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Volume 9, Number 4, October 2024 , pages 253-268 DOI: https://doi.org/10.12989/acd.2024.9.4.253 |
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A streamlined deep-learning algorithm for predicting the ultimate axial load of self-stressed columns |
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P. Krithika, P. Gajalakshmi and M.Y. Mohammed Asif
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| Abstract | ||
| In light of their confinement effect, composite columns were frequently chosen in modern construction procedures over reinforced concrete columns. The outer confining tube was made of various materials, which are primarily distinguished through their mechanical characteristics. The fundamental purpose of this research is to evolve an ingenious artificial neural network simulation that is more straightforward and can be utilized to calculate the ultimate load carrying capacity of self-stressed columns irrespective of the category of impounding tube deployed. The most recent experimental findings associated with the composite columns were utilized in the creation of a database. This database is employed for training, testing, and validating the machine learning model. Following the contemporaneous experimental research, several composite columns were chosen for further examination, and the model that was developed was utilized to validate the ultimate axial load of the columns. | ||
| Key Words | ||
| ANN; expansive cement; PVA fibers; self-stressed; shrinkage | ||
| Address | ||
| P. Krithika: Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India P. Gajalakshmi and M.Y. Mohammed Asif: Department of Civil Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, India | ||
