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https://hdl.handle.net/20.500.14279/33051
Τίτλος: | Using Machine Learning and Finite Element Modelling to Develop a Formula to Determine the Deflection of Horizontally Curved Steel I-beams | Συγγραφείς: | 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 | Λέξεις-κλειδιά: | Curved Beams;Machine Learning;Steel;Finite Element Method;Design | Ημερομηνία Έκδοσης: | 1-Ιαν-2022 | Πηγή: | 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 | Περίληψη: | 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 |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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