A Cepstrum-Informed neural network for Vibration-Based structural damage assessment

Abstract

Data-driven methods for vibration-based Structural Health Monitoring (SHM) have gained significant popularity for their straightforward modeling process and real-time tracking capabilities. However, developing complex models such as deep neural networks can pose challenges, including limited interpretability and substantial computational demands, due to the large number of parameters and deep layer stacking. This study introduces a novel Cepstrum-Informed Attention-Based Network (CIABN) developed to model power cepstral coefficients of structural acceleration responses, guided by cepstrum-based physical properties to facilitate efficient structural damage assessment. The CIABN integrates three key components: a unique input–output mapping based on weighted cepstral coefficients, a novel cepstral positional encoding mechanism, and a multi-head self-attention mechanism. The unique input–output mapping enables appreciable model generalization in overall structural characteristics, with the weighted cepstral coefficients serving as informative and compact data for efficient neural network modeling. The developed cepstral positional encoding scientifically guides the model to capture the coefficient indices, and the underlying trend of cepstral coefficients primarily governed by overall structural characteristics. The multi-head attention mechanism enables computationally efficient parallel analysis of interdependencies among coefficients, facilitating the development of a lightweight network. The effectiveness and superiority of the method have been validated using both simulated and experimental structural data.