Developing Quality Linked-data and Process Patterns in Digital Twins for Iterative Defect Trigger Identification

Abstract: Digital twins are increasingly applied in the construction industry. Utilizing real-time, structured data from digital twins for quality control remains a critical challenge. This paper defines the evolution mechanisms of defect triggers through a literature review. The quality linked-data is designed by using Resource Description Framework and related technical concepts to transform multi-source heterogeneous data into structured linked-data. A workflow with process patterns for defect trigger identification is developed to invoke the quality linked-data. The framework is validated with the reinforcement cage length deviation in cast-in-place piles. This work provides theoretical insights for automated defect trigger identification with digital twins.