Automated construction quality monitoring using trajectory planning and scan-vs-BIM integration

This research presents an integrated framework for automatic trajectory planning and Scan-vs-BIM for quality inspection. The methodology combines optimized viewpoint planning for structured light scanning with advanced point cloud processing algorithms for rebar detection and measurement. Key innovations include an enhanced greedy algorithm for coverage analysis considering key features and overall structure, and an advanced cylinder fitting approach combining RANSAC and least-squares minimization for diameter estimation. This study achieves 94 % accuracy in H16 rebar dimensional measurement. A photogrammetry-based enhancement technique successfully restores missing elements to 96.79 % length completeness. The comparative analysis demonstrates that the advanced cylinder fitting method excels with complete point clouds, while advanced sectional analysis performs better for fragmented data. This study establishes a robust foundation for automated reinforcement inspection, significantly improving efficiency and accuracy compared to traditional methods while reducing reliance on manual inspection. • Feature-based trajectory optimization for automated rebar inspection. • Dual-optimization approach combining RANSAC and least-squares minimization achieves 94 % accuracy for H16 rebar diameter estimation. • Density-based sectional analysis with adaptive placement and weighted contribution for cylinder measurement. • Photogrammetry-based enhancement technique restores missing segments to 96.79 % completeness.