Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning
Journal article
Musab,R., Ferreira, F., Abarkan, A., Limbachiya, V. and Shamass, R. (2023). Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning. Results in Engineering. 17 (100902). https://doi.org/10.1016/j.rineng.2023.100902
| Authors | Musab,R., Ferreira, F., Abarkan, A., Limbachiya, V. and Shamass, R. |
|---|---|
| Abstract | The use of circular hollow sections (CHS) have seen a large increase in usage in recent years mainly because of the distinctive mechanical properties and unique aesthetic appearance. The focus of this paper is the behaviour of cold-rolled CHS beam-columns made from normal and high strength steel, aiming to propose a design formula for predicting the ultimate cross-sectional load carrying capacity, employing machine learning. A finite element model is developed and validated to conduct an extensive parametric study with a total of 3410 numerical models covering a wide range of the most influential parameters. The ANN model is then trained and validated using the data obtained from the developed numerical models as well as 13 test results compiled from various research available in the literature, and accordingly a new design formula is proposed. A comprehensive comparison with the design rules given in EC3 is presented to assess the performance of the ANN model. According to the results and analysis presented in this study, the proposed ANN-based design formula is shown to be an efficient and powerful design tool to predict the cross-sectional resistance of the CHS beam-columns with a high level of accuracy and the least computational costs. |
| Keywords | CHS beam-Columns. cold-formed. normal and high strength steels. eurocode 3. finite element model. artificial neural networks (ANN) |
| Year | 2023 |
| Journal | Results in Engineering |
| Journal citation | 17 (100902) |
| Publisher | Elsevier |
| ISSN | 2590-1230 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.rineng.2023.100902 |
| Web address (URL) | https://www.sciencedirect.com/science/article/pii/S2590123023000294?via%3Dihub |
| Publication dates | |
| 19 Jan 2023 | |
| Publication process dates | |
| Accepted | 17 Jan 2023 |
| Deposited | 07 Feb 2023 |
| Publisher's version | License File Access Level Open |
| Accepted author manuscript | License File Access Level Controlled |
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