Using machine learning algorithms to develop a predictive model for computing the maximum deflection of horizontally curved steel I-beams
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Date
Authors
Ababu, Elvis
Markou, George
Skorpen, Sarah Anne
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Horizontally curved steel I-beams exhibit a complicated mechanical response as they
experience a combination of bending, shear, and torsion, which varies based on the geometry of the
beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they can fail
due to either flexure, shear, torsion, lateral torsional buckling, or a combination of these types of
failure. This therefore necessitates the usage of complicated nonlinear analyses in order to accurately
model their behaviour. Currently, little guidance is provided by international design standards in
consideration of the serviceability limit states of horizontally curved steel I-beams. In this research, an
experimentally validated dataset was created and was used to train numerous machine learning (ML)
algorithms for predicting the midspan deflection at failure as well as the failure load of numerous
horizontally curved steel I-beams. According to the experimental and numerical investigation, the
deep artificial neural network model was found to be the most accurate when used to predict the
validation dataset, where a mean absolute error of 6.4 mm (16.20%) was observed. This accuracy far
surpassed that of Castigliano’s second theorem, where the mean absolute error was found to be equal
to 49.84 mm (126%). The deep artificial neural network was also capable of estimating the failure
load with a mean absolute error of 30.43 kN (22.42%). This predictive model, which is the first of its
kind in the international literature, can be used by professional engineers for the design of curved
steel I-beams since it is currently the most accurate model ever developed.
Description
This article belongs to the Special Issue titled 'Computational Methods in Structural Engineering'.
DATA AVAILABITY STATEMENT: The datasets that were developed for the needs of this research work can be found through the following link (https://github.com/nbakas/nbml/tree/a0d27c94dd59068 8815180ebf6428963a24ca245/datasets, accessed on 1 July 2024).
DATA AVAILABITY STATEMENT: The datasets that were developed for the needs of this research work can be found through the following link (https://github.com/nbakas/nbml/tree/a0d27c94dd59068 8815180ebf6428963a24ca245/datasets, accessed on 1 July 2024).
Keywords
Structural engineering, Structural steel, Curved beams, Machine learning, Finite element modelling, SDG-09: Industry, innovation and infrastructure
Sustainable Development Goals
SDG-09: Industry, innovation and infrastructure
Citation
Ababu, E.; Markou, G.;
Skorpen, S. Using Machine Learning
Algorithms to Develop a Predictive
Model for Computing the Maximum
Deflection of Horizontally Curved
Steel I-Beams. Computation 2024, 12,
151. https://doi.org/10.3390/computation12080151.