Crack-Informed Structural Modelling and Assessment of Existing Concrete Infrastructure

加拿大的民用基础设施，如维尔玛丽隧道(蒙特利尔)和加德纳高速公路(多伦多)，会造成经济中断，并带来不必要的安全风险，因为它们恶化的速度超出了我们的更换能力。对于钢筋混凝土(RC)建筑，通常使用视觉基准(如混凝土裂缝长度、宽度和模式)结合条件评级标准对结构健康进行定性评估，以对损伤进行分类。然而，准确地从可视RC裂缝中推断遇险严重程度是极具挑战性的，需要考虑在评估过程中通常被忽略的结构和损坏细节。为了确保可用于管理国家建筑环境的有限资源用于最需要它们的地方，需要进行重大研究，以实现可靠的RC结构评估。这意味着新的损伤检查技术、创新和有能力的决策工具，以及利用可观察到的损伤的新的建模程序。这项研究计划的长期目标是开发能够准确表征已建RC基础设施中可识别的混凝土损伤的检测方法，以及利用检测数据进行可靠的基于损伤的结构评估的工具。这些技术的应用领域很广泛，在已经采用基础设施检查的行业中，非常有利于未来的采用和实施。在接下来的五年里，我的团队将：1)探索基于图像的分析技术在建立从RC基础设施提取裂缝特征响应数据的现场有利方法方面的作用；2)通过执行涉及具有现场代表性的RC组件的有针对性的测试活动，填补阻碍高级裂缝评估程序开发的关键数据空白；以及3)通过开发基于裂缝的非线性有限元分析技术和使用可识别的混凝土裂缝特征作为输入的评估工具，定义当前最先进的RC基础设施评估的创新替代方案。基于图像的裂缝检测技术可以提供更好的分辨率监测，提取目前尚未获得的裂缝特征数据，并消除过程中的主观性，从而极大地改进传统的钢筋混凝土结构检测方法。通过这项研究开发的建模工具将有助于确保使用可靠的评估技术来管理老化的基础设施，这是加拿大工程师目前经常面临的日益严峻的挑战。这一及时的研究将提供原创的和有影响力的结构评估技术，以及具有直接相关技能的HQP，以确保识别加拿大出现严重损坏的已建结构，并确保用于基础设施维护的资源得到最佳分配。

Civil infrastructure in Canada, such as the Ville-Marie tunnel (Montreal) and the Gardiner Expressway (Toronto), cause economic disruptions and pose undue safety risks as they deteriorate at rates exceeding our ability to replace them. For reinforced concrete (RC) construction, structure health is typically assessed in a qualitative manner using visual benchmarks such as concrete crack lengths, widths, and patterns in combination with condition-rating criteria to classify damage. However, accurately inferring distress severity from visual RC cracking is extremely challenging, requiring consideration of structure and damage details that are typically ignored in the assessment process. To ensure that the limited resources available for managing the nation's built environment are directed where they are needed most, significant research is required to enable reliable RC structural assessments. This means new damage inspection technologies, innovative and capable decision making tools, and novel modelling procedures that utilize observable damage. The long-term goal of this research program is to develop inspection methods that can accurately characterize discernible concrete damage in built RC infrastructure, and tools that employ inspection data to enable reliable damage-based structural assessments. The application areas for these technologies are widespread and, in industries that already employ infrastructure inspections, are highly-conducive to future adoption and implementation. Through the next five years my team will: 1) explore the role of image-based analysis techniques for establishing field-conducive methods of extracting crack feature response data from RC infrastructure, 2) fill key data gaps that are impeding the development of advanced crack-based assessment procedures through the performance of targeted testing activities involving field-representative RC components, and 3) define innovative alternatives to the current state-of-the-art in the assessment of damaged RC infrastructure through the development of crack-based nonlinear finite element analysis techniques and assessment tools that employ discernible concrete cracking features as input. Image-based crack measurement techniques have the potential to significantly advance traditional RC structure inspection methods by providing better resolution monitoring, extracting crack feature data not currently obtained, and removing subjectivity from the process. The modelling tools developed through this research will help ensure that the management of ageing infrastructure, a growing challenge that Canadian engineers are now facing regularly, is done using reliable assessment techniques. This timely research will provide original and impactful structural assessment technologies, and HQP with directly relevant skillsets, to ensure that built structures in Canada exhibiting severe distress are identified, and that resources for infrastructure maintenance are optimally allocated.