A streamlined deep-learning algorithm for predicting the ultimate axial load of self-stressed columns

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.