Study on Damage Identification of Beam Bridge Based on Characteristic Curvature and Improved Wavelet Threshold De-Noising Algorithm

Abstract

With the point cloud data of box girder obtained by the theory of structure from motion (SFM) algorithm chosen as the research background, a damage identification method based on characteristic curvature and improved wavelet threshold de-noising algorithm is presented. Firstly, the static load test is carried out for the full-scale box girder model, and after the cracking damage, the discrete point cloud data on the surface of the box girder are obtained through the SFM theory. According to the basic hypothesis, deformation is mainly caused by the bending moment, and micro damage has no effect on stress redistribution. Therefore, a conclusion can be made that the curvature is sensitive to the structural damage. Then, a characteristic curvature damage identification method, which is based on point cloud chord length, is used to solve the characteristic curvature of the specified section of the box girder. It is found that there are a large number of point cloud noise signals in the characteristic curvature, and on this basis, a new wavelet de-noising method established upon the threshold function is proposed. Finally, the damage index revealed from the characteristic curvature after de-noising in the specified section is compared with the actual damage location of the box girder. The results show that by combining the characteristic curvature algorithm based on the chord length of scattered point cloud with the improved wavelet threshold de-noising function, the structural surface crack damage based on spatial point cloud data can be identified. Especially, in consideration of noise interference, the improved wavelet threshold de-noising function proposed in this paper can effectively suppress the high frequency noise signal in the characteristic curvature and preserve 506 the low-frequency signal of damage in the damage area. Thus, the accuracy and sensitivity of the damage identification method based on the characteristic curvature are guaranteed. This method has the potential to be applied to structural health monitoring, since it can provide a new technical method for the early warning of the beam structure bridge.

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

“…The authors of that study also investigated the different damage cases, sensor locations, and external load velocities and magnitudes. Chu et al [86] studied the damage detection of beam bridges using the characteristic curvature and adopted the wavelet threshold method to reduce the noise interference and ensure the accuracy of the damage assessment. Aiming at the identification of bridge mode shapes in the field of structural health monitoring (SHM), Jian et al [87] proposed a wavelet analysis method to directly obtain mode shapes based on the dynamic responses of a tractor trailer vehicle model, and a wavelet denoising algorithm was utilized to enhance the accuracy.…”

An improved infrared image processing method based on adaptive threshold denoising

“…The fast algorithm based on the binary wavelet transform [18]-à trous algorithm [19]-not only has the invariance of translation but also makes the representation of the image in the domain of binary wavelet transform very redundant, and the disturbance of partial coefficients will not lead to the serious distortion of the reconstructed image. In addition, for the Gibbs visual distortion in a denoised image caused by the wavelet threshold denoising method using orthogonal wavelet transform, many researchers have made improvements such as literature [20][21][22][23]. The results show that translation invariance is an important property of effectively suppressing Gibbs phenomenon and improving denoising effect.…”