Monitoring Flexural Fatigue Life of Asphalt Concrete by Piezoelectric Sensors: Strategy for fatigue life prediction and damage detection based on load history on any asphalt matrix

This dissertation endeavors to introduce a novel supervised Structural Health Monitoring (SHM) methodology for the detection of damage and the prediction of fatigue life in asphalt concrete materials. Grounded in the principles of S-N (strain-number of cycles until failure) curves, this research addresses the intricate task of proficiently...

A Methodology for Damage Detection Using Unsupervised Learning in the Field of Structural Health Monitoring: Based on Gaussian Mixture Modeling

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...