| Autor: |
Xuan-Bang NGUYEN, Trong-Ha NGUYEN, Kieu-Vinh Thi NGUYEN, Thanh-Tung Thi NGUYEN, Duy-Duan NGUYEN |
| Jazyk: |
English<br />French |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Materials and Engineering Structures, Vol 10, Iss 4, Pp 551-568 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2170-127X |
| Popis: |
The behavior of concrete columns reinforced with fiber reinforced polymer (FRP) bars is different from conventional reinforced concrete columns due to the mechanical properties of FRP bars. This study develops a novel machine learning (ML) model, namely gradient boosting regression tree (GBRT), for efficiently predicting the axial load-bearing capacity (ALC) of concrete columns reinforced with FRP bars. A data base containing 283 experimental results is collected to develop the ML model. Seven code-based and empirical-based equations are also included in comparison with the developed ML models. Moreover, we also propose a multiple linear regression (MLR)-based formula for calculating the ALC of the FRP-concrete column. The performance results of GBRT model are compared with those of published formulas and the proposed MLR-based formula. Statistical properties including , , and are calculated to evaluate the accuracy of those predictive models. The comparisons demonstrate that GBRT outperforms other models with very high values and small . Moreover, the influence of input parameters on the predicted ALC isevaluated. Finally, an efficient graphical user interface tool is developed to simplify the practical design process of FRP-concrete columns. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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