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Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this framework, we tested six machine learning models on the DaDO database of observed damage from Italian earthquakes to assess their efficacy in characterizing seismic damage. The models included random forest, gradient boosting, and extreme gradient boosting. The input features were all or a subset of the structural features provided by DaDO, as well as macroseismic intensity. Extreme gradient boosting classification performed best, especially when using basic structural features (age, number of storeys, floor area, building height) and grouping damage according to the traffic-light-based system used, for example, during the post-disaster period (green, yellow, and red). The machine learning-based model had similar efficacy to the traditional Risk-UE method. Finally, the importance of structural features varied depending on the level of damage considered., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Assessing post-seismic damage on an urban/regional scale remains relatively difficult owing to the significant amount of time and resources required to acquire informa- tion and conduct a building-by-building seismic damage assessment. However, the application of new methods based on artificial intelligence, combined with the increasingly systematic availability of field surveys of post-seismic damage, has pro- vided new perspectives for urban/regional seismic damage assessment. This study analyzes the effectiveness and relevance of a number of machine learning techniques for analyzing spatially distributed seismic damage after an earthquake at the regional scale. The basic structural parameters of a portfolio of buildings and the post- earthquake damage surveyed after the Nepal 2015 earthquake are analyzed and com- bined with macro-seismic intensity values provided by the United States Geological Survey ShakeMap tool. Among the methods considered, the random forest regres- sion model provides the best damage predictions for specified ground motion inten- sity values and structural parameters. For traffic-light-based damage classification (three classes: green-, amber-, and red-tagged buildings based on post-earthquake damage grade), a mean accuracy of 0.68 is obtained. This study shows that restricting learning to basic features of buildings (i.e. number of stories, height, plinth area, and age), which could be readily available from authoritative databases (e.g. national cen- sus) or field-surveyed databases, yields a reliable prediction of building damage (4 fea- tures/3 damage grade accuracy: 0.64).

Pancreas divisum is the most common congenital malformation of the pancreas. Sometimes it is considered an etiology when patients present with recurrent episodes of pancreatitis. Endoscopic retrograde pancreatography (ERP) with selective cannulation of the minor papilla with sphincterotomy and stent placement are performed in these patients. Proximal migration of pancreatic stents in pancreas divisum is rare and challenging to manage. We describe a case of proximal migration of a pancreatic stent in a patient with pancreas divisum and perform a review of literature.