Schedule-Driven Analytics of 3D Point Clouds for Automated Construction Progress Monitoring

Timely progress update in the project schedule is essential for managing the critical path and expected project completion date. With the advent of AI-powered construction progress monitoring, inferring up-to-date progress information from visual data (images/videos) has become very much possible. Principally, these methods compare as-built reality models (point clouds) with 4D BIM to estimate element-wise or schedule-activity-wise project progress. Estimated progress is then updated in the project schedules. However, the 4D BIM creation process is time-consuming, and this schedule-linked model often becomes outdated after construction begins. Currently, there is no method to automatically align project schedules and reality models for progress updates without an updated 4D BIM. Therefore, this research proposes an automatic alignment method between project schedules and reality models with or without a 3D BIM. In particular, the reality models are first aligned to the world coordinate system using a 3D BIM or ground control points. Next, two-step point cloud segmentation is employed to detect the progress associated with a specific location, object, and task (L-O-T). Similarly, location, object, and task (L-O-T) information is inferred from each schedule-activity through natural language processing (NLP)-based information extraction. Later L-O-Ts are matched through a distance-based matching technique to link progress information with schedule activities. The method is applied in a precast building construction project, and the preliminary results confirm its applicability for automated progress updates.