Autonomous Construction Progress Quantification and Predictive Schedule Deviation Analysis with 4D BIM Integration

Authors: Sai Kothapalli

DOI: https://doi.org/10.37082/IJIRMPS.v12.i3.232599

Short DOI: https://doi.org/g9q4bj

Country: USA

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Abstract: This paper presents a comprehensive machine learning framework for real-time construction progress monitoring and deviation detection. The proposed system integrates computer vision, point cloud processing with deep learning, and graph neural networks to analyze multi-modal data from drones, LiDAR scanners, Building Information Modeling (BIM), and site cameras. This research approach achieves 94.2% accuracy in progress quantification and reduces schedule deviation detection time by 78% compared to traditional manual methods. The system demonstrates significant improvements in automated quantity take-off (±3.1% accuracy) and predictive scheduling with 89.7% precision in delay forecasting.

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Paper Id: 232599

Published On: 2024-05-07

Published In: Volume 12, Issue 3, May-June 2024

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