Research on noise reduction and data mining techniques for pavement dynamic response signals
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| Publicado en: | Smart and Resilient Transportation vol. 6, no. 2 (2024), p. 115-129 |
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| Otros Autores: | , , , |
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Emerald Group Publishing Limited
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3124178357 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2632-0495 | ||
| 024 | 7 | |a 10.1108/SRT-11-2023-0013 |2 doi | |
| 035 | |a 3124178357 | ||
| 045 | 2 | |b d20240701 |b d20241231 | |
| 100 | 1 | |a Xue Xin |u School of Civil Engineering and Architecture, University of Jinan, Jinan, China | |
| 245 | 1 | |a Research on noise reduction and data mining techniques for pavement dynamic response signals | |
| 260 | |b Emerald Group Publishing Limited |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a China | |
| 653 | |a Load | ||
| 653 | |a Outliers (statistics) | ||
| 653 | |a Humidity | ||
| 653 | |a Dynamic response | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Data mining | ||
| 653 | |a Calibration | ||
| 653 | |a Signal processing | ||
| 653 | |a Frequency ranges | ||
| 653 | |a Decomposition | ||
| 653 | |a Automobiles | ||
| 653 | |a Asphalt pavements | ||
| 653 | |a Design specifications | ||
| 653 | |a Asphalt | ||
| 653 | |a Automation | ||
| 653 | |a Stress analysis | ||
| 653 | |a Noise reduction | ||
| 653 | |a Signal reflection | ||
| 653 | |a Strain gauges | ||
| 653 | |a Data analysis | ||
| 653 | |a Spatial data | ||
| 653 | |a Infrastructure | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Clustering | ||
| 653 | |a Time-frequency analysis | ||
| 653 | |a Sensors | ||
| 653 | |a Effectiveness | ||
| 653 | |a Heavy duty trucks | ||
| 653 | |a Algorithms | ||
| 653 | |a Information industry | ||
| 653 | |a Engineering | ||
| 653 | |a Real time | ||
| 653 | |a Frequency analysis | ||
| 653 | |a Low pass filters | ||
| 653 | |a Roads & highways | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Jiao, Yuepeng |u School of Qilu Transportation, Shandong University, Jinan, China | |
| 700 | 1 | |a Zhang, Yunfeng |u School of Qilu Transportation, Shandong University, Jinan, China | |
| 700 | 1 | |a Liang, Ming |u School of Qilu Transportation, Shandong University, Jinan, China | |
| 700 | 1 | |a Yao, Zhanyong |u School of Qilu Transportation, Shandong University, Jinan, China | |
| 773 | 0 | |t Smart and Resilient Transportation |g vol. 6, no. 2 (2024), p. 115-129 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3124178357/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3124178357/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3124178357/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |