Seismic fragility, loss, and resilience assessment of Canada's bridge infrastructure

不列颠哥伦比亚省西南部和圣劳伦斯山谷都有发生强烈地震的危险。这些地区的桥梁结构由于老化和老化、过时的设计和延期维修而易受地震影响。例如，卑诗省近40%的公路桥是在第一张概率地震危险图绘制出来之前建成的。此外，BC省和魁北克省的交通基础设施由各种各样的桥梁组成，这些桥梁在材料、类型、连通性、几何形状和设计细节上各不相同。鉴于其复杂性，现有的研究不足以对加拿大西部和东部的桥梁清单提供全面、一致和可靠的地震评估。该计划旨在提高加拿大交通基础设施的抗震能力。为实现这一目标，将制定一个综合框架来评估卑诗省和魁北克省区域桥梁系统的地震脆弱性、损失和恢复能力。首先，将通过调查不列颠哥伦比亚省交通和基础设施部（BCMoT）和魁北克省交通部（MTQ）的大量图纸文件，建立高分辨率桥梁库存数据库。其次，高保真响应和容量模型将在OpenSees（一个开放获取的地震分析软件）中开发，用于各种类型的桥梁组件，包括非线性行为、设计变化和不同的不确定性来源。第三，将开发机器学习工具，对具有相似抗震性能的桥梁进行分组，通过这种方法，将桥梁库存细分为几个具有代表性的桥梁系统（rbs）。将推导出类脆弱性和参数化脆弱性模型，分别表示成组桥梁和单个桥梁的损伤潜力。最后，将设计一项基于网络的调查，以量化由于关闭决策和修复程序而导致的部件损坏与预期地震损失之间的关系。来自调查的响应数据将用于对每个RBS进行地震损失和恢复评估。该项目的成功将对当前设计实践的改进产生深远的影响，并为桥梁工程师提供对加拿大桥梁基础设施地震风险的更好的态势感知。拟议的研究将使灾后应急反应更加有效，并更快地恢复网络的移动性。它将支持有关有限资源分配的规划、改造和风险缓解决策，以最大限度地提高桥梁网络的抗震能力。所开发的框架和方法可以一致地应用于遭受不同类型自然灾害的其他结构系统。这项研究将为HQP提供跨学科培训，并有机会向不同的学生讲授科学和工程在提高基础设施弹性方面的作用。

Both southwestern British Columbia (BC) and St. Lawrence Valley are at significant risk of a strong earthquake. Bridge structures in these regions are seismically vulnerable due to aging and deterioration, outdated design and deferred maintenance. For example, nearly 40% of BC highway bridges were built before the first probabilistic seismic hazard map has been developed [1]. Moreover, the transportation infrastructure in BC and Quebec consists of a diverse collection of bridges that vary in material, type, connectivity, geometry and design details. Given the complexity, existing research falls short of providing a comprehensive, consistent and reliable seismic assessment of the bridge inventories in West and East Canada. The proposed program aims to enhance the seismic resilience of Canada's transportation infrastructure. An integrated framework to assess the seismic fragility, loss and resilience of regional bridge systems in BC and Quebec will be developed to achieve this goal. First, high-resolution bridge inventory databases will be built by surveying a large number of drawing files from British Columbia Ministry of Transportation and Infrastructure (BCMoT) and Transports Quebec (MTQ), respectively. Second, high-fidelity response and capacity models will be developed in OpenSees (an open access software for seismic analysis) for various types of bridge components that incorporate nonlinear behaviours, design variations and different sources of uncertainties. Third, machine learning tools will be developed to group bridges with similar seismic performance, through which the bridge inventory will be sub-divided into several representative bridge systems (RBSs). Class fragilities and parameterized fragility models will be derived to represent the damage potential of grouped and individual bridges, respectively. Last, a web-based survey will be devised to quantify the relationships between component damage and expected seismic losses due to closure decisions and repair procedures. Response data from the survey will be utilized to perform seismic loss and resilience assessment for each RBS. The success of this program will bear a profound impact on the improvement of current design practices and provide bridge engineers with improved situational awareness on the seismic risk of Canada's bridge infrastructure. The proposed research will enable a more effective post-hazard emergency response and a faster restoration of network mobility. It will support planning, retrofit and risk mitigation decisions regarding the allocation of limited resources to maximize the seismic resilience of bridge networks. The developed frameworks and methodologies can be congruently applied to other structural systems when subjected to different types of natural hazards. The research will provide interdisciplinary training for HQP and an opportunity to teach a diverse student body about the role of science and engineering in improving the infrastructure resilience.