A HIGH-VOLUME PROCESSING FRAMEWORK FOR HUMAN-STRUCTURE INTERFACES IN SMART INFRASTRUCTURE

Monitoring the behavior and performance of engineered structures has become increasingly desirable due to the value such information offers for occupant safety and structural maintenance. Vibration data collected from accelerometers has proven to be an effective tool to perform this type of monitoring. While some monitoring activities can occur autonomously, it is often necessary for humans to interact with the data to discern the need for additional evaluation. In large structures or those with a dense sensor deployment, continuously collected vibration data can quickly grow to massive scales. Consequently, the evaluation of structural performance is often limited by the ability of a system to efficiently process and present large volumes of data. To overcome this challenge, this paper presents a framework to process, store, and visualize data using open-source distributed computing technologies. The framework utilizes a publish-subscribe messaging queue deployed across multiple partitions to consume data in parallel, improving the rate of ingestion. Ingested data is stored in a structured format using a NoSQL database that provides high availability, scalability, and performance. The stored data acts as the source for webbased visualization. This setup provides a high degree of adaptability, allowing meaningful visualizations to be implemented for various forms of smart infrastructure monitoring tasks. The capabilities of the resultant human-infrastructure interface are demonstrated using Goodwin Hall, a five-story building instrumented with 225 hard-wired accelerometers. This case study showcases visualizations that enable users to perform real-time assessment of frequency domain features and efficiently identify notable excitation events during the building's history.