Lyu Zhaoqiu, Yu Yang, Samali Bijan, Rashidi Maria, Mohammadi Masoud, Nguyen Thuc N, Nguyen Andy
School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Centre for Infrastructure Engineering, Western Sydney University, Penrith, NSW 2751, Australia.
Materials (Basel). 2022 Feb 16;15(4):1477. doi: 10.3390/ma15041477.
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 (R) 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.
由于样本量在预测钢筋混凝土（RC）梁抗扭强度方面存在局限性，本文旨在探讨在小样本范围内采用一种结合遗传算法（GA）和神经网络（NN）优势的带有K折交叉验证的新型机器学习方法来预测RC梁抗扭强度的可行性。本研究不仅利用反向传播（BP）神经网络和遗传算法-反向传播（GA-BP）神经网络在预测RC梁抗扭强度方面的应用，还使用K折交叉验证来研究神经网络参数优化，包括连接权重和阈值。均方根误差（RMSE）、平均绝对误差（MAE）、均方误差（MSE）、平均绝对百分比误差（MAPE）和相关系数（R）是用于评估训练模型性能的评估指标。为详细说明所提出的网络模型在预测RC梁抗扭强度方面的优越性，通过将所提出的模型与现有设计规范中三个常用的经验公式进行比较，开展了参数研究。本研究的比较结果表明，BP神经网络的性能与设计规范高度相似；然而，其准确性不足。通过K折交叉验证和GA改进权重和阈值后，BP神经网络的预测结果与实际测量值显示出更高的一致性。本研究结果可为实际应用中RC梁的优化设计提供理论参考。
