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，监控和维护我们的建筑物和交通网络是昂贵的。考虑到桥梁，通常由人类专家进行目视检查。没有足够的资源按照预期的频率进行检查，或根据需要尽快进行任何修复；在英国，2019年确定的积压维护工作将耗资67亿英镑。当资源紧张时，可能会犯错误，有时会造成悲惨的后果；2018年，尽管有人警告可能存在问题，但意大利热诺瓦的莫兰迪大桥坍塌，造成43人死亡。坍塌并不是唯一的问题；气候变化引发的极端天气事件可能会考验基础设施超出其极限的性能，例如，考虑到洪水迫使桥梁关闭所造成的成本和不便。桥梁只是一个令人担忧的问题。海上风能(OW)行业降低了能源成本，增加了发电量，现在是全球清洁能源变革的先驱。英国在OW能源方面处于全球领先地位，装机容量约为8千兆瓦，预计到2030年将超过25千兆瓦，提供了英国年电力需求的近三分之一，并帮助实现了《气候变化法》(2008)提出的2050年英国碳排放削减80%的艰难目标。对深水涡轮机的推动需要新的资产管理、决策和控制方法，并限制运行/维护生命周期成本。随着涡轮机数量、尺寸和容量的增加，这些问题变得更加重要。上面强调的问题在我们基础设施网络的所有元素中都是常见的(此PG还将考虑电信基础设施；另一个关键试验台)，可以通过自动化运行状况监控来缓解这些问题。与昂贵、容易出错的人工检查不同，永久安装的传感器可以经济地提供诊断，不断收集结构数据，并通过计算机算法进行解释。这一目标导致了结构健康监测(SHM)的研究学科，这是一个学术活动了30多年的学科。尽管进行了大量的努力，但由于许多技术和操作障碍，结构健康监测尚未过渡到广泛使用。主要技术障碍是：硬件系统的优化实施；面对现场结构(如风、交通、桥梁)的混杂影响时的自信检测；缺乏损伤状态数据限制了机器学习在结构健康监测中的潜力。操作障碍是：惯性-过度依赖保守的设计规范；信任-SHM系统必须像结构本身一样可靠；透明度-复杂的技术必须提供可解释的、安全的决策支持。进步的关键是从考虑个体结构转向考虑群体。基于人口的结构管理(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，改变我们从传感器数据自动推断结构或结构网络状况的能力；这取决于收集更广泛数据的能力，丰富到知识中。ROSEHIPS将扩展和利用PBSHM，开发机器学习、传感和数字孪生技术，用于自动推断当前运行的结构的健康状况，并推动未来更安全、更环保的结构的新标准。该计划汇集了完美的团队，将机器学习和高级数据分析方面的互补技能与新传感器系统的专业知识和对复杂基础设施系统的洞察结合在一起。ROSEHIPS将提供开源软件系统，通过现实的演示进行说明，并预先填充真实世界的数据。所有者/运营商将能够定制和保护/保护他们自己的数据，同时利用给定的知识库。

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

WHEN IS AN SHM PROBLEM A MULTI-TASK- LEARNING PROBLEM?

什么时候 SHM 问题是多任务学习问题？

• DOI: 10.12783/shm2023/36899
• 发表时间: 2023
• 作者: BEE S