Ontology-Based Semantic Modeling for Steel Bridge Coating Systems

Research shows, even with the continuous development of coating technology, coating systems are still replaced before the expected lifespan. Recent advancement on data analytics (e.g., machine learning) offers an opportunity to leverage the wealthy historical data processed by State Departments of Transportation (DOTs) for improved understanding of coating premature failures. However, the coating-related data (e.g., coating condition data, coating application data) are stored in heterogeneous and unstructured formats (e.g., PDF) with limited data analytics capability. Therefore, better managing the data and knowledge related to coating systems is needed. To address the need, this paper presents a study that focuses on developing a semantic knowledge management system on steel bridge coating systems. As part of it, this paper focuses on presenting an ontology-based semantic model that formally represents and reasons about the knowledge of steel bridge coating systems. The model together with the knowledge management system will eventually support more systematic data-based analysis of coating premature failures, which will lead to new knowledge on factors that impact coating performance and potentially better practices that improve coating lifespans in the field.