Issue |
MATEC Web Conf.
Volume 398, 2024
2nd International Conference on Modern Technologies in Mechanical & Materials Engineering (MTME-2024)
|
|
---|---|---|
Article Number | 01033 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/matecconf/202439801033 | |
Published online | 25 June 2024 |
Application of Artificial Neural Networks for Predicting Axial Strain of FRP-Confined Concrete
Department of Civil Engineering, University of Engineering and Technology Taxila, 47050, Pakistan.
* hafizazan196@gmail.com
# aliwarraich2000@gmail.com
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+ khawajazain948@gmail.com
** attamustafa280443@gmail.com
## ali.raza@uettaxila.edu.pk
Multiple research studies have developed frameworks to forecast the ability of concrete structural elements to withstand compression along their length. However, further exploration is required to refine predictions for the axial compressive strain, as existing strain models lack precision. The earlier models were created with restricted and noisy data sets and basic modelling methods, underscoring the necessity for a more meticulous approach to introduce a more accurate strain model and to evaluate its forecasts against those of current models.This study wants to fill in the gap by creating models for how much concrete reinforced with fiber-reinforced polymer (FRP) can stretch using computer simulations called artificial neural networks (ANN). This approach is based on a substantial database comprising 570 sample points. The comprehensive investigation of these estimates robustly validates the accuracy and practicality of the suggested ANN models for predicting the axial strain of FRP -confined concrete compression members.
Key words: fiber reinforced polymer (FRP) / confined concrete / artificial neural networks / strain model
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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