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

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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.