【摘要】 电力基础设施工程中，钢筋混凝土的质量关系到工程的安全性和耐久性，而锈蚀等因素会导致钢筋的实际有效直径减小，直接影响其抗震抗压性能。提出了一种基于OPTINET-BP的混凝土钢筋直径检测方法。首先，对于不同钢筋直径的混凝土试件，采集涡流检测线圈在1 kHz脉冲激励下的电压响应，对其进行Savitzky-Golay滤波消除噪声；然后，对检测线圈电压进行指数函数拟合，提取电压信号特征量；最后，对电压信号特征量进行预处理得到数据样本，分别使用BP网络和OPTINET-BP网络进行训练，进而预测钢筋的直径。通过对比不同规格的钢筋混凝土试件的试验结果，发现改进后的模型均方误差为0.990 18，相较于传统BP模型降低了约65%。试验结果表明，改进后的模型能显著提高钢筋直径检测的准确性，具有较高的实用价值。更多还原

【Abstract】 In power infrastructure projects, the quality of reinforced concrete is related to the safety and durability of the project, and the actual effective diameter of steel bars will be reduced due to factors such as corrosion, which directly affects the seismic and compressive properties of reinforced concrete. A method for detecting the diameter of concrete steel bars based on OPTINET-BP was proposed. Firstly, for concrete specimens with different steel bar diameters, the voltage response of the eddy current detection coil under 1 kHz pulse excitation was collected, and Savitzky-Golay filtering was performed to eliminate noise. Secondly, the exponential function fitting of the detection coil voltage was carried out to extract the characteristic quantity of the voltage signal. Finally, the voltage signal characteristics were preprocessed to obtain data samples, and BP network and OPTINET-BP network were used for training to predict the diameter of steel bars. By comparing the experimental results of reinforced concrete samples with different specifications, it was found that the mean square error of the improved model was 0.990 18, which was about 65 % lower than that of the traditional BP model. The experimental results showed that the improved model could significantly improve the accuracy of steel bar diameter detection and had high practical value.更多还原