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Probabilistic connectivity assessment of bridge networks considering spatial correlations associated with flood and seismic hazards.

Authors :
Firdaus, Putri S.
Matsuzaki, Hiroshi
Akiyama, Mitsuyoshi
Aoki, Koki
Frangopol, Dan M.
Source :
Structure & Infrastructure Engineering: Maintenance, Management, Life-Cycle Design & Performance. Jul/Aug2024, Vol. 20 Issue 7/8, p1015-1032. 18p.
Publication Year :
2024

Abstract

To estimate the connectivity of a road network, it is crucial to evaluate the correlation of hazard intensities among individual bridge locations since the probability of multiple bridges being damaged simultaneously depends on the degree of this correlation. However, research on connectivity assessment of bridge networks considering spatial correlations associated with flood intensities is scarce in the literature. When quantifying the spatial correlation of flood intensities, modeling based on the stream distance rather than the Euclidean distance is required, taking into account that river flow is restricted only within the stream network. To achieve this purpose, a novel methodology is proposed to evaluate the spatial correlation of a stream network based on a geostatistical linear model and stream network covariance models. In addition, this study considers the spatial correlation of seismic hazard intensity. With the proposed method, it is possible to identify which bridges play an important role in ensuring the connectivity of the road network under multiple hazards, i.e. flood and seismic. As an illustrative example, the proposed method is applied to a hypothetical bridge network in Kumamoto Prefecture, Japan. The results demonstrate that improved network connectivity can be achieved by implementing a relevant retrofitting strategy for important bridges. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15732479
Volume :
20
Issue :
7/8
Database :
Academic Search Index
Journal :
Structure & Infrastructure Engineering: Maintenance, Management, Life-Cycle Design & Performance
Publication Type :
Academic Journal
Accession number :
177318653
Full Text :
https://doi.org/10.1080/15732479.2023.2276373