Study on Noise Reduction of Bridge Strain Monitoring Data Based on Improved Wavelet Threshold Function
In order to reduce the impact of noise on bridge strain monitoring signals and further improve the reliability of strain monitoring data,an improved wavelet threshold function is proposed.By improving the soft and hard threshold compromise function,a fitness function based on the Sparrow Search Algorithm(SSA)is constructed,and the Sparrow Search Algorithm is used to optimize and determine the adjustment factor in the threshold function,which overcomes the discontinuity of hard threshold functions and the constant deviation of soft threshold functions,and also avoid the shortcomings of traditional trial and error methods to obtain adjustment factors.Then,a simulation signal is established by using the numerical simulation software,and the denoising effect evaluation indicators are used to compare the denoising effect with the other improved threshold functions to verify the denoising effect of this method.The research shows that the denoising method proposed for bridge strain monitoring signals has a better denoising effect and can better preserve the bridge strain information and basic features.The effectiveness of the improved denoising method is verified by comparing the strain monitoring data of the high-pier steel trestle bridge in Lanjiawan.
bridge health monitoringstrain datawavelet denoisingthreshold functionSparrow Search Algorithm
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