A Big Data Management and Analytics Framework for Bridge Monitoring

Abstract

Bridge health monitoring involves massive volume of data with diverse and complex data types. While the data can potentially enhance the diagnostic and prognostic analyses of the structural condition of a bridge, the massive volume and the complexity of the data pose fundamental management and processing issues. Current practice of bridge health monitoring relies on proprietary servers and legacy data management tools, which are not well suited to meet today’s big data processing and management requirements. This paper discusses a cyberinfrastructure framework that takes advantages of state-of-the-art computing technologies to handle the data issues in bridge monitoring applications. We explore cloud computing as a scalable and reliable computing infrastructure service offered by cloud vendors. The use of a distributed NoSQL database system with cloud computing infrastructure facilitates scalability of data storage. In addition, the distributed computing resources in the cloud environment can be dynamically scaled on demand. The proposed framework is implemented for the monitoring of bridges located along the I-275 corridor in Michigan. The framework can effectively cope with changing demands for data management and processing in bridge monitoring.