Noise Reduction for Improvement of Ultrasonic Monitoring Using Coda Wave Interferometry on a Real Bridge

Abstract

Reinforced concrete bridges are iconic parts of modern infrastructure. They are designed for a minimum service life of 100 years. However, environmental factors and/or inappropriate use might cause overload and accelerate the deterioration of bridges. In extreme cases, bridges could collapse when necessary maintenance lacks. Thus, the permanent monitoring for structure health assessment has been proposed, which is the aim of structural health monitoring (SHM). Studies in laboratories have shown that ultrasonic (US) coda wave interferometry (CWI) using diffuse waves has high sensitivity and reliability to detect subtle changes in concrete structures. The creation of micro-cracks might be recognized at an early stage. Moreover, large-volume structures can be monitored with a relatively small number of US transducers. However, it is still a challenge to implement the CWI method in real SHM practical applications in an outdoor environment because of the complex external factors, such as various noise sources that interfere with the recorded signals. In this paper, monitoring data from a 36-m long bridge girder in Gliwice, Poland, instrumented with embedded US transducers, thermistors, and vibrating wire strain gauges, is presented. Noise estimation and reduction methods are discussed, and the influence of traffic, as well as temperature variation, are studied. As a result, the relative velocity variation of US waves following the temperature change with a very high precision of $$10^{-4} \%$$ 10 - 4 % is shown, and a good bridge health condition is inferred. The influence of lightweight real traffic is negligible. The study verified the feasibility of the implementation of the CWI method on real bridge structures.

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking

“…A fixed threshold T = σ • 2 log(N) (σ denotes data variance) was used in this study. (6) For the remaining IMFs, denoised componentsĉ i (t) can be obtained by repeating steps (3)(4)(5), and the final denoised signalx(t) is obtained by stacking the denoised componentsĉ i (t), as follows:…”

“…Consequently, to improve signal detection, classification, and phase arrival picking, it is necessary to remove unwanted noise. Currently, signal denoising is widely used in the fields of medical signals [ 1 ], speech signals [ 2 ], mechanical diagnosis [ 3 ], structural health monitoring [ 4 , 5 , 6 ], and seismic monitoring networks [ 7 , 8 , 9 ]; however, this study focused specifically on microseismic (MS) signal denoising.…”

“…Subsequently, SVD was combined with the Hankel matrix to denoise a signal [ 23 ], where the Hankel matrix can expand a signal into a high-dimensional matrix and is very beneficial for feature extraction. Recently, coda wave interferometry (CWI) has been successfully applied to monitor stress distribution and damage detection in engineering [ 4 , 5 , 6 ], where wavelet transformation [ 5 ], normal band-pass filter, narrow band-pass filter, and double-notch filter [ 6 ] were used to extract useful signal features. These techniques provided a good research direction for noise reduction and damage detection.…”

Damage Detection in Multiple RC Structures Based on Embedded Ultrasonic Sensors and Wavelet Transform

“…Therefore, this low wavelet scaling part of the result can be avoidable. The authors [28,43] also used a band pass filter to remove the high-frequency component from the diffuse ultrasonic signals. However, one can see in Figure 10a that the NDWT coefficient started to lose its linearity after 42 kN, which indicates the cracks forming and propagation (marked with the blue arrow).…”

“…Often, it can be seen that most of the signal processing approaches applied for damage detection have some limitations in detecting damage in real structures due to the influence of noises and temperature [27]. In [28], the authors presented a noise reduction method for CODA wave analysis in bridge monitoring using the double-notch filter. The designing of the extra filter is always challenging and needs more processing time and power.…”