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Τίτλος: DEVELOPMENT OF FORMULAE FOR THE SECTION ROTATIONS DUE TO BENDING OF CURVED STEEL I-BEAMS THROUGH AI AND ML ALGORITHMS
Συγγραφείς: Chibaya, Victor T. 
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: Finite Element Analysis;Machine Learning Algorithm;Steel Structures;Torsion;Horizontally Curved Beams;Closed Form Solutions
Ημερομηνία Έκδοσης: 1-Ιαν-2023
Πηγή: 9th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Athens, Greece, 12-–14 June 2023
Conference: ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering 
Περίληψη: Currently, there is no standardized method nor a reliable and easy to implement analytical relationship for obtaining the section rotations of horizontally curved steel I-beams that could be specified in design guides and used by practicing engineers. Analysis of horizontally curved I-beams is especially difficult to perform using traditional methods because, even under the influence of gravitational loads, the beams experience a combination of primary flexure and non-uniform torsion that result in vertical deflections that are coupled with twist rotations. This research work proposes a systematic approach that was used to develop a simple analytical formula that could predict the sectional rotation of horizontally curved steel I-beams that were fully fixed at each end and subject to a vertical point load at their midspan. The development of such a formula had never been attempted before in international literature yet by combining data derived from 3D finite element analysis with a machine learning algorithm that makes use of higher-order nonlinear regression, a predictive formula was developed that could calculate the section's rotation of horizontally curved steel I-beams with a mean absolute error of just 1.44%.
URI: https://hdl.handle.net/20.500.14279/33069
ISSN: 26233347
Type: Conference Papers
Affiliation: University of Pretoria 
National Infrastructures for Research and Technology 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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