Autonomous Monitoring and Decision-Making System for Bridge Infrastructure

桥梁基础设施自主监测与决策系统

• 批准号: RGPIN-2017-03922
• 负责人: Narasimhan, Sriram
• 金额: $ 2.99万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660649
• 关键词: Autonomous; Monitoring; Decision; Making; System

There has been an unprecedented growth and adoption of sensors during the last two decades for the purpose of monitoring performance and health of civil infrastructure. This growth has resulted primarily from a combination of factors: unanticipated high-profile failures (e.g., 2013 CP Rail bridge in Calgary, 2007 I-35W Mississippi River bridge), the need to extend the service life of aging infrastructure, and an availability of relatively low-cost measurement and computing hardware. However, this growth has been largely lopsided, with our ability to measure and store data far surpassing our ability to extract meaningful information from them. As a result, scores of structures globally have been instrumented with sensors, while the key task of interpreting useful information from data remains inadequately addressed. Through the proposed research program, we aim to bridge the gap between measurements obtained through sensors and the interpretation of those measurements to produce crucial information regarding the health of structures, specifically bridge infrastructure, through the development of theoretical, computational, and algorithmic tools. This will enable the delivery of powerful technology into the hands of the end user, without the need for the user to have advanced expertise of the technology in order to use it effectively.******To successfully bridge this gap, the following items must be adequately addressed: (i) development of an integrated framework bridging measurements with knowledge (knowledge management); (ii) autonomous implementation, where such information is extracted without the need for intervention of an expert user. Such a framework will allow users to respond if a structural component of a bridge has been compromised following a shock event (e.g., earthquake or vehicular impact), and it will enable users to estimate the remaining lifetime of the structure so that a proper intervention strategy, such as detailed inspection or replacement, can be planned. Through the development of autonomous algorithms, such information can be extracted with a limited need for an expert user. ******The proposal outlines a novel approach to integrate measurements with decision-making, made possible through a combination of theoretical, algorithmic and experimental developments. The proposed research program is transformative by trying to address the crucial gap between measurements and decision-making, thereby reducing the societal risk through the use of sensors. A successful implementation of this technology would enable us to not only save lives from catastrophic failures, but also allow us to extend the life of existing infrastructure, thereby saving capital costs. The inter-disciplinary training plan will train engineers who are equipped to use technology outside the traditional confines of civil engineering such as electronics and signal processing.**

在过去的二十年中，传感器以前所未有的速度增长和采用，用于监测民用基础设施的性能和健康状况。这一增长主要是由以下因素综合造成的：意外的高调失败（例如，2007年I-35 W密西西比河大桥），需要延长老化基础设施的使用寿命，以及相对低成本的测量和计算硬件的可用性。然而，这种增长在很大程度上是不平衡的，我们测量和存储数据的能力远远超过了我们从中提取有意义信息的能力。因此，全球数十个结构都装有传感器，而从数据中解释有用信息的关键任务仍然没有得到充分解决。通过拟议的研究计划，我们的目标是弥合通过传感器获得的测量值和这些测量值的解释之间的差距，以产生有关结构健康的关键信息，特别是桥梁基础设施，通过理论，计算和算法工具的发展。这将使强大的技术交付到最终用户手中，而不需要用户拥有先进的技术专业知识，以有效地使用它。为了成功地弥合这一差距，必须适当处理以下事项：㈠制定一个综合框架，将计量与知识（知识管理）联系起来; ㈡自主执行，即在不需要专家用户干预的情况下提取这类信息。这样的框架将允许用户在桥梁的结构部件在冲击事件（例如，它将使用户能够估计结构的剩余寿命，以便可以计划适当的干预策略，例如详细检查或更换。通过自主算法的发展，这样的信息可以提取与专家用户的有限需求。** 该提案概述了一种将测量与决策相结合的新方法，通过理论，算法和实验发展的结合成为可能。拟议的研究计划具有变革性，试图解决测量和决策之间的关键差距，从而通过使用传感器降低社会风险。这项技术的成功实施将使我们不仅能够从灾难性故障中挽救生命，而且还能够延长现有基础设施的寿命，从而节省资本成本。跨学科培训计划将培训那些有能力使用传统土木工程范围以外的技术的工程师，如电子和信号处理。