Open Access

ARTICLE

Condition Evaluation in Steel Truss Bridge with Fused Hilbert Transform, Spectral Kurtosis, and Bandpass Filter

Anshul Sharma^1,*, Pardeep Kumar¹, Hemant Kumar Vinayak², Suresh Kumar Walia³
1 National Institute of Technology, Hamirpur, 177005, India
2 Entrepreneurship Development and Industrial Coordination Department, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India
3 Himachal Pradesh Public Works Department, Kangra, 176001, India
* Corresponding Author: Anshul Sharma. Email:

Structural Durability & Health Monitoring 2021, 15(2), 139-165. https://doi.org/10.32604/sdhm.2021.012316

Received 25 June 2020; Accepted 26 February 2021; Issue published 03 June 2021

Abstract

This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges. The vibration response signals collected at bridges under three different vehicular speeds of 10 km/hr, 20 km/hr, and 30 km/hr are analyzed using statistical features such as kurtosis, magnitude of peak-to-peak, root mean square, crest factor as well as impulse factor in time domain, and Stockwell transform in the time-frequency domain. The considered statistical features except for kurtosis show uncertain behavior. The Stockwell transform showed low-resolution outcomes when the presence of noise in the recorded vibration responses. The elimination of noise and extraction of meaningful dynamic properties from the vibration responses is done by applying a new method which comes from the fusion of Hilbert transform with Spectral kurtosis and bandpass filtering. The outcomes obtained from Hilbert transform processed residual signals which are further filtered using bandpass filter show more robustness and accuracy in characterizing bridge modal frequencies from the noisy vibration responses. The proposed method produces a high-resolution frequency response which can unveil the joint discrepancy in the bridge structure.

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Keywords

Steel bridge; damage detection; stockwell transform; hilbert transform; spectral kurtosis; bandpass filter

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