A de-noising algorithm for bridge cable force monitoring data based on mathematical morphology

• Published in: Advances in bridge engineering Vol. 4; no. 1; pp. 28 - 12
• Main Authors: Deng, Chao, Li, Yi, Zou, Wei, Ren, Yuan, Peng, Ying, Han, Zhuo’er
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Bridge monitoring data; Cable force; Civil Engineering; Construction; Data mining; De-nosing; Design; Electromagnetic wave filters; Engineering; Large Scale Bridges: Design; Low pass filters; Maintenance; Management; Mathematical analysis; Mathematical morphology; Monitoring; Morphological filters; Morphology; Noise; Original Innovation; Signal processing; Signal to noise ratio; Spheres; Straight lines; White noise; White noise; Bridge monitoring data; Cable force; Mathematical morphology; De-nosing
• ISSN: 2662-5407; 2662-5407
• DOI: 10.1186/s43251-023-00109-x

A mathematical morphological filter-based de-noising method is developed in this study for bridge cable force monitoring data. Structure elements, one of the most important parameters in the mathematical morphology, dominate de-noising effects. The de-noising effects subject to single structure element and multi-structure element filters are discussed based on the simulation signals. The results indicate that the de-noising effects by using the spherical structure element are better than using the straight line or rhombic structure element. Moreover, the multi-structure element filter outperforms the single one. Through simulation analysis, the de-noising performance of the low-pass filter, wavelet filter and morphological filter is compared. The results show that the performance of the wavelet and morphological filters is better than that of the low-pass filter. For low signal-to-noise signals, the performance of the wavelet filter is superior. With the increase of signal-to-noise ratio, the morphological filters show more advantages. Taking the cable force monitoring data of the 3rd Nanjing Yangtze River Bridge as an example, the de-noising performance of the wavelet and morphological filters is discussed. The results show that both the wavelet filters and morphological filters have satisfactory de-noising effects. The mathematical morphology method can provide an optional and effective de-nosing choice, which enriches the means of de-noising for bridge monitoring data.