Enhanced processing of low signal-to-noise-ratio dynamic signals from pavement testing
| Autor: | Hao Han, Shijie Ma, Wenyang Han, Guiling Hu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal processing
Computer science business.industry Applied Mathematics Automated data processing 020208 electrical & electronic engineering 010401 analytical chemistry 02 engineering and technology Condensed Matter Physics 01 natural sciences Standard deviation 0104 chemical sciences Background noise Signal-to-noise ratio Software 0202 electrical engineering electronic engineering information engineering Performance prediction Electronic engineering Electrical and Electronic Engineering business Instrumentation Energy (signal processing) |
| Zdroj: | Measurement. 182:109697 |
| ISSN: | 0263-2241 |
| Popis: | The dynamic responses of a pavement structure to traffic loading is typically collected to predict pavement performance or in-service life. Researchers have primarily focused on using the maximum deviation from the peak strain responses to the conjoint valley responses for investigating the structural response of the pavement. However, the existing batch-processing program is not capable of identifying peak and valley responses from the low signal-to-noise-ratio (SNR) electronic signals. This paper explored the use of low-pass filtering and wavelet filtering to de-noise the strain signals for data pre-processing, and proposed the adaptive maximum energy ratio method and standard deviation method to isolate the foreground loading areas from the background noise of the strain responses. Finally, an automated data processing software was developed to precisely identify the peak and valley responses. This software provides a significant advancement to the current state of the practice in dynamic signal processing for pavement evaluation and performance prediction applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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