Fragility curves are required for the probabilistic evaluation of performance of an earthquakedamaged transportation network. Recently, fragility curves of several hundreds of bridges have been obtained via 3D inelastic response history analysis, and then used to calibrate a Bayesian network (BN) model of seismic fragility. This paper investigates the sensitivity of network-level results to the use of such a BN-based surrogate fragility model. Bridge damages were evaluated with both the exact FEM-based and the approximate BN-based fragilities, and network flow analysis was carried out on the damaged network for thousands of seismic events in several Monte Carlo simulations. Even though the BN-based model does not perform in an equally good manner over all bridges, overall more accurate results at the network level were observed, indicating that network performance does not in general depend on any single bad match between reference and approximate fragility curves.

A Bayesian Network model to assess seismic risk of reinforced concrete girder bridges / Franchin, Paolo; Lupoi, Alessio; Noto, Fabrizio; Tesfamariam, Solomon. - CD-ROM. - (2015). ((Intervento presentato al convegno 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 tenutosi a Canada nel 2015.

A Bayesian Network model to assess seismic risk of reinforced concrete girder bridges

Franchin, Paolo;Lupoi, Alessio;
2015

Abstract

Fragility curves are required for the probabilistic evaluation of performance of an earthquakedamaged transportation network. Recently, fragility curves of several hundreds of bridges have been obtained via 3D inelastic response history analysis, and then used to calibrate a Bayesian network (BN) model of seismic fragility. This paper investigates the sensitivity of network-level results to the use of such a BN-based surrogate fragility model. Bridge damages were evaluated with both the exact FEM-based and the approximate BN-based fragilities, and network flow analysis was carried out on the damaged network for thousands of seismic events in several Monte Carlo simulations. Even though the BN-based model does not perform in an equally good manner over all bridges, overall more accurate results at the network level were observed, indicating that network performance does not in general depend on any single bad match between reference and approximate fragility curves.
12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012
Civil and Structural Engineering; Statistics and Probability
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Bayesian Network model to assess seismic risk of reinforced concrete girder bridges / Franchin, Paolo; Lupoi, Alessio; Noto, Fabrizio; Tesfamariam, Solomon. - CD-ROM. - (2015). ((Intervento presentato al convegno 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 tenutosi a Canada nel 2015.
File allegati a questo prodotto
File Dimensione Formato  
Cavalieri_Sensitivity_2106.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.28 MB
Formato Adobe PDF
2.28 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1099180
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact