Deep Learning-Based Structural Health Monitoring: A Multi-Scale Neural Network Approach for Real-Time Damage Detection in Composite Materials

For the structural health monitoring of composite materials, data analysis technology must be very sophisticated, capable of detecting fault patterns that are multi-level and complicated. A comprehensive deep learning paradigm was designed for real-time damage detection in this paper. It used advanced neural network architectures with hierarchies and then trained the model on an extensive dataset until it was ready to be published. In other words, the whole process began from scratch. We adopt Cartesian neural network architectures at different levels of scale: from micro- to macro. This system processes damage in composite materials logistically speaking. Through this hierarchical deep learning approach, even if the neural network system is unable to recognize a certain type of spatial damage pattern, it can still be recognized at an earlier stage. The method proposed herein integrates convolutional neural networks with recurrent neural networks and attention mechanisms to effectively capture spatial temporal patterns of damage. Our deep learning method calculates 94.2% damage localization accuracy under carbon fiber reinforced polymer test specimens and decreases false positive rates by 67% compared with traditional signal processing methodologies. This framework has established a new benchmark in industry practice and offers a suite of user-friendly tools with excellent performance repetitive in diverse situations but highly efficient from the computational perspective.