SenStore: A Scalable Cyberinfrastructure Platform for Implementation of Data-to-Decision Frameworks for Infrastructure Health Management

Abstract

Structural monitoring systems are an objective and quantitative-based management tool that have been developed to assist structure owners with their diagnostic and prognostic decision making processes. As sensing technologies mature, the deployment of permanent sensing arrays in structures is becoming more popular, resulting in increasing volumes of sensing data. Comprehensive data-management systems are needed to host and curate heterogeneous data sets associated with structural asset management including structural information (design and inspection information) and massive amounts of sensing data. More importantly, the data-management system must also provide a functional but secure means of allowing software clients to perform analytics on the data contained in the system. In this paper, a scalable and secure cyberinfrastructure platform termed SenStore is introduced for the management and automated analysis of sensing data. SenStore includes a hybrid database architecture that maximizes query speed by storing structural metadata in a relational database, sensor data in a hierarchical data format repository, and multimedia files such as inspector photos in a flat file system. The design of SenStore is presented, followed by description of its application to manage and process data associated with a long-term wireless monitoring system installed on the Telegraph Road Bridge (Monroe, Michigan). To illustrate the effectiveness of SenStore in tracking the performance of monitored bridges, this study automates the process of extracting modal data from the Telegraph Road Bridge sensor data stored in SenStore and using modal parameters to update a finite-element model that is integral to the bridge owner’s decision-making processes.