Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam

Materials

Maria Rashidi¹  Yang Yu¹  Masoud Mohammadi¹  Bijan Samali¹  Zhaoqiu Lyu²  Thuc N. Nguyen²  Andy Nguyen³ 

[1] Centre for Infrastructure Engineering, Western Sydney University, Penrith, NSW 2751, Australia;School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia;School of Engineering, University of Southern Queensland, Springfield Central, QLD 4300, Australia;

关键词: back-propagation neural network;    genetic algorithm;    k-fold cross-validation;    torsional behavior;    reinforced concrete beam;   

DOI  :  10.3390/ma15041477

来源: DOAJ

【 摘 要 】

Due to the limitation of sample size in predicting the torsional strength of Reinforced Concrete (RC) beams, this paper aims to discuss the feasibility of employing a novel machine learning approach with K-fold cross-validation in a small sample range, which combines the advantages of a Genetic Algorithm (GA) and a Neural Network (NN) to predict the torsional strength of RC beams. This research study not only utilizes the application of a Back Propagation (BP) neural network and the Gene Algorithm-Back Propagation (GA-BP) neural network in the prediction of the torsional strength of the RC beam, but it also investigates neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and correlation coefficient (R²) are among the evaluation metrics used to assess the performance of the trained model. To elaborate on the superiority of the proposed network models in predicting the torsional strength of RC beams, a parametric study is conducted by comparing the proposed model to three commonly used empirical formulae from existing design codes. The comparative findings of this research study demonstrate that the performance of the BP neural network is highly similar to that of design codes; however, its accuracy is inadequate. After improving the weights and thresholds by k-fold cross-validation and GA, the prediction of the BP neural network shows higher consistency with the actual measured values. The outcome of this study can be used as a theoretical reference for the optimal design of RC beams in practical applications.

【 授权许可】

Unknown