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

无论是现在还是将来，现有结构的维修和保养都是一笔巨大的费用。据估计，仅在英国，这一数字每年就在150亿英镑左右。此外，现有结构含有大量的碳，因此在环境和经济方面必须尽可能促进其继续有效服务。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，追踪这些结构的状况和健康状况的有效方法是有价值的。未来的结构评估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（在建造或改造时）和最先进的测量技术中收集。人们已经认识到，将机器学习技术应用于这些来自结构的大型数据集，以协助工程师进行模式识别，检测和诊断缺陷，以及理解潜在的未来场景，具有巨大的潜力。利用机器学习实施结构健康监测，对现有结构的预测作出有效判断，被称为“ 21世纪工程师面临的重大挑战问题”。有人建议，实施这种方法将大大增加已建资产的价值。因此，开发一个在现实世界场景中协助决策的功能框架是一个有价值的目标。该项目将侧重于发展综合仪器、测量和数据处理方法，以评估现有结构服务的健康和完整性，以及未来的预测。这将补充传统的工程方法，并为工程师在有限信息的基础上评估结构提供帮助。将研究应用机器学习技术来支持和实现这一过程的机会。将与相关的英国和国际工业伙伴开展合作。人们希望，这个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。

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.