Abstract
Composite columns were preferred over reinforced concrete columns in modern-day construction techniques due to their confinement effect. Different materials were utilized as the outer confining tube and are mainly characterized by their mechanical properties. The main objective of this research is to develop a novel simplified artificial neural network model for the determination of the ultimate axial load of the circular composite columns irrespective of the type of confining tube. A database had been created with the existing experimental results of the composite columns and is employed for training, testing, and validation of the model. A set of composite columns were selected from the real-time experimental study and the ultimate axial load of the columns was determined and validated against the developed model. A user-friendly graphical user interface is created from the proposed model which can help the researchers for anticipating the ultimate axial load of the circular composite columns easily and efficiently.
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The authors confirm that the datasets generated or analyzed during the current research study are included in the article. Raw data that support the findings are available from the corresponding author upon reasonable request.
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Acknowledgements
The authors acknowledged the support by All India Council for Technical Education (AICTE) under Research Promotion Scheme; File No. 8232/RIFD/RPS (POLICY-1)/ 2018–19.
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This work was supported by All India Council for Technical Education (AICTE) under Research Promotion Scheme; File No. 8232/RIFD/RPS (POLICY-1) / 2018–19.
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Conceptualization: VV, MAMY; methodology: GP, RJ; investigation: VV; writing—original draft preparation: VV; writing—review and editing: GP, RJ; funding acquisition: RJ; resources: GP, RJ; supervision: GP, RJ.
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Veerapandian, V., Pandulu, G., Jayaseelan, R. et al. Simplified deep-learning approach for estimating the ultimate axial load of circular composite columns. Asian J Civ Eng 24, 2375–2387 (2023). https://doi.org/10.1007/s42107-023-00647-9
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DOI: https://doi.org/10.1007/s42107-023-00647-9