| Autor: |
Al Thawabteh, Jafar, Al Adwan, Jamal, Al Yamani, Walaa, Yasin, Bilal, Alzubi, Yazan |
| Předmět: |
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| Zdroj: |
International Review on Modelling & Simulations; Apr2024, Vol. 17 Issue 2, p62-73, 12p |
| Abstrakt: |
Nowadays, a concrete-filled steel tube column is considered a widely used section in the construction industry thanks to its high strength-to-weight ratio and ability to resist lateral loads. Recently, the use of concrete-filled steel tubes in composite structures has increased considerably due to their ability to withstand concentric compressive loads effectively while maintaining high ductility after yielding. These advantages are attributed to the combination of the properties of concrete and steel. Therefore, accurate estimation of their performance is essential for ensuring the safety and stability of structures. One approach to simply and rapidly predicting this is adopting soft computing techniques. Accordingly, this paper investigates the effectiveness of many soft computing techniques, including machine learning and regularized regression models, in predicting the capacity of rectangular concrete-filled steel tube columns. As part of the study, the results of the developed models will be compared against those of previous experimental works. Finally, the most accurate model will be recommended for utilization in such applications. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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