Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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Bureau de Recherches Géologiques et Minières

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2023A methodology for the risk-based design of bridges in Italycitations
  • 2013Ranking of epistemic uncertainties in scenario-based seismic risk evaluationscitations
  • 2011Modeling the difference in ground-motion magnitude-scaling in small and large earthquakes37citations
  • 2010Comment on "Test of seismic hazard map from 500 years of recorded intensity data in Japan" by Masatoshi Miyazawa and Jim Mori7citations

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Chart of shared publication
Turchetti, Francesca
1 / 1 shared
Dallasta, Andrea
1 / 6 shared
Zanini, Mariano
1 / 1 shared
Tubaldi, Enrico
1 / 2 shared
Gehl, Pierre
1 / 1 shared
Ducellier, Ariane
1 / 1 shared
Ulrich, Thomas
1 / 2 shared
Rohmer, Jeremy
1 / 2 shared
Negulescu, Caterina
1 / 1 shared
Jousset, Philippe
1 / 3 shared
Bard, Pierre Yves
1 / 1 shared
Beauval, Céline
1 / 1 shared
Chart of publication period
2023
2013
2011
2010

Co-Authors (by relevance)

  • Turchetti, Francesca
  • Dallasta, Andrea
  • Zanini, Mariano
  • Tubaldi, Enrico
  • Gehl, Pierre
  • Ducellier, Ariane
  • Ulrich, Thomas
  • Rohmer, Jeremy
  • Negulescu, Caterina
  • Jousset, Philippe
  • Bard, Pierre Yves
  • Beauval, Céline
OrganizationsLocationPeople

conferencepaper

Ranking of epistemic uncertainties in scenario-based seismic risk evaluations

  • Gehl, Pierre
  • Douglas, John
  • Ducellier, Ariane
  • Ulrich, Thomas
  • Rohmer, Jeremy
  • Negulescu, Caterina
Abstract

International audience ; In the scope of a scenario-based risk analysis, this study aims to quantify and rank various types of epistemic uncertainties that enter into the derivation of fragility functions for common buildings. Using a numerical model of a test structure (a reinforced concrete five-story building with infill panels on the first two floors), a first type of uncertainty is introduced, consisting of the mechanical properties of the materials (i.e. Young's modulus and compressive strength for concrete, and Young's modulus and yield strength for steel). The area of longitudinal reinforcement is also modified in the model, to generate various damage mechanisms for the same structure, depending on which floor first experiences failure. Finally, another source of epistemic uncertainty is studied, by comparing different types of fragility models: fragility curves derived from dynamic analyses and fragility functions generated from a capacity spectrum approach (i.e. use of a set of natural response spectra to identify a series of performance points from the capacity curve). To this end, a ranking of the importance of different sources of uncertainty in the vulnerability analysis (i.e. mechanical properties, structural models and fragility models) is conducted by computing, for each uncertainty source, the Sobol' indices (i.e. the main effects and total effects of each source of uncertainty). This variance based sensitivity technique presents the appealing features of both exploring the influence of input parameters over their whole range of variation and fully accounting for possible interactions between them. Nevertheless, addressing the issue of sensitivity to model uncertainty implies paying special attention to the appropriate treatment of different types of input parameters, i.e. continuous for mechanical properties or categorical in the case of fragility models. This is achieved by relying on recent advances in functional variance decomposition. For all these types of models, a uncertainty analysis ...

Topics
  • impedance spectroscopy
  • strength
  • steel
  • yield strength
  • decomposition