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University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade – Faculty of Mathematics , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
University of Belgrade - Faculty of Civil Engineering , Belgrade , Serbia
The paper presents the RELAR project, with the aim of improving the assessment of losses and the recovery process after an earthquake. The project is based on the application of machine learning methods and image recognition techniques to speed up the damage assessment process and increase the accuracy of remediation cost estimates. The limitations of traditional approaches are pointed out, which are often time-consuming and prone to errors due to the lack of data and their rigidity. RELAR introduces innovative solutions that enable fast and reliable assessments, independent of the availability of ground acceleration records. The ultimate goal of the project is the development of algorithms, models and guidelines for risk reduction and faster recovery.
This research was supported by the Science Fund of the Republic of Serbia, Grant No. 7038, Rapid Earthquake Loss Assessment and Recovery Framework – RELAR.
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