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A new deep-learning algorithm studies aerial photographs after fires to identify damage.
Satellite imagery has been used extensively to identify damage in conflict areas, for example in Syria and Libya.
A methodology to identify damage in a structure is presented in this paper.
Most vibration-based methods identify damage by interpreting measured changes in modal parameters.
Unsupervised pattern recognition procedure is applied to identify damage mechanisms from acoustic signals.
The AE method utilizes the signal energy rate per time to identify damage events.
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Therefore, this study proposes an innovative neural network (NN -based patterNN -baseds tool (1) to identify damage-sensitive frequency ranges autonomously and (2) to pattern detanalysisformatool such as the damage type and severity.
Then three damage quantification techniques are studied to identify damages more precisely.
Also the tests of re-saturation helped to identify damages of the lightweight concrete structure.
Therefore, it is necessary to clearly identify damaged elements on grounding grid.
To identify damages of electrical messenger wire in the icing environment, a synergetic damage recognition approach was put forward.
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