Prediction of the Strength of the Concrete-Filled Tubular Steel Columns Using the Artificial Intelligence

Introduction . The machine learning algorithms are highly promising for predicting the load-bearing capacity of the building structures. The paper aims at building the predictive models for calculating the strength of the concrete-filled steel tubular (CFST) columns to enable a highly accurate prediction of the ultimate loads for the entire possible range of parameters affecting the load-bearing capacity of the eccentrically compressed columns. Materials and Methods . The article studies the eccentrically compressed short concrete-filled steel tubular (CFST) columns of circular cross-section. Model input parameters: column outer diameter, pipe wall thickness, yield strength of steel, compressive strength of concrete, relative eccentricity. Output parameters: the ultimate loads without taking into account and taking into account the random eccentricities. The models were trained on synthetic data generated based on the theoretical principles of the limit equilibrium method. Two machine learning models were built. When training the first model, the ultimate loads were determined at a given eccentricity of the longitudinal force without taking into account the additional random eccentricity. When training the second model, the additional random eccentricity was taken into account. The effect of the features on the model predictions was assessed using the Feature Importance function. The Optuna method was used to select the hyperparameters. The machine learning models were implemented in the Jupiter Notebook environment using the Gradient Boosting learning method. The total volume of the training sample was 179 025 samples. Results . The importance of the features most affecting the predictive values of the model have been determined. For both models, the outer diameter of the column and the relative eccentricity have proved to be the most important features, which is consistent with the existing experience of designing and calculating such structures. Optimisation of the hyperparameters using the Grid Search method enabled getting the improved results. The high accuracy of prediction has been ascertained by the low values of the regression metrics: MSE = 9.024; MAE = 9.250; MAPE = 0.004 — for the model built without taking into account the additional random eccentricity; MSE = 8.673; MAE = 8.673; MAPE = 0.004 — for the model built taking into account the additional random eccentricity. Discussion and Conclusion . The developed Gradient Boosting models for predicting the ultimate loads of the eccentrically compressed short concrete-filled steel tubular (CFST) columns of circular cross-section, both without taking into account and taking into account the additional random eccentricities, have demonstrated high accuracy and stability of prediction, they can be applied for assessing the strength of the columns during design and construction, which will reduce the time and resources involved in physical testing. In the future, it is planned to expand the data range by including other materials, different cross-section geometries of the columns and a slenderness parameter, which may improve the generalization ability of the model.