Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33051
Title: Using Machine Learning and Finite Element Modelling to Develop a Formula to Determine the Deflection of Horizontally Curved Steel I-beams
Authors: Ababu, Elvis M. 
Markou, George 
Bakas, Nikolaos P. 
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences;ENGINEERING AND TECHNOLOGY;Civil Engineering;Other Engineering and Technologies
Keywords: Curved Beams;Machine Learning;Steel;Finite Element Method;Design
Issue Date: 1-Jan-2022
Source: Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) - vol. 3, pp. 958-963
Volume: 3
Start page: 958
End page: 963
Conference: International Conference on Agents and Artificial Intelligence 
Abstract: The use of curved I-beams has been increasing throughout the years as the steel forming industry continues to advance. However, there are often design limitations on such structures due to the lack of recommendations and design code formulae for the estimation of the expected deflection of these structures. This is attributed to the lack of understanding of the behaviour of curved I-beams that exhibit extreme torsion and bending. Thus, currently, there are no formulae readily available for practising engineers to use to estimate the deflection of curved beams. Since the design of light steel structures is often governed by serviceability considerations, this paper aims to analyse the properties of curved steel I-beams and their impact on deflection as well as develop an accurate formula that will be able to predict the expected deflection of these beams. By using a combination of an experimentally validated finite element modelling approach and machine learning. Numerous formulae are developed and tested for the needs of this research work. The final proposed formula, which is the first of its kind, was found to have an average error of 4.11% in estimating the midspan deflection on the test dataset.
URI: https://hdl.handle.net/20.500.14279/33051
ISBN: 978-989-758-547-0
ISSN: 21843589
DOI: 10.5220/0010982400003116
Type: Conference Papers
Affiliation : University of Pretoria 
RDC Informatics 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

CORE Recommender
Show full item record

Page view(s)

18
Last Week
5
Last month
checked on Nov 23, 2024

Google ScholarTM

Check

Altmetric


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