Alsamia, Shaymaa and Koch, Edina (2024) Random forest regression on pullout resistance of a pile. POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES, 19 (3). pp. 28-33. ISSN 1788-1994
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Abstract
This research aims to study the pullout resistance of a helical pile using three methods of machine learning techniques, which are: random forest regression, support vector regression, and adaptive neuro-fuzzy inference system, based on experimental results of a helical pile. The performance of these three techniques has been d compared and the results show that random forest algorithm has best performance than neuro-fuzzy inference system and support vector technique. The results show that machine learning considered a good tool in terms of estimating the pullout resistance of helical piles in the soil.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | helical piles, pull-out resistance, artificial neural network, adaptive neuro-fuzzy inference system, random forest, support vector machine |
| Subjects: | T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 14 Nov 2024 09:59 |
| Last Modified: | 14 Nov 2024 09:59 |
| URI: | https://real.mtak.hu/id/eprint/209578 |
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