Revolutionising Operational Safety and Economy for High-value Infrastructure using Population-based SHM (ROSEHIPS)

使用基于人口的 SHM (ROSEHIPS) 彻底改变高价值基础设施的运营安全性和经济性

• 批准号: EP/W005816/1
• 负责人: Keith Worden
• 金额: $ 806.16万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW005816%2F1
• 关键词: Revolutionising; Operational; Safety; Economy; value

Healthy infrastructure is critical in ensuring the continued health of UK society and the economy. Unfortunately, monitoring and maintaining our buildings and transport network is expensive. Considering bridges, inspection is usually carried out visually by human experts. There are not the resources to carry out the inspections as often as desired, or to make any repairs as quickly as needed; in the UK a backlog of maintenance works, identified in 2019, will cost £6.7bn. When resources are stretched, mistakes can be made, sometimes with tragic consequences; in 2018, despite warnings about possible problems, the Morandi Bridge in Genova, Italy, collapsed at a cost of 43 lives. Collapse is not the only problem; extreme weather events driven by climate change can test the performance of infrastructure beyond its limits e.g. consider the cost and inconvenience caused by bridge closures forced by flooding.Bridges are only one concern. The offshore wind (OW) sector has driven down energy costs and increased power output, and now pioneers a global change to clean energy. The UK leads globally in OW energy, with ~8 GW of capacity, expected to exceed 25 GW by 2030, providing almost one third of the UK's annual electricity demand and helping meet the Climate Change Act's (2008) difficult 2050 target for an 80% cut in UK carbon output. The drive for turbines in deeper water demands new ways of asset management, decision making and controlling and limiting operation/maintenance lifetime costs. As turbines increase in numbers, size, and capacity, these issues become even more important.The issues highlighted above are common across all elements of our infrastructure network (this PG will also consider telecoms infrastructure; another key test bed) and can be mitigated by automating the health monitoring. Instead of expensive, error-prone, human inspections, diagnoses can be provided economically by permanently-installed sensors, collecting structural data continuously and interpreting it via computer algorithms. This aim has led to the research discipline of Structural Health Monitoring (SHM), a subject of academic activity for over three decades. Despite intensive effort, SHM has not transitioned to widespread use because of a number of barriers - technical and operational.The main technological barriers are: optimal implementation of hardware systems; confident detection in the face of confounding effects for in situ structures e.g. wind, traffic, for bridges; lack of damage-state data limiting the potential of machine learning for SHM. The operational barriers are: inertia - over-reliance on conservative design codes; trust - the SHM system must be as reliable as the structure itself; transparency - complex technology must deliver interpretable, secure decision support. The key to progress is to shift from thinking about individual structures to thinking about populations.Population-Based SHM (PBSHM) is a game-changing idea, emerging in the UK very recently, with the potential to overcome the technological barriers above and transform our ability to automatically infer the condition of a structure, or a network of structures, from sensor data; this depends on an ability to collect a broader range of data, enriched into knowledge.ROSEHIPS will extend and exploit PBSHM, developing machine learning, sensing and digital twin technology for automated inference of health for structures in operation now, and drive new standards for safer, greener structures in future. The Programme brings together the perfect team, mixing complementary skills in machine learning and advanced data analysis with expertise in new sensor systems and insight into complex infrastructure systems.ROSEHIPS will provide open-source software systems, illustrated by realistic demonstrators and pre-populated with real-world data. Owners/operators will be able to customise and protect/secure their own data, while exploiting the knowledge base given.

