0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAssessment of Tsunami vulnerability of coastal buildings has gained high interest in the last years for the areas characterized by a high Tsunami hazard.A probabilistic representation of vulnerability is performed by fragility curves, fundamental tools to define possible strategies for risk mitigation.Different prediction approaches can be used for obtaining analytical fragility curves.In this paper, a prediction proposal to be used for masonry structures typical of the Mediterranean coasts based on simplified structural analyses and damage indexes is presented.Different damage states are considered and inundation depth is assumed as input intensity measure.The uncertainties in the demand by the definition of the probability distribution of the inundation scenarios are considered.Further, the uncertainties in the structural capacity are included.Monte Carlo simulations are performed for the scope of this work.
Panagiotis Asteris, Marco Filippo Ferrotto, Liborio Cavaleri (2023). A PROCEDURE FOR THE PROBABILISTIC ASSESSMENT OF MASONRY STRUCTURES UNDER TSUNAMI. , DOI: https://doi.org/10.7712/120123.10532.21508.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2023
Authors
3
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.7712/120123.10532.21508
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access