基于WPT-SVD和GA-BPNN的混凝土结构损伤识别

针对基于压电波动法检测混凝土结构损伤离散性大的问题,提出基于小波包-奇异值分解(WPT-SVD)和遗传算法优化的BP神经网络(GA-BPNN)模型的损伤识别方法.该方法深度挖掘结构开裂损伤信号时频域变化特征,构建信号特征与损伤的对应关系,可以有效地识别结构损伤位置和程度.在混凝土结构表面粘贴压电传感器测得损伤信号,对损伤信号进行WPT分解,以获得多维时频矩阵.采用SVD对不同损伤状态下的时频矩阵进行降维,构建具有较高损伤敏感性的特征向量.建立具有自适应学习能力的GA-BPNN,实现结构的损伤识别.试验验证表明,压电信号奇异值可以作为损伤特征参量,主要频段的奇异值随着损伤的发展而下降,归一化奇异值向量距与损伤情况呈现3阶段对应关系. GA-BPNN较BPNN能够更好地表征信号特征与损伤间的关联性,识别结果更加稳定且精确度高,结构损伤位置和程度的识别精确度分别达到95.19%和94.47%.

Damage identification of concrete structure based on WPT-SVD and GA-BPNN

A damage detection method based on wavelet packet-singular value decomposition (WPT-SVD) and BP neural network model optimized by genetic algorithm (GA-BPNN) was proposed aiming at the problem of damage discretization of concrete structures based on piezoelectric wave method. The damage characteristics of signal were deeply explored for structural cracking in time-frequency domain. The relationship between the signal characteristics and the damage condition was built, which can identify the damage location and the damage degree of structures. The damage signal was measured by surface-mounted piezoceramic transducer on the concrete structures. The WPT was employed to obtain the multi-dimensional time-frequency matrix. Then SVD was used to reduce the dimension of time-frequency matrix under different damage states and construct feature vectors with high damage sensitivity. The GA-BPNN model with auto-adaptive learning was established, and the identification of structural damage was realized. The experimental results show that the singular value of piezoelectric signal can be used as the characteristic parameter of damage. The singular value of the main spectrum decreases with the development of damage. There is a correspondence of three stages between the normalized singular value vector distance and the damage. The GA-BPNN better fitted the correlation between the damage signal characteristics and the damage compared with the BPNN model. The identification was more stable, and the accuracy was significantly improved. The identification accuracy of damage location and damage degree of concrete structures were 95.19% and 94.47%, respectively.