数据驱动的公路桥梁网络全寿命抗震韧性评估

doi:10.13229/j.cnki.jdxbgxb.20221352

Abstract

Abstract:

Firstly， a seismic resilience assessment framework was proposed for highway bridge networks by comprehensively integrating various influencing factors （network topologies， deterioration effects of bridges， regional seismic hazards， etc.） and their uncertainties. Then， the network topologies， seismic performance of regional bridges and traffic functionalities of highway segments were analyzed， and integrated to establish a time-dependent post-disaster simulation of highway bridge networks. Subsequently， an artificial neural network based methodology was proposed for rapid seismic fragility assessment of regional bridges. Finally， the life-cycle seismic resilience of the highway bridge network in San Francisco， USA was analyzed as a case study， which reveals the evolution law of seismic resilience the service life. The results demonstrate the developed data-driven fragility model can act as a reliable surrogate to traditional time-consuming fragility methods， with the goodness of fit of 0.73. Consequently， the proposed methodology for the life-cycle seismic resilience of highway bridge networks is effective and accurate， providing decision-making strategies for disaster prevention and mitigation.

Key words: engineering of communication and transportation system, traffic, highway bridge network, seismic resilience, data-driven fragility, life-cycle seismic performance

CLC Number:

Zhen-liang LIU,Cun-bao ZHAO,Yun-peng WU,Mi-na MA,Long-shuang MA. Life⁃cycle seismic resilience assessment of highway bridge networks using data⁃driven method[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1695-1701.

Figures/Tables 8

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Table 1

Link damage state-related residual traffic capacity and free-flow speed"

公路损伤状态 $L D$	残余功能百分比/%
公路损伤状态 $L D$	$γ c$	$γ v$
未破坏（L_D $< 0.5$ ）	100	100
轻微破坏（ $0.5 ≤$ L_D<1.0）	100	75
中等破坏（ $1.0 ≤$ L_D<1.5）	75	50
严重破坏（ $1.5 ≤$ L_D $< ∞$ ）	50	50
完全破坏（L_D $= ∞$ ）	0	0

Table 1

Fig.7

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Life⁃cycle seismic resilience assessment of highway bridge networks using data⁃driven method

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