Prediction of shear capacity of RC beams strengthened with FRCM composite using hybrid ANN-PSO model
Tác giả: Trong-Ha Nguyen, Ngoc-Long Tran, Van-Tien Phan, Duy-Duan Nguyen
Case Studies in Construction Materials
Quyển: Vol 18 Trang: e02183
Năm xuất bản: 7/2023
Tóm tắt
The aim of this study is to develop a hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) model for improving shear strength prediction of reinforced concrete (RC) beams strengthened with fiber reinforced cementitious matrix (FRCM). A set of 89 experimental test results of strengthening RC beams are collected and used for developing the ANN-PSO model. The performance results of ANN-PSO are compared with those of pure ANN model. Typical statistical properties including the coefficient of determination (R 2), root mean squared error (RMSE), and the number of predicted data falling in a deviation of±20% compared with experimental data (a 20− index) are calculated to evaluate the accuracy of those models. The comparisons reveal that ANN-PSO outperforms the ANN model with R 2, RMSE, and a 20− index values of 0.937, 6.02, and 0.842, respectively. Moreover, the effects of input
Từ khóa
Artificial Neural Network (ANN)Particle Swarm Optimization (PSO)Reinforced concrete beamFiber reinforced cementitious matrix (FRCM)Graphical user interface
