Concrete Ultimate Strain Wrapped by Aramid Fiber-Reinforced Polymer: Application of Regression Analysis

Jingli WEN

doi:10.56748/ejse.24775

Authors

Jingli WEN Chongqing Technology and Business Institute

DOI:

https://doi.org/10.56748/ejse.24775

Keywords:

Aramid Fiber, Polymers, Concrete, Ultimate Strain, Least Square, Support Vector Regression, Feature Selection

Abstract

Concrete confinement using fiber-reinforced polymer (FRP) jackets is widely employed in structural retrofitting. A number of machine learning (ML) algorithms using tree-based methodologies were developed to forecast the ultimate strain (ε_cu) of circular columns wrapped in aramid fiber-reinforced polymer (AFRP). Hyperparameters are optimized using Artificial Hummingbird Optimizer (AHO) and Giant Trevally Optimizer (GTO) with least square support vector regression (LSSVR), leveraging AFRP-made concrete data from earlier studies to establish a suitable dataset. The AFRP jacket's total thickness (t_f), elastic modulus (E_f), ultimate tensile strength (f_f), the height of the column (L), unconfined compressive strength (f_co), and specimen diameter (d) are the input variables used in this approach. LSSVR(A) got the smallest uncertainty values (0.2893 and 0.2261) in training and evaluation. The values obtained during learning and evaluation were lower than LSSVR(G)'s 0.323 and 0.2476. The variation percentage between the two models for these measures, that is, at least 7% and sometimes 36%, depending on the variance percentage that was employed, shows the accuracy and reliability of the LSSVR(G). Regarding index values, throughout the training and assessment stages, the achieved values of 0.0676 and 0.0559, respectively, while the received the smallest values of 0.0591 and 0.0434.

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