Artificial Intelligence for the Condition Assessment of Critical Infrastructure

用于关键基础设施状况评估的人工智能

• 批准号: 569563-2021
• 负责人: MorettiSanchez, LeandroFrancisco
• 金额: $ 1.76万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723206
• 关键词: Artificial; Intelligence; Condition; Assessment; Critical

The goals of this partnership are to improve testing speed, reproducibility, and accuracy of tools and techniques commonly used for monitoring structural deterioration of concrete. We will use artificial intelligence (AI) and deep learning (DL) to develop automated tools for on-site visual crack inspection and for lab-based microscopic techniques for concrete damage assessment. Our solutions will include rapid and reliable tools to i) automate computation of the cracking index (CI), using a smartphone application ("app") to analyze, in real time, pictures taken in-situ during visual inspections of structures, and ii) to automate the Damage Rating Index (DRI) calculation by applying image analysis (IA) techniques to high-resolution stereomicroscope images of concrete specimens. The proposed technology will significantly enhance two important concrete monitoring techniques: a) computation of the CI, a preliminary assessment of cause and extent of damage based on visual inspection of structures, and b) computation of the DRI, in which core samples retrieved from structures are inspected in a lab-based microscopic protocol to diagnose cause and extent of damage in concrete affected by distress mechanisms such as internal swelling reactions (ISR). Both methods have important limitations: CI requires in-situ qualitative investigations and, although useful as a preliminary indicator, its ability to assess damage of affected concrete is still unclear for structures presenting multiple distress mechanisms and under distinct degrees of confinement and exposure conditions. Computation of the DRI is a time-consuming, expertise-based, lab procedure. Both CI and DRI are subjective in nature and rely heavily on the skill and experience of the person performing the analysis. This project will enable rapid and reliable assessments of the nature and extent of concrete damage in critical infrastructure prevalent in Canada, such as bridges, dams, and buildings. This will, in turn, enable timely and cost-effective preventative rehabilitation strategies, thus ensuring a safer and more reliable built environment.

该合作伙伴关系的目标是提高测试速度，可重复性以及通常用于监测混凝土结构恶化的工具和技术的准确性。我们将使用人工智能（AI）和深度学习（DL）来开发自动化的工具，以进行现场视觉裂纹检查和基于实验室的显微镜技术进行混凝土损害评估。我们的解决方案将包括i）使用智能手机应用程序（“应用程序”）自动计算开裂索引（CI）的快速可靠工具，以实时分析结构的视觉检查期间的现场图片，以及II ）通过将图像分析（IA）技术应用于混凝土标本的高分辨率立体显微镜图像来自动化损伤等级指数（DRI）计算。 所提出的技术将显着增强两种重要的具体监控技术：a）CI的计算，基于结构的目视检查的原因和损害的初步评估，b）DRI的计算，其中从结构中检索到的核心样品中在基于实验室的显微镜方案中检查，以诊断受遇险机制（例如内部肿胀反应（ISR））影响的混凝土损害程度和程度。这两种方法都有重要的局限性：CI需要原位定性研究，尽管作为初步指标，但它评估受影响混凝土损害的能力仍然不清楚，在呈现多种遇险机制并在不同程度的监禁和暴露条件下的结构中尚不清楚。 DRI的计算是一个耗时的，基于专业的实验室程序。 CI和DRI本质上都是主观的，并且严重依赖执行分析的人的技能和经验。该项目将对加拿大普遍存在的关键基础设施（例如桥梁，大坝和建筑物）中的具体基础设施的性质和程度进行快速可靠的评估。反过来，这将使及时且具有成本效益的预防性康复策略，从而确保更安全，更可靠的建筑环境。