Singh Rohan, Arora Harish Chandra, Bahrami Alireza, Kumar Aman, Kapoor Nishant Raj, Kumar Krishna, Rai Hardeep Singh
Department of Civil Engineering, Guru Nanak Dev Engineering College (GNDEC), Ludhiana 141006, India.
Department of Structural Engineering, CSIR-Central Building Research Institute, Roorkee 247667, India.
Materials (Basel). 2022 Nov 22;15(23):8295. doi: 10.3390/ma15238295.
The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.
混凝土与锈蚀钢筋之间的粘结强度是影响钢筋混凝土（RC）结构极限承载能力的主要因素之一。因此，准确预测粘结强度已成为RC结构安全测量的一个重要参数。然而，分析模型不足以估计粘结强度，因为它们是基于各种假设和有限数据集建立的。名为人工神经网络（ANN）和支持向量机（SVM）的机器学习（ML）技术已被用于估计混凝土与锈蚀钢筋之间的粘结强度。本研究中考虑的输入参数有试件表面积、混凝土保护层厚度、钢筋类型、钢筋屈服强度、混凝土抗压强度、钢筋直径、粘结长度、水灰比和钢筋锈蚀程度。这些参数被用于建立ANN和SVM模型。已将所开发的ANN和SVM模型的可靠性与二十个分析模型进行了比较。此外，分析结果表明，与分析模型相比，ANN和SVM模型的精度和效率更高。雷达图和泰勒图也被用于展示最佳拟合模型的图形表示。与SVM模型相比，所提出的ANN模型具有最佳的精度和可靠性，相关系数为0.99，平均绝对误差为1.091MPa，均方根误差为1.495MPa。研究人员和设计师可以应用所开发的ANN模型来精确估计钢与混凝土之间的粘结强度。
