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Probabilistic modeling of disrupted infrastructures due to fallen trees subjected to extreme winds in urban community

Authors :
Guangyang Hou
Suren Chen
Source :
Natural Hazards. 102:1323-1350
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

Tree failures due to strong winds in urban areas cause extensive direct and indirect economic and environmental loss, including disrupting adjacent infrastructures, such as buildings, underground pipelines, roads and overhead powerlines. To effectively improve the resilience of a community subjected to extreme wind events through prevention, response and recovery, it becomes critical to rationally assess the risks of wind-induced tree failures and the disruptions to different types of infrastructures due to fallen trees. An integrated probabilistic methodology to model the performance of disrupted infrastructures is developed for fallen urban trees subjected to extreme winds in a typical community. Firstly, the finite element modeling of the trees subjected to wind loads is conducted and based on which the windthrow fragility curves of several typical urban tree species are developed. Secondly, a probabilistic framework is developed based on the fragility results to characterize the disrupted scenarios and further predict the disruption probability of some critical infrastructures due to fallen trees. The matrix-based system reliability (MSR) method is introduced to assess the transportation network performance. The proposed framework and MSR method are demonstrated in detail on studying the overhead powerline and transportation network of a small urban community in the city of Fort Collins, Colorado. In the demonstrative example, the probabilities of powerline disruption, road closure, and origin–destination disconnection and travel time reliability under different wind conditions are predicted. Finally, mitigation efforts such as crown thinning of trees are discussed to reduce possible risks of disrupting the infrastructures.

Details

ISSN :
15730840 and 0921030X
Volume :
102
Database :
OpenAIRE
Journal :
Natural Hazards
Accession number :
edsair.doi...........e86d74e3eec321d1aad52b352a0e1e96
Full Text :
https://doi.org/10.1007/s11069-020-03969-y