https://orcid.org/0000-0002-8144-2198
Abstract
Pavement monitoring has experienced significant advancements with the integration of fiber Bragg grating (FBG) sensors, offering real-time insights into the structural health of roads. This article tackles challenges associated with the analysis of long-term data collected by FBG sensors for pavement monitoring, addressing issues such as data volume, processing, and analysis. The primary objective is to establish a streamlined process for data analysis, demonstrated through a test track featuring a single fiber and a comprehensive data collection system. The study outlines a continuous monitoring framework, placing particular emphasis on data preprocessing and peak-counting–based segmentation to enable meaningful analysis. The presented preprocessing technique incorporates wavelet multiresolution analysis for the separation of load and temperature effects, which facilitates a detailed investigation of the load-induced strain. The application of peak-counting–based segmentation aims to address variability arising from varying traffic and loading weights by dividing FBG signals into windows with similar sampling size. The study utilizes waterfall plots and box plots for the comparative analysis and visualization of FBG sensor data. This approach provides insights into traffic distribution, loading conditions, and structural changes in the asphalt pavement over time. Overall, this research contributes to the efficient utilization of FBG sensors for pavement monitoring.
Issue Section:
Technical Papers
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