CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
Database on performance of low- rise reinforced concrete buildings in the 2015 Nepal earthquake
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VGG16 transfer learning on 1200 building images yields 97.85% training and 89.38% validation accuracy for four-class post-earthquake damage classification.
citing papers explorer
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Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure
CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
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Post-Earthquake Assessment of Buildings Using Deep Learning
VGG16 transfer learning on 1200 building images yields 97.85% training and 89.38% validation accuracy for four-class post-earthquake damage classification.