Automated Construction Progress Monitoring of Partially Completed Building Elements Leveraging Geometry Modeling and Appearance Detection with Deep Learning

The exponential growth of on-site visual data and the advent of computer vision techniques have created a unique opportunity to improve automated construction progress monitoring methods. To date, the state-of-the-art vision-based methods are capable of reporting the progress of a building element in terms of binary function. However, for better schedule control and microlevel monitoring, it is necessary to report the partial completion of tasks associated with an element. This research proposes a novel approach for computing and reporting the partial progress of tasks in terms of completion percentage using the on-site visual data, 4D BIM, and deep-learning-based computer vision algorithms. The approach leverages geometry modeling and appearance detection to automatically calculate the percentage completion of tasks associated with each element. The proposed approach is applied to a building construction project, and the preliminary results demonstrate its applicability to generate completion percentage per task in the lookahead schedule for accurate daily progress report generation.