CPS: Synergy: Collaborative Research: Enhanced Structural Health Monitoring of Civil Infrastructure Systems by Observing and Controlling Loads using Cyber-Physical Systems

通过使用数字物理系统框架观察和控制负荷来加强民用基础设施系统的结构健康监测国家的经济繁荣有赖于庞大的民用基础设施系统网络。不幸的是，这些基础设施系统中的很大一部分正迅速接近其预期设计寿命的末期。正如美国最近发生的灾难性桥梁坍塌事件所揭示的那样，国家公路桥梁网络特别容易因年龄而恶化。目前存在两大瓶颈，严重限制了现有桥梁健康管理方法的有效性。首先，人们没有很好地理解卡车反复装载与长期结构恶化之间的因果关系。其次，目前的管理方法依赖于目测，只提供有关桥梁健康的定性信息，并在检查后的决策中引入主观性。该项目旨在解决这些主要的瓶颈，通过发展一个网络物理系统(CPS)，旨在监测公路桥梁的健康状况，控制重型卡车对桥梁施加的荷载，并为视觉检查员提供数据驱动的桥梁健康评估的量化信息。创建的CPS框架将通过加强公共安全，同时显著提高基础设施管理方法的成本效益，对国民经济产生巨大影响。该项目还将创建面向公众的研究生级别课程，重点关注CPS技术，并吸引来自代表性不足群体的市中心中学生，为他们在科学、技术、工程和数学(STEM)领域的职业生涯做好准备。该研究项目的总体目标是创建一个可扩展的、健壮的CPS框架，用于观察和控制与固定物理系统进行异步和瞬时交互的移动代理。虽然这类问题在许多工程学科中都有发现，但该项目的重点是公路桥梁的健康管理。与桥梁健康相关的流动代理人是装载并在桥梁中引入长期损害的卡车和目测桥梁的检查员。设计一个强大的CPS框架的任务将受到所涉及的代理人高度瞬变的性质的挑战。具体地说，卡车和桥梁之间相互作用的压缩时间导致对卡车装载的观察、量化和控制的时间限制很紧。该项目将依靠临时无线通信，将移动代理(卡车和检查员)中嵌入的传感器与安装在桥上的无线传感器以及位于互联网上的专用于基于云的分析的服务器无缝集成。该项目将设计CPS框架，以基于流入CPS框架的传感器数据来量化实时卡车负载。将为CPS框架创建一个分布式计算架构，以自动分解计算任务，以大幅提高该框架的数据处理能力的速度和效率。最后，CPS框架将建立对移动代理的自组织反馈控制，以控制移动代理与静止系统的交互。特别是，仪表化卡车的反馈控制允许CPS框架控制施加在桥梁上的载荷，以改善健康评估。CPS框架将进一步扩展，根据对收集的传感器数据的严格分析，向检查人员提供建议的检查行动，从而控制目视检查过程。CPS框架的智力意义在于，它首次观察和控制公路桥梁上的卡车负载，同时创建了一种全新的数据驱动范式，以更准确地评估基础设施系统的健康状况。

Enhanced Structural Health Monitoring of Civil Infrastructure Systems by Observing and Controlling Loads using a Cyber-Physical System Framework The economic prosperity of the nation is dependent on vast networks of civil infrastructure systems. Unfortunately, large fractions of these infrastructure systems are rapidly approaching the end of their intended design lives. The national network of highway bridges is especially vulnerable to age-based deterioration as revealed by recent catastrophic bridge collapses in the United States. Two major bottlenecks currently exist that severely limit the effectiveness of existing bridge health management methods. First, the causal relationship between repeated truck loading and long-term structural deterioration is not well understood. Second, current management methods are reliant on visual inspections which only provide qualitative information regarding bridge health and introduce subjectivity in post-inspection decision making. This project aims to resolve these major bottlenecks by advancing a cyber-physical system (CPS) designed to monitor the health of highway bridges, control the loads imposed on bridges by heavy trucks, and provide visual inspectors with quantitative information for data-driven bridge health assessments. The CPS framework created will have enormous impact on the national economy by enhancing public safety while dramatically improving the cost-effectiveness of infrastructure management methods. The project will also create publically available graduate-level course curricula focused on CPS technology and engages inner-city middle-school students from underrepresented groups to prepare them to pursue careers in the science, technology, engineering, and mathematics (STEM) fields. The overarching goal of the research project is to create a scalable and robust CPS framework for the observation and control of mobile agents that asynchronously and transiently interact with a stationary physical system. While this class of problem is found throughout many engineering disciplines, the project focuses on the health management of highway bridges. The mobile agents relevant to bridge health are the trucks that load and introduce long-term damage in the bridge and inspectors who visually inspect the bridge. The task of devising a robust CPS framework will be challenged by the highly transient nature of the agents involved. Specifically, the compressed time of interaction between the truck and bridge results in tight time constraints on observation, quantification and control of the truck's loading. The project will rely on ad-hoc wireless communications to seamlessly integrate sensors embedded in the mobile agents (trucks and inspectors) with wireless sensors installed on the bridge and with servers dedicated to cloud-based analytics located on the Internet. The project will design the CPS framework to quantify in real-time truck loads based on sensor data streaming into the CPS framework. A distributed computing architecture will be created for the CPS framework to automate the decomposition of computational tasks in order to dramatically improve the speed and efficiency of the framework's data processing capabilities. Finally, the CPS framework will establish ad-hoc feedback control of the mobile agents in order to control mobile agent-stationary system interactions. In particular, feedback control of an instrumented truck allows the CPS framework to control the loads imposed on the bridge for improved health assessments. The CPS framework will be further extended to control visual inspection processes by providing inspectors with recommend inspection actions based on rigorous analysis of collected sensor data. The intellectual significance of the CPS framework is that it observes and controls truck loads on highway bridges for the first time while creating an entirely new data-driven paradigm for more accurate health assessment of infrastructure systems.