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However, in situations such as damaged tray, the damage severity cannot be identified using conventional gamma-ray scanning method.
Two testing methods are studied with TSA to detect damage and characterize damage severity.
This new index simultaneously reflects the damage severity and possibility of damage at each structural component.
Then we demonstrate that damage severity can be identified using a supervised neural networks approach.
In terms of multi-impacts, the damage severity is also very influenced by the distance.
Logistic regression models for predicting probability of damage for various levels of damage severity were evaluated.
Also, the movement and damage severity can be identified using the dynamic bridge characteristics obtained from GPS.
Furthermore, the locations and severities of multiple damages are identified via the superposition of the presented damage severity functions.
A damage severity index has been introduced and effect of damage on the blade group natural frequencies is investigated.
The robustness of the method was examined with respect to the damage severity with various damage conditions.
The unknown excitation force, damage location, and damage severity in the plane truss structure are successfully identified.
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