A Dynamic Bayesian Deep Learning Approach to Structural Health Monitoring

Structural Health Monitoring (SHM) is crucial for ensuring the safety and longevity of critical infrastructures. Traditional methods for crack detection and damage assessment are often labor intensive and time consuming, highlighting the need for advanced technologies to enhance efficiency and accuracy. This paper introduces a novel approach that integrates continual learning frameworks within multi-layer recurrent neural networks to improve parameter estimation in SHM applications. By deploying the Generalized Expectation Maximization algorithm, we address challenges associated with dynamic operational environments and inherent uncertainties in sensor data. Our methodology enables real-time monitoring and adaptive learning, allowing the model to continuously refine its predictions based on new data. We demonstrate its effectiveness in automating structural anomaly detection from accelerometer readings, significantly enhancing the reliability of damage assessment. First results indicate that our framework not only improves crack detection accuracy, but also facilitates timely interventions, contributing to a more sustainable infrastructure management.