Bringing Intelligence to the Edge for Structural Health Monitoring: The Case Study of the Z24 Bridge

Structural health monitoring (SHM) is key in civil engineering because of the importance and the aging of the infrastructure. We argue that applying leading-edge, data-driven methods of large-scale complex industrial systems may be beneficial, particularly for accuracy and responsiveness. A fundamental step concerns the identification of the best tools to extract meaningful information from the vibrational raw signals. To this end, we study the application of two convolutional neural network architectures that have emerged in the literature for efficient feature extraction from time series, namely WaveNet and MINImally RandOm Convolutional KErnel Transform (MiniRocket). The test bench is the Z24 bridge progressive damage test classification dataset. Results show that a model based on WaveNet reaches state-of-the-art performance, also reducing model size and computational complexity. WaveNet proves perfectly suited to interpret the bridge vibration waveforms directly in the time domain, without any specific preprocessing. On the other hand, MiniRocket excels for ease of configuration (only two hyperparameters are to be tweaked), overall training efficiency, and model size, lending itself as a valuable agile alternative (e.g., for rapid prototyping). Our main advancement is, thus, the identification and characterization of highly effective feature extraction methods, employable in different SHM tasks. We have assessed the performance of the models on two embedded platforms, proposing a smart sensor system where a local hub collects the signals from a constellation of inertial sensors and infers damage assessment onsite, allowing the bridge to self-assess its health state without resorting to connectivity nor cloud resources.