Bayesian Networks for post-earthquake assessment of bridges

Y. C. Yue, M. Pozzi, D. Zonta, R. Zandonini

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

4 Citations (Scopus)

Abstract

We propose a probabilistic framework to estimate the condition state of a bridge stock in a post-earthquake situation based on the knowledge of the state of individual bridges, appraised through visual inspection. The capacity of each bridge is modeled using Hazus' fragility curve approach, while the geographical distribution of the seismic demand is calculated using an attenuation function. Uncertainty terms are considered both in capacity and demand, and the logical correlation between capacity and demand inherent to different bridges is handled through Bayesian Networks. In this paper this framework is developed in the simplest case of two bridges. The correlation between two bridges is first analyzed, and their prior probabilities of being in a damage state are calculated during the initialization procedure. When an earthquake occurs, if data such as magnitude and epicenter are known, or the damage state of one of the bridges is observed, then the probability of the companion bridge being in a damage state can be predicted and updated. Two bridges of similar characteristics located in the Italian Trentino region, are used to illustrate this approach.

LanguageEnglish
Title of host publicationBridge Maintenance, Safety, Management, Resilience and Sustainability
Subtitle of host publicationProceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management
Place of PublicationBoca Raton, FL.
Pages722-729
Number of pages8
DOIs
Publication statusPublished - 2012
Event6th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2012 - Stresa, Lake Maggiore, Italy
Duration: 8 Jul 201212 Jul 2012

Conference

Conference6th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2012
CountryItaly
CityStresa, Lake Maggiore
Period8/07/1212/07/12

Fingerprint

Bayesian networks
Earthquakes
Geographical distribution
Inspection

Keywords

  • bridge construction
  • post-earthquake assessments
  • seismic assessment

Cite this

Yue, Y. C., Pozzi, M., Zonta, D., & Zandonini, R. (2012). Bayesian Networks for post-earthquake assessment of bridges. In Bridge Maintenance, Safety, Management, Resilience and Sustainability: Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management (pp. 722-729). Boca Raton, FL.. https://doi.org/10.1201/b12352-97
Yue, Y. C. ; Pozzi, M. ; Zonta, D. ; Zandonini, R. / Bayesian Networks for post-earthquake assessment of bridges. Bridge Maintenance, Safety, Management, Resilience and Sustainability: Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management. Boca Raton, FL., 2012. pp. 722-729
@inproceedings{a7d1b5fde069441b86257e992351e124,
title = "Bayesian Networks for post-earthquake assessment of bridges",
abstract = "We propose a probabilistic framework to estimate the condition state of a bridge stock in a post-earthquake situation based on the knowledge of the state of individual bridges, appraised through visual inspection. The capacity of each bridge is modeled using Hazus' fragility curve approach, while the geographical distribution of the seismic demand is calculated using an attenuation function. Uncertainty terms are considered both in capacity and demand, and the logical correlation between capacity and demand inherent to different bridges is handled through Bayesian Networks. In this paper this framework is developed in the simplest case of two bridges. The correlation between two bridges is first analyzed, and their prior probabilities of being in a damage state are calculated during the initialization procedure. When an earthquake occurs, if data such as magnitude and epicenter are known, or the damage state of one of the bridges is observed, then the probability of the companion bridge being in a damage state can be predicted and updated. Two bridges of similar characteristics located in the Italian Trentino region, are used to illustrate this approach.",
keywords = "bridge construction, post-earthquake assessments, seismic assessment",
author = "Yue, {Y. C.} and M. Pozzi and D. Zonta and R. Zandonini",
year = "2012",
doi = "10.1201/b12352-97",
language = "English",
isbn = "9780415621243",
pages = "722--729",
booktitle = "Bridge Maintenance, Safety, Management, Resilience and Sustainability",

}

Yue, YC, Pozzi, M, Zonta, D & Zandonini, R 2012, Bayesian Networks for post-earthquake assessment of bridges. in Bridge Maintenance, Safety, Management, Resilience and Sustainability: Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management. Boca Raton, FL., pp. 722-729, 6th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2012, Stresa, Lake Maggiore, Italy, 8/07/12. https://doi.org/10.1201/b12352-97

Bayesian Networks for post-earthquake assessment of bridges. / Yue, Y. C.; Pozzi, M.; Zonta, D.; Zandonini, R.

Bridge Maintenance, Safety, Management, Resilience and Sustainability: Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management. Boca Raton, FL., 2012. p. 722-729.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Bayesian Networks for post-earthquake assessment of bridges

AU - Yue, Y. C.

AU - Pozzi, M.

AU - Zonta, D.

AU - Zandonini, R.

PY - 2012

Y1 - 2012

N2 - We propose a probabilistic framework to estimate the condition state of a bridge stock in a post-earthquake situation based on the knowledge of the state of individual bridges, appraised through visual inspection. The capacity of each bridge is modeled using Hazus' fragility curve approach, while the geographical distribution of the seismic demand is calculated using an attenuation function. Uncertainty terms are considered both in capacity and demand, and the logical correlation between capacity and demand inherent to different bridges is handled through Bayesian Networks. In this paper this framework is developed in the simplest case of two bridges. The correlation between two bridges is first analyzed, and their prior probabilities of being in a damage state are calculated during the initialization procedure. When an earthquake occurs, if data such as magnitude and epicenter are known, or the damage state of one of the bridges is observed, then the probability of the companion bridge being in a damage state can be predicted and updated. Two bridges of similar characteristics located in the Italian Trentino region, are used to illustrate this approach.

AB - We propose a probabilistic framework to estimate the condition state of a bridge stock in a post-earthquake situation based on the knowledge of the state of individual bridges, appraised through visual inspection. The capacity of each bridge is modeled using Hazus' fragility curve approach, while the geographical distribution of the seismic demand is calculated using an attenuation function. Uncertainty terms are considered both in capacity and demand, and the logical correlation between capacity and demand inherent to different bridges is handled through Bayesian Networks. In this paper this framework is developed in the simplest case of two bridges. The correlation between two bridges is first analyzed, and their prior probabilities of being in a damage state are calculated during the initialization procedure. When an earthquake occurs, if data such as magnitude and epicenter are known, or the damage state of one of the bridges is observed, then the probability of the companion bridge being in a damage state can be predicted and updated. Two bridges of similar characteristics located in the Italian Trentino region, are used to illustrate this approach.

KW - bridge construction

KW - post-earthquake assessments

KW - seismic assessment

UR - http://www.scopus.com/inward/record.url?scp=84863953727&partnerID=8YFLogxK

U2 - 10.1201/b12352-97

DO - 10.1201/b12352-97

M3 - Conference contribution book

SN - 9780415621243

SP - 722

EP - 729

BT - Bridge Maintenance, Safety, Management, Resilience and Sustainability

CY - Boca Raton, FL.

ER -

Yue YC, Pozzi M, Zonta D, Zandonini R. Bayesian Networks for post-earthquake assessment of bridges. In Bridge Maintenance, Safety, Management, Resilience and Sustainability: Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management. Boca Raton, FL. 2012. p. 722-729 https://doi.org/10.1201/b12352-97