion moment
machine learning
regression algorithm
Issue Date: 2022
Publisher: TUBITAK
Abstract: In this study, it is aimed to estimate the torsional strength values obtained from reinforced concrete beam tests with artificial intelligence algorithms without the need for experimental work. In this context, a data pool was created with the beam test data and machine learning regression algorithms were developed with these data. The beam dimensions, concrete compressive strength, stirrup strength, distance and spacing between stirrup arms, yield strength of stirrups and longitudinal torsion reinforcement, ratio of stirrups and longitudinal reinforcement, and longitudinal torsion reinforcement area data included in the experimental studies are input parameters for the algorithms, and the torsional strength value is output (target) selected as the parameter. Multiple Linear Regression, Support Vector Regression, Decision Trees, and Random Forest algorithm models were chosen as regression algorithms. As a result, the Support Vector Regression model gave the best result with a prediction success rate of 97.59 % for the estimation of the torsional strength by knowing the material and section properties of reinforced concrete beams. © 2022, TUBITAK. All rights reserved.
URI: https://doi.org/10.31202/ecjse.1031950
https://hdl.handle.net/20.500.13091/2972
ISSN: 2148-3736
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

Files in This Item:
File SizeFormat 
10.31202-ecjse.1031950-2113367.pdf941.42 kBAdobe PDFView/Open
Show full item record



CORE Recommender

Page view(s)

122
checked on Feb 26, 2024

Download(s)

32
checked on Feb 26, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.