Health monitoring, damage prognosis and possible futures: applying machine learning to the appraisal and analysis of existing structures

健康监测、损伤预测和可能的未来：将机器学习应用于现有结构的评估和分析

• 批准号: 2274475
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2274475
• 关键词: Health; monitoring; damage; prognosis; possible

The repair and maintenance of existing structures represents a significant cost, both now and in the future. This has been estimated as being in the region of £15 billion per year in the United Kingdom alone. Moreover, existing structures contain significant quantities of embodied carbon and there is thus an environmental and economic imperative to facilitating their continued effective service when possible. Many structures exist that that have exceeded, or will soon exceed their intended design life. For some years it has been recognised that effective ways of tracking the condition and health of these structures would be valuable.The future of structural appraisal will rely on effective decision-making using large data sets. The data will be collected from instruments embedded within structures (at the time of construction or retrofitted) and from state of the art surveying techniques. It has been recognised that there is significant potential for applying machine learning techniques to these large data sets derived from structures to assist engineers in pattern-recognition, detection and diagnosis of defects, and in understanding potential future scenarios.The implementation of structural health monitoring using machine learning to make effective judgements regarding the prognosis of existing structures has been called a "grand challenge problem for engineers in the twenty-first century". It has been suggested that implementation of such an approach would add significantly to the value of built assets. The development of a functional framework for assisting decision-making in real world scenarios is therefore a worthwhile goal.The project will focus on the development of integrated instrumentation, measurement and data processing methodologies for evaluating health and integrity for service of existing structures, in additon to future prognosis. This will complement traditional engineering approaches and offer assistance to engineers assessing structures based on limited information. Opportunities for applying machine learning techniques to support and enable this process will be investigated. Collaboration with relevant UK and international industrial partners will be pursued. It is hoped that the project will be pivotal to establishing a collaborative international laboratory for invesitgation of such problems, based at the University of Edinburgh.

无论是现在还是将来，现有结构的维修和维护都会产生巨大的成本。据估计，仅在英国每年的花费就达到 150 亿英镑。此外，现有结构含有大量的隐含碳，因此在可能的情况下促进其持续有效的服务具有环境和经济上的必要性。许多结构已经超过或即将超过其预期设计寿命。多年来，人们已经认识到跟踪这些结构的状况和健康状况的有效方法将是有价值的。结构评估的未来将依赖于使用大数据集的有效决策。数据将从嵌入结构内的仪器（在施工或改造时）和最先进的测量技术中收集。人们认识到，将机器学习技术应用于这些源自结构的大数据集，以帮助工程师进行模式识别、缺陷检测和诊断，以及理解未来潜在的场景，具有巨大的潜力。利用机器学习实现结构健康监测，对现有结构的预后做出有效的判断，被称为“二十一世纪工程师的巨大挑战问题”。有人建议，实施这种方法将大大增加建成资产的价值。因此，开发一个在现实世界场景中辅助决策的功能框架是一个有价值的目标。该项目将重点开发集成仪器、测量和数据处理方法，用于评估现有结构的健康和完整性，以及未来的预测。这将补充传统的工程方法，并为工程师根据有限的信息评估结构提供帮助。我们将研究应用机器学习技术来支持和实现这一过程的机会。将寻求与相关英国和国际工业伙伴的合作。希望该项目将成为在爱丁堡大学建立一个研究此类问题的国际合作实验室的关键。