Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386212657> ?p ?o ?g. }
Showing items 1 to 58 of
58
with 100 items per page.
- W4386212657 abstract "The rapid and accurate damage assessment of the buildings in the residential areas after the earthquake is an important issue for the reconstruction of the damaged cities. This study aims to automatically classify the damage conditions of structural load-bearing elements such as columns and beams with deep learning models through photographs taken from the buildings after the earthquake. ConvNeXt model, which is a new generation deep convolutional neural network, has been used as a deep learning method. Thanks to the trained model, it is possible to distinguish between two types of structural and nonstructural damage classes. In addition, the position of cracks on the existing structural element, which has an important place for engineers in damage detection, can also be determined. As the training dataset, reinforced concrete building images taken after the Elazi earthquake and labeled by experts were used. In the g current ConvNeXt model, a reliable model with high accuracy has been obtained using different transfer learning strategies and fine-tuning, data augmentation, and regularization techniques. The results obtained were compared with other convolutional neural network methods accepted in the literature, and it was observed that the ConvNeXt-based method produced faster and more accurate results." @default.
- W4386212657 created "2023-08-29" @default.
- W4386212657 creator A5044162933 @default.
- W4386212657 creator A5051965326 @default.
- W4386212657 creator A5058660643 @default.
- W4386212657 creator A5079427167 @default.
- W4386212657 creator A5092705797 @default.
- W4386212657 date "2023-07-05" @default.
- W4386212657 modified "2023-09-30" @default.
- W4386212657 title "A Structural Damage Assessment Model Based on Deep Learning" @default.
- W4386212657 doi "https://doi.org/10.1109/siu59756.2023.10223945" @default.
- W4386212657 hasPublicationYear "2023" @default.
- W4386212657 type Work @default.
- W4386212657 citedByCount "0" @default.
- W4386212657 crossrefType "proceedings-article" @default.
- W4386212657 hasAuthorship W4386212657A5044162933 @default.
- W4386212657 hasAuthorship W4386212657A5051965326 @default.
- W4386212657 hasAuthorship W4386212657A5058660643 @default.
- W4386212657 hasAuthorship W4386212657A5079427167 @default.
- W4386212657 hasAuthorship W4386212657A5092705797 @default.
- W4386212657 hasConcept C108583219 @default.
- W4386212657 hasConcept C119857082 @default.
- W4386212657 hasConcept C127413603 @default.
- W4386212657 hasConcept C150899416 @default.
- W4386212657 hasConcept C153180895 @default.
- W4386212657 hasConcept C154945302 @default.
- W4386212657 hasConcept C2776135515 @default.
- W4386212657 hasConcept C41008148 @default.
- W4386212657 hasConcept C50644808 @default.
- W4386212657 hasConcept C66938386 @default.
- W4386212657 hasConcept C81363708 @default.
- W4386212657 hasConceptScore W4386212657C108583219 @default.
- W4386212657 hasConceptScore W4386212657C119857082 @default.
- W4386212657 hasConceptScore W4386212657C127413603 @default.
- W4386212657 hasConceptScore W4386212657C150899416 @default.
- W4386212657 hasConceptScore W4386212657C153180895 @default.
- W4386212657 hasConceptScore W4386212657C154945302 @default.
- W4386212657 hasConceptScore W4386212657C2776135515 @default.
- W4386212657 hasConceptScore W4386212657C41008148 @default.
- W4386212657 hasConceptScore W4386212657C50644808 @default.
- W4386212657 hasConceptScore W4386212657C66938386 @default.
- W4386212657 hasConceptScore W4386212657C81363708 @default.
- W4386212657 hasLocation W43862126571 @default.
- W4386212657 hasOpenAccess W4386212657 @default.
- W4386212657 hasPrimaryLocation W43862126571 @default.
- W4386212657 hasRelatedWork W2996856019 @default.
- W4386212657 hasRelatedWork W3012459282 @default.
- W4386212657 hasRelatedWork W3018421652 @default.
- W4386212657 hasRelatedWork W3021430260 @default.
- W4386212657 hasRelatedWork W3091976719 @default.
- W4386212657 hasRelatedWork W3192840557 @default.
- W4386212657 hasRelatedWork W4220996320 @default.
- W4386212657 hasRelatedWork W4285149559 @default.
- W4386212657 hasRelatedWork W4312200629 @default.
- W4386212657 hasRelatedWork W4382286161 @default.
- W4386212657 isParatext "false" @default.
- W4386212657 isRetracted "false" @default.
- W4386212657 workType "article" @default.