Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data - Yang, Liu; Cheng, Jack C.P.; Wang, