Incorporation of Context-based Data to Refine Machine Learning Models in Industrialized Construction

Abstract: The industrialized construction promises to transform the industry by performing activities in shop floors to improve operational efficiency. However, the lack of integration between practical experience and real-time data limits more efficient operations in the shop floor. This study addresses this limitation by combining design parameters from BIM models with real-time production data collected via RFID in a semi-automated shop floor. By including context-based data in machine learning models, cycle time prediction accuracy was improved, compared to models containing only design-based data. highlighting how data granularity optimizes operational planning and production processes in Industrialized Construction.