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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749296
• 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.

世界各地的基础设施由于老化、运行条件、环境负荷和地震等突发极端事件而恶化。在加拿大，条件差和非常差的民用基础设施的重置成本估计为1 410亿加元。我的研究计划集中在降低开发，维护，运营和更换基础设施的整体成本，通过改善现有结构的健康和可靠性评估过程，称为结构健康监测（SHM）。我将开发结构健康监测技术的准确性水平，使它们能够作为工具，支持战略，战术和运营规划决策维护，修复和更换大规模的公共和私人资产。我最初的重点是桥梁，但目的是开发方法，也适用于建筑物，输电塔，风米尔斯，大坝结构。大多数以前的研究在SHM考虑一个单一的方法作为唯一的监测技术。我的计划侧重于开发一个整体的方法来结构健康监测，通过从各种监测方法的混合数据的整合。在我的程序中的核心方法是操作模态分析（OMA），一个输出的方法，已显示出显着的成功，在桥梁，建筑物，风米尔斯，大坝的SHM。我的目标是开发OMA与静态输入输出方法等互补方法的集成方法，以提高监控过程的准确性和效率。我将集中在OMA的理论发展，多模型识别，损伤识别，并使用SHM数据的结构可靠性评估。数值研究，实验室实验和全尺寸测试将被用来验证结果。我的研究计划，预计将有重大影响的基础研究，在结构健康监测，操作模态分析和结构损伤模型。此外，我的计划预计将有助于SHM的应用开发，特别是OMA方法，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营和公共实体提高详细结构评估的效率和频率。这将有助于他们优化操作程序以及维护和更换策略。