极端气候下交通基础设施脆弱性研究综述

贾宏宇; 肖楚照; 康炜; 王传琦; 郑史雄

doi:10.3969/j.issn.0258-2724.20230650

极端气候下交通基础设施脆弱性研究综述

doi: 10.3969/j.issn.0258-2724.20230650

贾宏宇^{1, 2,},
肖楚照¹,
康炜^{1, 3},
王传琦^{1, 4},
郑史雄^{1, 2, ,}

1.
西南交通大学土木工程学院，四川成都，610031
2.
西南交通大学桥梁智能与绿色建造全国重点实验室，四川成都，611756
3.
中铁第一勘察设计院集团有限公司，陕西西安，710043
4.
中国市政工程西南设计研究总院有限公司，四川成都，610031

基金项目: 国家自然科学基金项目（52178169）

详细信息

作者简介:
贾宏宇（1981—），男，副教授，博士生导师，研究方向为桥梁抗震，E-mail：Hongyu1016@swjtu.edu.cn

通讯作者:
郑史雄（1965—），男，教授，博士生导师，研究方向为桥梁抗风与抗震，E-mail：Zhengsx@swjtu.edu.cn

中图分类号: TU99；P429
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- HTML全文浏览量: 81
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-06
- 录用日期: 2024-12-10
- 修回日期: 2024-01-15
- 网络出版日期: 2024-12-30

Review of Research on Vulnerability of Transportation Infrastructure to Extreme Climatic Conditions

JIA Hongyu^{1, 2
,},
XIAO Chuzhao¹,
KANG Wei^{1, 3},
WANG Chuanqi^{1, 4},
ZHENG Shixiong^{1, 2
, ,}

1.
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2.
State Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu 611756, China
3.
China Railway First Survey and Design Institute Group Co., Ltd., Xi’an 710043, China
4.
Southwest Municipal Engineering Design & Research Institute of China, Chengdu 610031, China

摘要

摘要:
全球气候变化日益剧烈，极端强降水、高温、低温以及干旱等极端气候事件对现有交通基础设施的运行性能造成影响，甚至导致严重损坏. 与此同时，随着交通强国战略的深入实施，大量新的交通基础设施在恶劣环境中被建设，新建设施的功能性、耐久性和维护管理面临前所未有的挑战. 极端气候荷载变化迅速且难以预测，常常伴随多种灾害的耦合效应，使得交通基础设施在其作用下的破坏机理极为复杂. 为确保极端气候条件下交通基础设施的安全和效能，在国内外极端气候及多灾害耦合研究的基础上，系统梳理了极端气候的时空演变、多灾害耦合作用的研究历程以及多重灾害对工程结构的影响机理. 在此基础上，明确了极端气候影响的特性，并提出交通基础设施在设计、施工和维护阶段的防灾减灾设计原则. 同时，综合总结了在极端气候条件下交通基础设施的多灾害风险评估方法，并对未来的研究方向进行展望，指出利用人工智能和机器学习技术进行极端气候灾害的快速预测和评估，以及在全寿命周期内分析交通基础设施系统性能的变化将成为重要的发展趋势. 为桥梁、道路和隧道等交通基础设施在极端气候条件下的抗灾设计、性能评估和韧性提升提供了宝贵的参考.
- 极端气候 /
- 交通基础设施 /
- 多灾害 /
- 全寿命周期 /
- 抗震韧性
Abstract:
The intensifying global climate change is increasingly affecting the operational performance of existing transportation infrastructure due to extreme climatic events such as heavy precipitation, high temperatures, low temperatures, and drought, leading to severe damage. Meanwhile, with the further implementation of the strategy of building China with a strong transportation network, a significant number of new transportation infrastructure projects are being constructed in harsh environments, posing unprecedented challenges to the functionality, durability, and maintenance management of these new facilities. The characteristics of extreme climate loads include rapid and unpredictable variations, often accompanied by coupled effects of multiple disasters, rendering the mechanisms of damage to transportation infrastructure under their influence highly complex. To ensure the safety and effectiveness of transportation infrastructure under extreme climatic conditions, Chinese and international research on extreme climate and multi-disaster coupling was studied, and the research progress on spatiotemporal evolution of extreme climates and multi-disaster coupling effects was systematically reviewed. The impact mechanisms of multiple disasters on engineering structures were sorted out. Based on this foundation, the characteristics of extreme climate impacts were defined, and disaster prevention and reduction design principles for transportation infrastructure during the design, construction, and maintenance phases were proposed. Furthermore, methods for assessing multi-disaster risks to transportation infrastructure under extreme climatic conditions were comprehensively summarized, and future research was prospected, highlighting the importance of utilizing artificial intelligence and machine learning technologies for rapid prediction and assessment of extreme climatic disasters and analyzing changes in the performance of transportation infrastructure systems throughout their whole life cycle. This research provides valuable references for the disaster-resistant design, performance assessment, and resilience enhancement of transportation infrastructure such as bridges, roads, and tunnels under extreme climatic conditions.
- extreme climate /
- transportation infrastructure /
- multi-disaster /
- whole life cycle /
- seismic resilience

