Abstract

This research delves into a transformative approach to bridge health monitoring, introducing unsupervised machine learning, with a specific emphasis on acoustic characteristics. The aim is to revolutionize traditional physics-based methods, motivated by the constraints inherent in conventional structural health monitoring heavily reliant on manual inspections. Leveraging techniques such as Melfrequency cepstral coefficients (MFCC), we transform acoustic features to serve as the foundation for a neural network’s classification capabilities. This innovative methodology enables the discernment between healthy and potentially compromised bridge conditions.

Keywords:Structural health monitoring; Unsupervised machine learning; Feature extraction; Classification; Autoencoder; Mel-frequency cepstral coefficients; ADA Bridge; Self-Supervised learning

Abbreviations: MFCC: Mel-frequency Cepstral Coefficients; NN: Neural Network; PCA: Principal Component Analysi