| Issue |
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
|
|
|---|---|---|
| Article Number | 06009 | |
| Number of page(s) | 7 | |
| Section | Building Energy Modeling | |
| DOI | https://doi.org/10.1051/e3sconf/202458306009 | |
| Published online | 25 October 2024 | |
CatBoost algorithms to predict the load-bearing capacity of centrally compressed short CFST columns of circular cross-section
1 Don State Technical University, 344002, 1 Gagarin Square, Rostov-on-Don, Russia
2 Kazan Federal University ; 420008, 18 Kremlevskaya st., Kazan, Russia
* Corresponding author: anton_chepurnenk@mail.ru
The article investigates the use of regression models, GradientBoostingRegressor and RandomForestRegressor, to predict the load-bearing capacity of centrally compressed short concrete filled steel tubular columns. The work is based on experimental data covering a wide range of column geometric characteristics and material strength characteristics. An important part of the analysis was to deter-mine the influence of various parameters on the model predictions. The importance of features, assessment of the quality of models (MSE, MAE, MAPE) were considered , and visualization of actual and predicted values was carried out to compare the results. The results showed that both models success-fully cope with the task of predicting the load-bearing capacity of structures under given conditions. Analysis of the importance of features revealed the most significant parameters affecting the load-bearing capacity of columns. Visualization of forecasts and analysis of residuals confirmed the adequacy of the models. Additionally, a process of tuning model parameters using cross-validation was carried out to optimize their performance. The results of the study can be used in engineering applications such as the design of reinforced concrete structures to predict load-bearing capacity.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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