Damage detection for bridges via adaptive modal neighborhood standardization considering environmental variability
Structural Health Monitoring: An International Journal
Published online on June 07, 2026
Abstract
Structural Health Monitoring, Ahead of Print.
Data-driven unsupervised learning models are widely regarded as valuable tools for bridge damage detection. However, variable environmental conditions introduce nonlinear and non-Gaussian characteristics in modal frequencies, along with local ...
Data-driven unsupervised learning models are widely regarded as valuable tools for bridge damage detection. However, variable environmental conditions introduce nonlinear and non-Gaussian characteristics in modal frequencies, along with local ...