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Machine learning-aided scenario-based seismic drift measurement for RC moment frames using visual features of surface damage
, Article Measurement: Journal of the International Measurement Confederation ; Volume 205 , 2022 ; 02632241 (ISSN) ; Mansourdehghan, S ; Asjodi, A. H ; Dolatshahi, K.M ; Sharif University of Technology
Elsevier B.V
2022
Abstract
This paper presents a novel computer vision-based methodology for assessment of the seismic damage in reinforced concrete moment frames using visual characteristics of surface damage following an earthquake. An extensive collected database comprising 974 images associated with 256 cyclic-loaded damaged beam-column joints, providing a set of cracking and crushing progression with increasing the evolution of damage level, is collected and used for the development and validation of the methodology. Employing image processing techniques, the characteristics of the surface damage, including the cracking length and crushing areas, are measured and used in a scenario-based assessment for the...