Scalable road infrastructure monitoring using embedded fiber Bragg grating sensors based on wavelet scattering-long short-term memory autoencoder

Abstract

Managing and extracting insights from the large volumes of data generated by optical fiber sensor networks is a major challenge. This paper presents an intelligent, scalable framework for real-time road health monitoring using fiber Bragg grating (FBG) sensor data. The proposed framework reduces reliance on manual data handling and cuts storage needs by over 99 % by constructing a compact health indicator (HI). Data preprocessing and fusion reduce volume and variability, while a wavelet scattering network (WSN) extracts damage-sensitive features that are encoded via a long short-term memory (LSTM) autoencoder to represent the health state. Temperature data is integrated to distinguish structural damage from environmental effects. The approach is evaluated through laboratory fatigue tests and synthetic damage data generated from healthy-state field measurements. Results demonstrate accurate, efficient monitoring with potential for edge deployment, enabling low-cost, real-time, long-term structural health management and representing a significant step toward automated, resource-efficient infrastructure maintenance.