Schema-Agnostic Data Type Inference and Validation for Exchanging JSON-Encoded Construction Engineering Information

Modern construction and infrastructure projects produce large volumes of heterogeneous data, including building information models, JSON sensor streams, and maintenance logs. Ensuring interoperability and data integrity across diverse software platforms requires standardized data exchange methods. However, traditional neutral object models, often constrained by rigid and incompatible schemas, are ill-suited to accommodate the heterogeneity and long-term nature of such data. Addressing this challenge, the study proposes a schema-less data exchange approach that improves flexibility in representing and interpreting infrastructure information. The method uses dynamic JSON-based objects, with infrastructure model definitions serving as semantic guidelines rather than rigid templates. Rule-based reasoning and dictionary-guided term mapping are employed to infer entity types from semi-structured data without enforcing prior schema conformance. Experimental evaluation across four datasets demonstrated exact entity-type match rates ranging from 61.4% to 76.5%, with overall success rates—including supertypes and ties—reaching up to 95.0% when weighted accuracy metrics were applied. Compared to a previous baseline, the method showed a notable improvement in exact matches while maintaining overall performance. These results confirm the feasibility of schema-less inference using domain dictionaries and indicate that incorporating schema-derived constraints could further improve accuracy and applicability in real-world infrastructure data environments.