Deifalla Ahmed, Salem Nermin M
Engineering and Construction Management Department, Future University in Egypt (FUE), Cairo 11835, Egypt.
Electrical Engineering Department, Future University in Egypt (FUE), Cairo 11835, Egypt.
Polymers (Basel). 2022 Apr 29;14(9):1824. doi: 10.3390/polym14091824.
Strengthening of reinforced concrete (RC) beams subjected to significant torsion is an ongoing area of research. In addition, fiber-reinforced polymer (FRP) is the most popular choice as a strengthening material due to its superior properties. Moreover, machine learning models have successfully modeled complex behavior affected by many parameters. This study will introduce a machine learning model for calculating the ultimate torsion strength of concrete beams strengthened using externally bonded (EB) FRP. An experimental dataset from published literature was collected. Available models were outlined. Several machine learning models were developed and evaluated. The best model was the wide neural network, which had the most accurate results with a coefficient of determination, root mean square error, mean average error, an average safety factor, and coefficient of variation values of 0.93, 1.66, 0.98, 1.11, and 45%. It was selected and further compared with the models from the existing literature. The model showed an improved agreement and consistency with the experimental results compared to the available models from the literature. In addition, the effect of each parameter on the strength was identified and discussed. The most dominant input parameter is effective depth, followed by FRP-reinforcement ratio and strengthening scheme, while fiber orientation has proven to have the least effect on the prediction output accuracy.
对承受显著扭矩的钢筋混凝土（RC）梁进行加固是一个正在进行研究的领域。此外，纤维增强聚合物（FRP）因其优越的性能而成为最受欢迎的加固材料选择。而且，机器学习模型已成功对受许多参数影响的复杂行为进行建模。本研究将介绍一种用于计算采用外部粘贴（EB）FRP加固的混凝土梁极限抗扭强度的机器学习模型。收集了来自已发表文献的实验数据集。概述了现有的模型。开发并评估了几种机器学习模型。最佳模型是宽神经网络，其结果最为准确，决定系数、均方根误差、平均绝对误差、平均安全系数和变异系数值分别为0.93、1.66、0.98、1.11和45%。该模型被选中并与现有文献中的模型进行进一步比较。与文献中的现有模型相比，该模型与实验结果的一致性和吻合度更高。此外，还识别并讨论了各参数对强度的影响。最主要的输入参数是有效深度，其次是FRP配筋率和加固方案，而纤维方向已证明对预测输出精度的影响最小。
