Data quality-oriented scan planning for steel structure scenes using a probabilistic genetic algorithm

• Published in: Automation in construction Vol. 167; p. 105700
• Main Authors: Li, Fangxin, Yi, Chang-Yong, Li, Qiongfang, Chi, Hung-Lin, Kim, Min-Koo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024
• Subjects: Data acquisition; Data quality; Probabilistic genetic algorithm; Scan planning; Scan-to-BIM; Steel structure scenes; Steel structure scenes; Data quality; Probabilistic genetic algorithm; Scan-to-BIM; Data acquisition; Scan planning
• ISSN: 0926-5805
• DOI: 10.1016/j.autcon.2024.105700

Scan planning is often challenging particularly in steel structure scenes because of its complex shapes and occlusions. Meeting the requirements of data quality for the scan-to-BIM model is also another issue for accurate point cloud data acquisition. To address these issues, this study proposes a solution that determines an optimal number of scans and corresponding scan positions and parameters. Three primary steps include 1) extraction of feature points using a slicing cutting method and range images, 2) evaluation of data quality using visibility check and data density evaluation, and 3) determination of optimal scan configuration using a probabilistic genetic algorithm. In order to validate the proposed solution, a series of lab-scale experiments involving five case studies with different scenarios are conducted and the results show a similarity of 88.4% between simulation and actual experiments, demonstrating the feasibility of the proposed method for steel structure scenes with complex shapes and occlusions. •A scan planning solution that satisfies data quality requirement for scan-to-BIM is developed.•Feature point extraction is performed using slicing cutting method and range images.•Evaluation of data quality is performed using visibility check and data density evaluation.•Five case studies with different structure scenes are conducted.•Validation tests show more than 88% similarity between simulation and experiments.