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• This study successfully employed discrete wavelet transform (DWT) to de-noise fiber Bragg grating (FBG) sensor signals in asphalt pavement, which is essential in precise measurements in structural health monitoring (SHM) applications. • Through extensive parameter evaluation, the study identified optimal mother wavelets and thresholding techniques for enhancing the signal-to-noise ratio (SNR) while preserving signal amplitude. • An optimal decomposition level was established for FBG signal denoising, striking a balance between noise reduction and computational efficiency. • Comparative analysis with other filtering methods demonstrated that DWT-based denoising significantly outperformed these methods. In recent years, the Fiber Bragg Grating (FBG) sensor technology has been increasingly utilized as an optical measurement system in various engineering applications, particularly for structural health monitoring (SHM) purposes. This trend can be attributed to the inherent benefits of FBG sensors, such as their small size, immunity to electromagnetic interference, resistance to corrosion, and high accuracy and sensitivity. Various factors cause noise in the FBG sensor signal, which has a significant effect on measurement precision. As a result, de-noising plays an important role in the use of FBG sensor systems. In this study, strain data collected from FBG sensors embedded in a road section were used to evaluate the performance of discretized wavelet transform (DWT) for denoising FBG signals. The presence of noise poses a significant challenge in accurately measuring low-amplitude strains and light loads. To address this issue, various approaches have been investigated, including the selection of appropriate mother wavelets, levels of decomposition, thresholding functions, and thresholding selection approaches, with the aim of identifying the optimal parameters for effective denoising. The results show that FBG signals could be denoised successfully and low amplitude strains appeared completely without any loss of valuable data. [ABSTRACT FROM AUTHOR]

• Introduction of an advanced pavement monitoring system using fiber Bragg grating (FBG) sensors. • Successful application of multi-sensor data fusion techniques to decrease variability in FBG data. • Implementation of Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for abnormal data pattern detection. • Focus on anomaly detection, interpretation, and severity assessment in strain data, highlighting contextual information. Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, pre-processing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The presented approach enhances accuracy by detecting anomalies at any strain level, computational time using multi-sensor data fusion, and reliability of structural health assessment through anomaly evaluation. It also provides a framework for proactive decision-making, ultimately improving the longevity and structural integrity of critical infrastructure. [ABSTRACT FROM AUTHOR]