Sensor and Data Analytics Systems for Structural Health Monitoring in Smart Infrastructure

人们对使用结构健康监测（SHM）来评估工程结构的状况非常感兴趣，尤其是与智能城市和新交通系统的基础设施相关。SHM已经在机械工程应用中使用了几十年，但在民用基础设施中越来越多地被广泛应用。然而，在这一领域大规模采用SHM主要存在两个挑战：（i）数据收集和（ii）数据解释和分析。为了确定可以从测量中获得哪些信息，迄今为止的大部分研究都集中在数据解释上，这项工作正在进行中。然而，对于实际实施，迫切需要能够（a）收集关于可能没有电力或通信的结构的必要数据以及（B）足够低的成本以使大规模数据收集在经济上可行的感测解决方案。例如，在许多桥梁的情况下，这是一个特别的问题，因为这些结构提供了至关重要的交通连接，特别是在农村;因此，提供低成本、实用的解决方案，能够持续监测性能是至关重要的。该项目旨在从多学科的角度应对这些挑战（连接信息和通信技术与工程主题，特别是将传感器和仪器仪表，人工智能，微电子设计和结构工程结合在一起。为此，已经确定了几个创新领域，这些领域的产出为低成本、长期的SHM现实世界实施提供了使能技术;- 通过模拟和实际实验，研究利用新传感模式进行监测的新方法-开发新的数据压缩算法和策略，以应对嵌入式传感器的挑战，（低功耗、低通信能力、低计算资源）-创建包含嵌入式人工智能和边缘计算的解决方案，通过分析计算的分散化来降低数据回程容量要求

There is considerable interest in employing Structural Health Monitoring (SHM) to evaluate the condition of engineering structures and is particularly relevant for infrastructure in smart cities and new transportation systems. SHM has been used within Mechanical Engineering applications for some decades now but is increasingly being looked at within Civil Infrastructure for widespread implementation. However, there exist largely 2 challenges to wide-scale adoption of SHM in this domain; (i) data collection and (ii) data interpretation and analysis.To identify what information can be garnered from measurements, much of the research to date has focused on data interpretation and this work is ongoing. However, for practical implementation there is an urgent need for sensing solutions that can (a) collect the necessary data on structures that may have no power or communications and (b) be sufficiently low cost to make large scale data collection financially practicable. In the case of many bridges, for example, this is a particular problem as these structures provide vital transport interconnections particularly in the countryside; therefore, providing low-cost, practical solutions that can monitor performance on a continual basis is vital.This project aims to tackle these challenges from a multidisciplinary perspective (bridging both Information and Communication technologies and Engineering themes, and in particular bringing together sensors and instrumentation, artificial intelligence, microelectronics design and structural engineering.) and to this end several areas for innovation have been identified with the outputs of these providing enabling technologies for low-cost, long-term SHM real-world implementations;- Carrying out research into new approaches to monitoring utilising new sensing paradigms through both simulation and practical experiments- Developing new data compression algorithms and strategies to tackle the challenges of embedded sensor's (low power, low communications capacities, low compute resources)- Creating solutions incorporating embedded artificial intelligence and edge computing to reduce data backhaul capacity requirements through the decentralisation of analytics computation