Experimental study on damage identification of bridge structures based on wavelet packet decomposition and optimized BP neural network
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维普
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针对单一损伤指标对桥梁损伤识别研究的局限性,根据小波包变换基本原理和神经网络原理,构造小波包能量相对变化率(RES)作为损伤识别指标并建立优化反向传播(back propagation,BP)神经网络模型,提出基于小波包分析和优化BP神经网络的桥梁结构损伤位置和损伤程度识别方法,并对该方法进行试验验证,探讨了信号噪音和车速等因素对试验结果的影响.结果表明,以多个损伤工况作为遗传算法优化反向传播(genetic algorithm-back propagation,GA-BP)和遗传算法和模拟退火优化反向传播(genetic algorithm and simulated annealing-back propagation,GASA-BP)神经网络的训练集,两种神经网络模型在数值模拟工况和试验工况中均展现出了良好的识别能力,在数值模拟工况下,GASA-BP相较于GA-BP神经网络的最大平均误差提高了92.61%;在试验工况下,GASA-BP相较于GA-BP神经网络的最大平均误差提高了67.66%,由此可见GASA-BP神经网络具有更好的识别精度和较好的鲁棒性.该方法仅需少量的传感器即可对桥梁结构损伤位置实现精准定位.
Aiming at the limitations of a single damage indicator for bridge damage identification research,according to the basic principles of wavelet packet transform and neural network principles,the relative energy rate of wavelet packet energy spectrum(RES)is constructed as a damage identification indicator and an optimised back propagation(BP)neural network model is established.BP neural network model,put forward based on wavelet packet analysis and optimisation of BP neural network bridge structure damage location and damage degree identification method,and the method of experimental validation,to explore the signal noise and speed and other factors on the test results.The results show that multiple damage conditions are used as the basis for genetic algorithm-back propagation(GA-BP)and genetic algorithm and simulated annealing-back propagation(GASA-BP).GA-BP and GASA-BP neural networks,the two neural network models show good recognition ability in both numerical simulation and experimental conditions,and in numerical simulation,the maximum average error of GASA-BP is 92.61%higher than that of the GA-BP neural network,and in experimental conditions,the maximum average error of GASA-BP is 92.61%higher than that of the GA-BP neural network.In the experimental condition,the maximum average error of GASA-BP is 67.66%higher than that of GA-BP neural network,which shows that GASA-BP neural network has better recognition accuracy and better robustness.The method requires only a small number of sensors to accurately locate the damage location of the bridge structure.
bridge structure damage identificationrelative change rate of wavelet packet energyBP neural networkpi-lot study
维普
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