Ontologies-Based Domain Knowledge Modeling and Heterogeneous Sensor Data Integration for Bridge Health Monitoring Systems
Li Ren, Tianjin Mo, Jianxi Yang, Shixin Jiang, Tong Li, Yiming Liu
Abstract
Structural health monitoring (SHM) systems have been extensively used to ensure the operational safety of long-span bridges. Large-scale bridge structural response and loading data observed from various sensors show obvious big data characteristics. However, serious “data island” problems, which exist in the conventional SHM solutions, inevitably limit the effects of sensory data analysis and information sharing. A unified bridge SHM semantic representation model is much in demand. By taking the advantages of Semantic Web technologies, this article presents a novel model, called the bridge structure and health monitoring ontology, to achieve fine-grained modeling of bridge structures, SHM systems, sensors, and sensory data from multiple perspectives. A bridge SHM big data platform is used to demonstrate the usefulness. Several representative data accessing and rule-based reasoning scenarios are employed as to illustrate the advantages of the proposed manner.