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

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，监测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需要经常进行检查，或者根据需要尽快进行任何维修;在英国，2019年确定的维修工作积压将花费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，尽管有可能出现问题的警告，但意大利热那亚的莫兰迪大桥倒塌，造成43人死亡。坍塌并不是唯一的问题;气候变化导致的极端天气事件可以测试基础设施的性能，超出其极限，例如考虑洪水迫使桥梁关闭所造成的成本和不便。桥梁只是一个问题。海上风电（OW）行业降低了能源成本，增加了电力输出，现在引领了全球向清洁能源的转变。英国在OW能源方面处于全球领先地位，拥有约8吉瓦的容量，预计到2030年将超过25吉瓦，提供英国年电力需求的近三分之一，并有助于实现《气候变化法》（2008年）提出的2050年英国碳排放量减少80%的艰难目标。深水涡轮机的驱动需要新的资产管理、决策和控制方式，并限制操作/维护寿命成本。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。上述突出的问题在我们基础设施网络的所有元素中都很常见（本PG还将考虑电信基础设施;另一个关键测试平台），可以通过自动化健康监测来缓解。代替昂贵的、容易出错的人工检查，可以通过永久安装的传感器经济地提供诊断，连续收集结构数据并通过计算机算法对其进行解释。这一目标导致了结构健康监测（SHM）的研究学科，这是三十多年来学术活动的主题。尽管进行了大量的努力，但由于技术和操作方面的一些障碍，SHM尚未过渡到广泛使用。主要的技术障碍是：硬件系统的最佳实施;面对现场结构（如风，交通，桥梁）的混淆效应的可靠检测;缺乏损伤状态数据限制了机器学习用于SHM的潜力。业务障碍是：惯性-过度依赖保守的设计规范;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决策支持。进步的关键是从考虑个体结构转变为考虑群体。基于群体的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，并改变我们从传感器数据自动推断结构或结构网络状况的能力; ROSEHIPS将扩展和利用PBSHM，开发机器学习、传感和数字孪生技术，用于自动推断目前运行中的结构的健康状况，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力相结合。ROSEHIPS将提供开源软件系统，由现实的演示者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/保护自己的数据，同时利用给定的知识库。

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.