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.