HTML全文

图 1 长江流域极端气候事件危险性等级（198l—2010年）^[5]

Figure 1. Risk level of extreme climatic events in Yangtze River Basin （198l–2010）^[5]

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图 2 极端气候对交通基础设施的影响

Figure 2. Impacts of extreme climates on transportation infrastructure

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图 3 长江流域极端气候事件年变化率（196l—2020年）^[5]

Figure 3. Annual change rate of extreme climatic events in Yangtze River Basin （1961–2020）^[5]

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图 4 缺陷桥梁适应气候变化的成本

Figure 4. Cost of climate change adaptation for defective bridges

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图 5 极端气候事件引起的生命损失

Figure 5. Loss of life due to extreme climatic events

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图 6 暴雨导致Hatchie River 桥梁冲刷破坏

Figure 6. Erosion damage to Hatchie River Bridge caused by heavy rain

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图 7 飓风桑迪引起公路和桥梁破坏

Figure 7. Damage to roads and bridges caused by hurricane Sandy

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图 8 特大暴雨引起石太铁路桥梁垮塌

Figure 8. Collapse of Shijiazhuang-Taiyuan Railway Bridge caused by heavy rain

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图 9 多灾害风险矩阵

Figure 9. Multi-disaster risk matrix

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图 10 台风灾害桥梁设计风险矩阵

Figure 10. Risk matrix of bridge design in typhoon disaster

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图 11 极端气候灾害时空分布

Figure 11. Spatiotemporal distribution of extreme climatic disasters

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图 12 气候多灾害评估量化步骤

Figure 12. Quantitative steps of climatic multi-disaster assessment

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图 13 冲刷主动防护措施^[79]

Figure 13. Active protection measures for erosion^[79]

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图 14 冲刷被动防护措施^[79]

Figure 14. Passive protection measures for erosion^[79]

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图 15 基于时变风险模型的概率密度函数与风险重现区间的关系^[54]

Figure 15. Relationship between probability density function and risk recurrence interval based on time-varying risk model^[54]

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图 16 全寿命周期内性能预测

Figure 16. Performance prediction during whole life cycle

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图 17 最优Copula函数步骤

Figure 17. Steps for optimal Copula function

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图 18 风险曲线主要流程

Figure 18. Main process of risk curve calculation

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图 19 风险评估框架主要流程

Figure 19. Main process of risk assessment framework

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图 20 技术路线

Figure 20. Technology roadmap

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图 21 中国典型灾害地区分布^[67]

Figure 21. Distribution of typical disaster areas in China^[67]

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表 1 未来中国年平均地表气温与降水量（相对1961—1990年平均值）

Table 1. Annual average surface temperature and precipitation in China in the future （relative to average value in 1961–1990）

年份温度变化/℃ 降水变化%

2020 年 1.3～2.1 2～3

2030 年 1.5～2.8

2050 年 2.3～3.3 5～7

2100 年 3.9～6.0 11～17

下载: 导出CSV

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