基于小波分析和深度学习的钢-混组合梁损伤识别

Damage Identification of Steel-Concrete Composite Girders Based on Wavelet Analysis and Deep Learning

为提高钢-混组合梁损伤识别的准确率,提出一种基于小波分析和深度学习的损伤识别方法。设计并制作6种不同损伤的工字钢-混凝土组合梁,通过钢球自由落体冲击组合梁表面,采用光纤光栅应变传感器获取不同损伤组合梁的应变信号;采用haar、sym2、sym4、db2和db4五种小波基函数对采集到的应变信号进行降噪处理;搭建ResNet-18、ResNet-50、ResNet-101、InceptionV3、InceptionResNetV2、MobileNetV2六个深度学习模型对降噪前、后的应变信号进行训练和预测,分析得出预测准确率最高的模型,以实现对组合梁损伤的分类和定位。结果表明:haar小波基函数降噪效果优于其它函数;ResNet-50模型预测准确率均高于其它模型,降噪前、后的预测准确率平均值分别为96.73%、97.91%,小波降噪使ResNet-50模型预测准确率提高了1.18%;远离损伤位置ResNet-50模型预测准确率平均值为96.82%,仍有较高的准确率。该方法可为钢-混组合梁的运营维护以及损伤评估提供参考。

This paper presents a damage identification method based on wavelet analysis and deep learning,which provides improved damage identification accuracy for steel-concrete composite girders.Six composite girder specimens consisting of steel I girders and concrete slabs and revealing different levels of damages were prepared,the surfaces of these specimens were impacted by the freely-falling steel balls to make damages,and the strain signals of the specimens with different levels of damages were collected using fiber bragg grating strain sensors.The collected strain signals were denoised by five wavelet basis functions,including haar,sym2,sym4,db2 and db4.Additionally,six deep learning models(ResNet-18,ResNet-50,ResNet-101,InceptionV3,InceptionResNetV2,and MobileNetV2)were built to train and predict the strain signals before and after the denoising,aiming to select the model with the highest prediction accuracy,and finally to achieve the categorization and localization of damages in the specimens.It is concluded that the denoising effect of haar is superior to the other four functions,and the prediction accuracy of ResNet-50 is higher than the other five models,showing the average prediction accuracies of 96.73%and 97.91%before and after denoising,respectively,and the wavelet denoising enables the prediction accuracy of the ResNet-50 to be improved by 1.18%.The ResNet-50 model reveals a prediction accuracy of 96.82%even afar damages.This method serves as an alternative for maintenance and damage prediction of the steel-concrete composite girders.