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.

此次合作的目标是提高常用于监测混凝土结构劣化的工具和技术的测试速度、再现性和准确性。我们将使用人工智能 (AI) 和深度学习 (DL) 开发用于现场视觉裂缝检查的自动化工具以及用于混凝土损伤评估的基于实验室的显微技术。我们的解决方案将包括快速可靠的工具，用于 i) 自动计算开裂指数 (CI)，使用智能手机应用程序（“应用程序”）实时分析结构目视检查期间现场拍摄的图片，以及 ii) 通过将图像分析 (IA) 技术应用于混凝土样本的高分辨率立体显微镜图像，自动计算损坏等级指数 (DRI)。 所提出的技术将显着增强两项重要的混凝土监测技术：a）CI计算​​，基于对结构的目视检查对损坏原因和程度进行初步评估；b）DRI计算，其中从结构中取出的核心样本在基于实验室的显微协议中进行检查，以诊断受内部膨胀反应（ISR）等遇险机制影响的混凝土损坏原因和程度。这两种方法都有重要的局限性：CI 需要现场定性调查，尽管作为初步指标很有用，但对于呈现多种损伤机制以及在不同程度的限制和暴露条件下的结构，其评估受影响混凝土损坏的能力仍不清楚。 DRI 的计算是一个耗时且基于专业知识的实验室程序。 CI 和 DRI 本质上都是主观的，并且在很大程度上依赖于执行分析人员的技能和经验。该项目将能够对加拿大普遍存在的关键基础设施（如桥梁、水坝和建筑物）的混凝土损坏的性质和程度进行快速、可靠的评估。反过来，这将实现及时且具有成本效益的预防性修复策略，从而确保更安全、更可靠的建筑环境。