| Title |
Predicting Shear Performance of Reinforced Concrete Beams Through Crack Data and Neural Network Modeling |
| Authors |
권아영(Kwon, Ah-Young) ; 엥리번듯(Eng Lybundith) ; 김창혁(Kim, Changhyuk) |
| DOI |
https://doi.org/10.5659/JAIK.2023.39.10.225 |
| Keywords |
Shear Crack Information; Artificial Neural Network; Regression Analysis; Structural Health Monitoring; Shear Strength |
| Abstract |
This study aims to predict how reinforced concrete beams perform under shear stress using Artificial Neural Networks (ANN) and numerical
crack data. The shear crack data from reinforced concrete beam specimens through finite element analysis were obtained. Afterward,
K-clustering to create an input dataset for the ANN analysis was used. The training and testing of a multi-layer perceptron regression model
involved the use of samples that had been analyzed using the Finite Element Method (FEM). The evaluation of the ANN model’s
performance considered the Mean Absolute Error (MAE), Adjusted R squared, Coefficient of Correlation, and Coefficient of Variation (CV). |