Integration of Hybrid Inspection Data into Structural Health Monitoring

将混合检测数据集成到结构健康监测中

• 批准号: RGPIN-2017-04356
• 负责人: Arjomandi, Kaveh
• 金额: $ 1.6万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710982
• 关键词: Integration; Hybrid; Inspection; Data; into

Infrastructure around the world deteriorates as a result of aging, operational conditions, environmental loads, and sudden extreme events such as earthquakes. In Canada the civil infrastructure in poor and very poor conditions has been estimated to have a replacement cost of $141 billion. My research program concentrates on reducing the overall cost of development, maintenance, operation, and replacement of infrastructure by improving the process of health and reliability assessment of existing structures, known as Structural Health Monitoring (SHM). I will develop structural health monitoring techniques to a level of accuracy that enables them to be applied as tools supporting strategic, tactical and operational planning decisions for maintenance, repair, and replacement of large scale public and private assets. My initial focus is on bridges, but the intent is to develop methods that also apply to buildings, transmission towers, wind mills, and dam structures. Most previous research in SHM considers a single method as the sole monitoring technique. My program focuses on developing a holistic approach to structural health monitoring through the integration of hybrid data from various monitoring methods. The core method in my program is Operational Modal Analysis (OMA), an output-only method that has shown significant success in the SHM of bridges, buildings, wind mills, and dams. My goal is to develop methodologies for the integration of OMA with complementary approaches such as static input-output methods in order to improve the accuracy and efficiency of the monitoring process. I will focus on theoretical developments of OMA, multi-model identification, damage identification, and reliability assessment of structures using SHM data. Numerical studies, laboratory experiments, and full-scale tests will be used to verify the results. My research program is expected to have significant impact on the fundamental research in structural health monitoring, operational modal analysis, and structural damage models. Additionally, my program is expected to contribute to the application development of SHM and in particular the OMA method, with a focus on large-scale monitoring systems. Activities described in this Discovery Grant application will help private and public entities that are responsible for safety and operation of civil engineering infrastructure to improve the efficiency and frequency of detailed structural assessments. This will assist them to optimize the operational procedures as well as the maintenance and replacement strategies.

由于老化、运行条件、环境负荷以及地震等突发极端事件，世界各地的基础设施不断恶化。在加拿大，条件恶劣和非常恶劣的民用基础设施的重置成本估计为 1,410 亿加元。我的研究项目集中于通过改进现有结构的健康和可靠性评估过程（称为结构健康监测（SHM））来降低基础设施的开发、维护、运营和更换的总体成本。 我将开发结构健康监测技术，使其达到一定的准确度，使其能够作为支持战略、战术和运营规划决策的工具，以维护、维修和更换大型公共和私人资产。我最初的重点是桥梁，但目的是开发也适用于建筑物、输电塔、风车和水坝结构的方法。 大多数先前的健康管理研究都将单一方法视为唯一的监测技术。我的项目重点是通过整合来自各种监测方法的混合数据来开发结构健康监测的整体方法。我的程序中的核心方法是操作模态分析 (OMA)，这是一种纯输出方法，在桥梁、建筑物、风车和水坝的健康监测中取得了巨大成功。我的目标是开发将 OMA 与静态输入输出方法等补充方法相集成的方法，以提高监测过程的准确性和效率。我将重点关注 OMA 的理论发展、多模型识别、损伤识别以及使用 SHM 数据进行结构的可靠性评估。将使用数值研究、实验室实验和全面测试来验证结果。 我的研究项目预计将对结构健康监测、运行模态分析和结构损伤模型的基础研究产生重大影响。此外，我的项目预计将为 SHM，特别是 OMA 方法的应用开发做出贡献，重点是大型监测系统。本发现补助金申请中描述的活动将帮助负责土木工程基础设施安全和运营的私人和公共实体提高详细结构评估的效率和频率。这将帮助他们优化操作程序以及维护和更换策略。