Leonardo Zunino
BRIDGE DAMAGE DETECTION UNDER TRAFFIC LOADING AND ENVIRONMENTAL VARIABILITY USING AI: A REVOLUTION IN INFRASTRUCTURAL ASSET MANAGEMENT.
Rel. Marco Domaneschi, Joan Ramon Casas. Politecnico di Torino, Master of science program in Civil Engineering, 2024
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Abstract
Bridges are vital components of civil infrastructure that must operate safely and reliably. Traditional methods for assessing structural health rely on the concept that changes in a structure's dynamic response may indicate potential damage. However, in the case of bridges, variations due to operational factors (like traffic loads) and environmental factors (such as temperature and humidity) can also contribute to these changes. This makes damage detection more challenging, as a bridge may still be safe while exhibiting changes in its dynamic response due to these factors. If the effects of traffic and the environment are not properly accounted for, it could lead to false positive alerts.
This thesis proposes and tests a methodology for detecting and localizing damage in bridges subjected to traffic loads and environmental variability
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