Research on noise reduction and data mining techniques for pavement dynamic response signals

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Dades bibliogràfiques
Publicat a:	Smart and Resilient Transportation vol. 6, no. 2 (2024), p. 115-129
Autor principal:	Xue Xin
Altres autors:	Jiao, Yuepeng, Zhang, Yunfeng, Liang, Ming, Yao, Zhanyong
Publicat:	Emerald Group Publishing Limited
Matèries:	China Load Outliers (statistics) Humidity Dynamic response Wavelet transforms Data mining Calibration Signal processing Frequency ranges Decomposition Automobiles Asphalt pavements Design specifications Asphalt Automation Stress analysis Noise reduction Signal reflection Strain gauges Data analysis Spatial data Infrastructure Fourier transforms Clustering Time-frequency analysis Sensors Effectiveness Heavy duty trucks Algorithms Information industry Engineering Real time Frequency analysis Low pass filters Roads & highways Environmental
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Descripció
Resum:	PurposeThis study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.Design/methodology/approachThe paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.FindingsThe study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.Originality/valueThe authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.
ISSN:	2632-0495
DOI:	10.1108/SRT-11-2023-0013
Font:	Engineering Database