Damage Detection in Structural Health Monitoring with Spiking Neural Networks

Structural Health Monitoring (SHM) is an evolving research field involving internet-of-things and machine-learning technologies. Recent research in this field demonstrated the effectiveness of low-cost MEMS accelerometers to monitor the vibrations of buildings, and neural networks analyse generated data streams. In this work, we propose a novel SHM approach using Spiking Neural Networks (SNNs) applied to MEMS data to detect infrastructural damages in a motorway bridge. SNNs are brain-inspired network models that are promising as being more compact and potentially energy-efficient than traditional networks. In particular, Long Short-Term SNN (LSNN) are very effective in analysing streams of data, but they require a nontrivial learning process.We study the feasibility of LSNNs for SHM, and we compare their accuracy with alternative artificial neural network (ANN) models. We demonstrate that SNN can effectively discriminate whether a structure is in a healthy or damaged condition with an accuracy level similar to ANN. To this purpose, we exploited a state-of-the-art fast training algorithm that approximates the Back Propagation Through Time (BPTT). We also show that inference times are compliant with real-time SHM requirements.