A post-earthquake repair decision method for bridges in regional road networks considering power facility failures
Article Text (Baidu Translation)
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摘要: 为降低地震灾害对区域交通系统运行的负面影响，增强公路交通基础设施的震后恢复能力，研究了资源受限条件下区域路网桥梁群的震后修复决策问题；基于路段通行成本函数与交叉口控制延误模型，构建了能够量化交通功能随设施损伤和修复动态变化的区域路网交通性能分析方法；引入2类依存机制，建立了考虑电力设施失效的区域路网桥梁韧性评估模型；以桥梁和电力设施联合修复次序为优化变量，设计了基于韧性最优准则的双层修复决策模型，其中上层为整数规划模型，用于求解桥梁和电力设施的最优修复调度，下层为用户均衡流量分配模型，旨在动态评估桥梁和电力设施服务状态变化对交通用户出行效率的影响；以Centerville社区的交通-电力耦合网络为算例，对模型的有效性和应用价值进行了分析验证。分析结果表明：修复决策方法能够有效解决考虑电力设施失效与多重资源约束的区域路网桥梁修复优化问题，准确再现震后交通功能动态演化过程；相比传统基准策略，近似最优修复策略在网络恢复效率与抗震韧性方面均显著提升；相互依存关系对修复次序优化具有显著影响，忽略功能和修复依存关系将导致区域交通功能与抗震韧性被高估。研究成果可为区域交通基础设施的灾后恢复决策与抗震韧性提升提供科学参考。Abstract: The post-earthquake repair decision-making of bridge groups in regional road networks under resource constraints was studied to mitigate the adverse effects of earthquake disasters on the operation of regional traffic systems and enhance the post-disaster recovery capabilities of road traffic infrastructure. A traffic performance analysis method for regional road networks was developed based on the travel cost function of road segments and the intersection control delay model, enabling the quantification of dynamic changes of traffic functions with infrastructure damage and repair processes. By incorporating two types of interdependency between infrastructure systems, a resilience assessment model for bridges in regional road networks that considers power infrastructure failures was built. By employing the joint repair sequence of bridges and power infrastructure as the optimization variable, a bi-level repair decision-making model was designed based on resilience-oriented criteria. Specifically, the upper level is formulated as an integer programming model for determining the optimal repair scheduling of both bridges and power infrastructure, while the lower level adopts a user equilibrium traffic assignment model to dynamically evaluate the influence of service state changes of bridges and power infrastructure on travel efficiency. The model's validity and application value were analyzed and verified by employing the transportation-power network of the Centerville virtual community as a case study. Analysis results demonstrate that the proposed repair decision-making method can effectively address the optimization problem of post-earthquake bridge repair under power infrastructure failures and multiple resource constraints, accurately reproducing the dynamic evolution of traffic functions. Compared to traditional baseline strategies, the near-optimal repair strategy significantly enhances both its network recovery efficiency and seismic resilience. Furthermore, the interdependency exerts a significant influence on repair sequence optimization, and neglecting both functional and repair dependency results in the overestimation of regional traffic performance and seismic resilience. The findings can provide scientific reference for post-earthquake repair decision-making and seismic resilience enhancement of regional traffic infrastructure.
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图 9 震前与震后各节点对间的最短通行时间的比值
Figure 9. Ratio of the shortest travel time between each node pair before and after earthquake
图 13 1 000个样本下不同策略的网络功能演变结果
Figure 13. Results from 1 000 simulations for different strategies
图 14 不同修复策略的平均恢复轨迹
Figure 14. Average network recovery trajectories with different restoration strategies
表 1 相互依存关系的施加方式
Table 1. Implementation of interdependencies
相互依存关系类别 影响因素 施加对象 功能依存关系 交叉口延误 桥梁网络 修复相互依存关系 修复延误 电力网络 延长修复持时 桥梁网络 
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表 2 桥梁损伤状态与道路残余性能
Table 2. Bridge damage state and residual performance of road segment
% 损伤状态 残余百分比 交通容量θc 自由流速度θv ND 100 100 SD 70 75 MD 50 50 ED 20 30 CD 0 0
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表 3 损伤状态与预期修复持时
Table 3. Damage states and expected restoration durations of elements
编号 损伤
状态平均修复
持时/d编号 损伤
状态平均修复
持时/dB2 CD 162 E5 ED 8 B3 CD 175 E6 CD 33 B4 ED 66 E9 CD 11 B5 CD 158 E11 ED 2 B6 MD 47 E12 CD 9 B7 CD 139 E15 CD 9 B8 ED 73 E16 CD 6 B9 SD 20 E17 ED 9 B10 ED 85 E22 CD 7 E2 ED 3 E23 ED 11 E3 CD 8 E26 CD 35 E4 CD 29 E31 CD 6
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表 4 修复策略的统计结果
Table 4. Statistical results for all strategies
修复策略 NOS BM1 BM2 BM3 抗震
韧性最大值 0.776 8 0.643 2 0.654 5 0.621 6 最小值 0.750 3 0.589 9 0.590 7 0.565 1 均值 0.763 6 0.617 6 0.621 2 0.594 0 变异系数 0.006 4 0.014 7 0.016 8 0.017 3 最终
恢复
时间最大值/d 600.820 4 747.583 4 728.049 7 673.037 3 最小值/d 510.394 4 656.775 6 609.581 8 595.814 6 均值/d 551.777 3 703.332 3 670.063 9 636.338 1 变异系数 0.027 1 0.021 7 0.024 7 0.021 1 P[Q(720)=1]/% 100.0 88.8 98.4 100.0
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