Abstract
In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found superior to the results published in the literature (R2 = 0.776 or 0.768) when predicting the load capacity of elliptical CFST columns. Finally, sensitivity analysis showed that the thickness of the steel tube and the minor axis length of the elliptical cross section were the most influential parameters. For practical application, a Graphical User Interface (GUI) was developed in MATLAB for researchers and engineers and to support the teaching and interpretation of the axial behavior of CFST columns.
Highlights
Composite concrete-filled steel tubular (CFST) columns are considerably employed in the construction of infrastructures thanks to their excellent structural behavior [1]. ese structural members exhibit many benefits than single material columns. ese advantages could be listed as fire, axial capacity, and earthquake resistance [2, 3]
The prevention of local buckling in the elliptical CFST columns could be well-established thanks to the concrete core [12, 13]. e elliptical section possesses aesthetic qualities along with more effective bending resistance when compared to circular section due to having different second moments of area around its principal axes [14]. erefore, analyzing the structural behavior, especially the ultimate load of elliptical CFST columns, is essential to facilitate the use in civil engineering structures
Erefore, the primary objective of the present work was to develop an machine learning (ML)-based model to predict the ultimate load of elliptical CFST columns under axial loading
Summary
Composite concrete-filled steel tubular (CFST) columns are considerably employed in the construction of infrastructures thanks to their excellent structural behavior [1]. ese structural members exhibit many benefits than single material columns (i.e., concrete columns or hollow steel columns). ese advantages could be listed as fire, axial capacity, and earthquake resistance [2, 3]. Composite concrete-filled steel tubular (CFST) columns are considerably employed in the construction of infrastructures thanks to their excellent structural behavior [1]. The use of elliptical CFST columns has gained attention from the scientific community and applied engineering as it provides specific advantages compared to other cross sections of CFST, including a better strength and rigidity as well as fire resistance [9]. Due to its reasonable distribution of the major-minor axis, elliptical CFST column exhibits a better architectural aesthetic appearance and a small fluid resistance coefficient [10, 11]. E elliptical section possesses aesthetic qualities along with more effective bending resistance when compared to circular section due to having different second moments of area around its principal axes [14]. The prevention of local buckling in the elliptical CFST columns could be well-established thanks to the concrete core [12, 13]. e elliptical section possesses aesthetic qualities along with more effective bending resistance when compared to circular section due to having different second moments of area around its principal axes [14]. erefore, analyzing the structural behavior, especially the ultimate load of elliptical CFST columns, is essential to facilitate the use in civil engineering structures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
