Edge/Cloud hybrid architecture for time-domain Structural Health Monitoring transfer learning

Abstract

Current Structural Health Monitoring (SHM) systems remain constrained by their reliance on handcrafted feature extraction and centralized cloud processing, limiting their real-time performance, scalability, and deployment on resource-constrained infrastructure. This study seeks to overcome these limitations by introducing a hybrid edge/cloud SHM framework that facilitates low-power, real-time anomaly detection directly on embedded devices. We introduce a temporal transfer learning approach based on the WaveNet and Recurrent Neural Network architectures that operates on raw time-series data and eliminates the need for statistical or frequency-domain feature engineering. The models were trained and validated using real-world datasets from the Z24 (Switzerland) and S101 (Austria) bridges and deployed on edge hardware (Raspberry Pi 5 and LattePanda Mu, Intel® N100) for on-device inferences. The framework implements a hierarchical inference strategy in which edge devices perform continuous, low-latency anomaly detection, whereas cloud resources are selectively engaged for the high-precision analysis of critical events. This design reduces bandwidth requirements and enhances system autonomy without compromising accuracy. The results demonstrate that the proposed approach achieves a high anomaly detection performance in time-domain transfer learning while maintaining an efficient edge deployment. This study provides a validated pathway toward scalable, real-time, and feature-free SHM systems for deployment in operational bridge networks, supporting continuous monitoring, early damage detection, and maintenance decision-making in the future.