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 micro-level 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.