A damage localization and quantification algorithm for indirect structural health monitoring of bridges using multi-task learning

Abstract
We propose a multi-task learning approach for estimating both the location and magnitude of damage occurring on an experimental bridge using acceleration signals collected from a passing vehicle. This is a low-cost and low-maintenance indirect structural health monitoring approach in which sensors on the vehicle are used to detect bridge damage. Recently, signal processing and machine learning approaches have been shown to perform well in...
Paper Details
Title
A damage localization and quantification algorithm for indirect structural health monitoring of bridges using multi-task learning
Published Date
Jan 1, 2019
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