Wavelet Denoising of TSD Deflection Slope Measurements for Improved Pavement Structural Evaluation

Abstract

Continuous deflection devices (CDDs) can safely measure pavement deflection (or other related properties) while traveling at highway speed, which reduces traffic disruption. CDD measurements are contaminated with relatively high noise levels compared to stop‐and‐go devices such as the Falling Weight Deflectometer. In this article, we use wavelet transform denoising to remove the noise and estimate the true deflection slope measurements obtained from the Traffic Speed Deflectometer. Results show that failure to denoise deflection slope measurements can lead to calculated Effective Structural Number values that are highly variable (unstable). Attempting to filter these highly variable measurements can lead to erroneous results. We also use wavelet transform denoising to identify localized weak spots such as those that are caused by pavement reflection cracking. Identifying weak spots with wavelets is possible because wavelets are spatially adaptive to local features. In contrast, a linear filter is not capable of adapting to local features.

Computer-Aided Diagnosis of Depression Using EEG Signals

“…Puthankattil et al [43] extracted Relative Wavelet Energy (RWE) parameters from the Discrete Wavelet Transform (DWT) [73,74,75,76,77,78] coefficients and used ANN [79,80,81] to classify the EEG signal into normal and depressed classes. They claim an overall classification accuracy of 98.11% in discriminating the two classes.…”

Pavement condition measurement at high velocity using a TSD

“…Continuous Deflection Devices (CDDs) have been introduced to solve this problem (Katicha et al, 2014a). CDDs measure deflection at driving speed, avoiding the safety implications of stationary measurements (Katicha et al, 2014b). The two main CDDs are the Rolling Weight Deflectometer (RWD) and the Traffic Speed Deflectometer (TSD).…”

A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals

“…Many physical signals include significant noise and closely-spaced frequencies that cannot be effectively analyzed using these methods [12]. Further, real-life time series signals such as biomedical signals [38,43,30,48,8,2,[9][10][11]6,7], or vibration signals obtained from civil structure subjected to dynamic excitations [33,14,26,13,32,27] include nonlinear and non-stationary properties that cannot be adequately analyzed using these methods. In order to overcome these limitations, time-frequency representation (TFR) has become a good alternative to analyze nonlinear and non-stationary signals since a TFR can provide information about the frequencies contained in signal over time [40].…”