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

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Pubblicato in:Smart and Resilient Transportation vol. 6, no. 2 (2024), p. 115-129
Autore principale: Xue Xin
Altri autori: Jiao, Yuepeng, Zhang, Yunfeng, Liang, Ming, Yao, Zhanyong
Pubblicazione:
Emerald Group Publishing Limited
Soggetti:
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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Abstract: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
Fonte:Engineering Database