摘要
为研究混凝土梁应力的无损检测方法,提出一种基于深度残差收缩网络(deep residual shrinkage network,DRSN)的混凝土超声尾波应力识别算法(coda wave-DRSN,C-DRSN)。根据超声信号向量的高维特性,通过引入残差收缩块,使用软阈值函数和注意力机制,降低信号噪声对测量应力精度的干扰,实现自适应识别并提取信号中应力特征,提高了识别准确率;对提取的信号特征进行可视化分析,从而建立特征与应力的映射关系。为验证所提方法的应力识别能力,分别采集混凝土工字梁在三点弯曲和偏心受压荷载作用下的超声尾波信号。结果表明:两种加载模式下,识别率均可达99%,表明该方法在超声混凝土梁应力识别方面具有可行性,与尾波干涉法相比,所提方法的准确率更高。
In order to study a non-destructive testing method for concrete beam stress,a ultrasonic tail stress identification algorithm coda wave-deep residual shrinkage network(C-DRSN)based on deep residual shrinkage network(DRSN)was proposed.According to the high-dimensional characteristics of the tail wave signal vector,the interference of signal noise to the measurement stress accuracy was reduced by introducing residual contraction block,using soft threshold function and attention mechanism,and the adaptive recognition and extraction of stress features in the signal were realized,and the recognition accuracy was improved.The characteristics were visually analyzed,and the mapping relationship between the tail wave sign and the stress was established.In order to verify the model's ability of stress recognition,ultrasonic tail wave signals of concrete I-beams under three-point bending and eccentric compression loads were collected respectively.The results show that the recognition rate can reach 99%under both loading modes,indicating that the proposed method is feasible in the stress recognition of concrete beams,and the accuracy of the proposed method is higher than that of the tail wave interference method.
作者
郑罡
张智宇
于吉港
宋林正
ZHENG Gang;ZHANG Zhi-yu;YU Ji-gang;SONG Lin-zheng(College of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China;State Key Laboratory of Mountain Bridge and Tunnel Engineering(Chongqing Jiaotong University),Chongqing 400074,China)
出处
《科学技术与工程》
北大核心
2025年第16期6869-6878,共10页
Science Technology and Engineering
基金
国家自然科学基金(51978112)。
关键词
无损检测
超声尾波
应力识别
混凝土
深度残差收缩网络(DRSN)
nondestructive testing
ultrasonic coda wave
stress recognition
concrete
deep residual shrinkage network(DRSN)