健康的基础设施对于确保英国社会和经济的持续健康至关重要。不幸的是，监视和维护我们的建筑物和运输网络很昂贵。考虑到桥梁，通常以人为专家的视觉进行检查。没有资源可以根据需要进行检查，或者根据需要进行任何维修；在英国，2019年确定的维护工程的积压为67亿英镑。当资源拉伸时，可能会犯错误，有时会带来悲惨的后果； 2018年，目的地警告可能出现问题，意大利热那瓦市的莫兰迪桥（Morandi Bridge）倒塌了43人的生命。崩溃不是唯一的问题。气候变化驱动的极端天气事件可以测试超出其极限的基础设施的性能，例如考虑一下洪水强迫的桥梁闭合所带来的成本和影响。桥只是一个问题。离岸风（OW）部门降低了能源成本并增加了功率输出，现在开拓了全球变化对清洁能源的变化。英国的OW能源领先，到2030年，其产能约为8吉瓦，预计将超过25吉瓦，提供了英国的年度电力需求的近三分之一，并帮助满足《气候变化法》（2008年）的2050年难度目标，即在英国碳输出量下降80％。在更深的水中涡轮机的驱动器需要新的资产管理，决策以及控制和限制操作/维护寿命成本的方式。随着涡轮机的数量，大小和容量的增加，这些问题变得更加重要。上面突出的问题在我们的基础设施网络的所有元素中都很常见（该PG也将考虑电信基础设施；另一个钥匙测试台），可以通过自动进行健康监测来缓解。可以通过永久安装的传感器连续收集结构数据并通过计算机算法来对其进行经济提供诊断，而是经济地提供诊断。这个目标导致了结构性健康监测（SHM）的研究学科，这是一项超过三十年的学术活动。尽管努力进行了激烈的努力，但由于许多障碍 - 技术和操作，SHM并未过渡到广泛使用。主要的技术障碍是：硬件系统的最佳实现；面对原位结构的混杂作用，自信检测，例如风，交通，用于桥梁；缺乏损害状态数据限制了SHM机器学习的潜力。操作障碍是：惯性 - 对保守设计代码的过度依赖；信任 - SHM系统必须与结构本身一样可靠；透明度 - 复杂的技术必须提供可解释的，安全的决策支持。进步的关键是要从思考单个结构转变为对人群的思考。基于人口的SHM（PBSHM）是一个改变游戏规则的想法，最近在英国出现，有可能克服上述技术障碍并改变我们自动推断结构状况的能力，或者从传感器数据中推断出结构网络或结构网络；这取决于能够收集更广泛数据的能力，并丰富了知识。功能将扩展和探索PBSHM，开发机器学习，敏感性和数字双技术，以自动化现在运营的结构的健康推断，并为将来的更安全，绿色的结构推动新的标准。该计划将完美的团队汇集在一起​​，将机器学习和高级数据分析的完整技能与新传感器系统的专业知识相结合，并深入了解复杂的基础架构系统。功能将提供开源软件系统，由现实的示威者说明，并预先填充现实世界中的数据。所有者/运营商将能够自定义和保护/保护自己的数据，同时利用给出的知识库。

项目成果

Combining Transfer Learning and Numerical Modelling to Deal with the Lack of Training Data in Data-Based SHM

结合迁移学习和数值建模解决基于数据的 SHM 中训练数据的缺乏

• DOI: 10.2139/ssrn.4674218
• 发表时间: 2024
• 作者: Battu R

On the application of population-based structural health monitoring in aerospace engineering.

• DOI: 10.3389/frobt.2022.840058
• 发表时间: 2022
• 期刊: FRONTIERS IN ROBOTICS AND AI
• 影响因子: 3.4
• 作者: Brennan, Daniel S;Gosliga, Julian;Gardner, Paul;Mills, Robin S;Worden, Keith
• 通讯作者: Worden, Keith

Dynamics of Civil Structures, Volume 2 - Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023

土木结构动力学，第 2 卷 - 第 41 届 IMAC 会议论文集，2023 年结构动力学会议和博览会

• DOI: 10.1007/978-3-031-36663-5_19
• 发表时间: 2024
• 作者: Brennan D

Data Science in Engineering, Volume 10 - Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023

工程中的数据科学，第 10 卷 - 第 41 届 IMAC 会议论文集，2023 年结构动力学会议暨博览会

• DOI: 10.1007/978-3-031-34946-1_7
• 发表时间: 2023
• 作者: Bee S

DETECTION, LOCALISATION, AND QUANTIFICATION OF BOLT LOOSENESS IN AN ALUMINIUM PLATE USING LAMB WAVE ANALYSIS

使用兰姆波分析检测、定位和量化铝板中的螺栓松动

• DOI: 10.12783/shm2023/36949
• 发表时间: 2023
• 作者: BRASSINGTON A