From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation

Persistent issues of schedule deviations and cost overruns within large construction projects aggravate the construction industry's global productivity concerns. However, how holistic, data-oriented methods can effectively be leveraged for investigating project performance and identifying potential bottlenecks during the construction phase remains unanswered. Our research addresses this issue with a novel approach encompassing data acquisition, object detection, geometric projection, and graph-based linking. Image data, continuously captured by crane-camera systems, gets transformed into higher-level information using an end-to-end deep learning-based pipeline that covers the detection of specific on-site objects and integrates it in a knowledge graph. The knowledge graph facilitates extracting precise construction metrics, identifying spatiotemporal irregularities, like work hotspots characterized by high activity and intensive work concentrations, but also phases with low activity. The proposed method improves learning from past construction data, aiding stakeholders and inspiring further research into real-time monitoring, predictive analytics, and data-integrated decision-making systems to reshape construction practices.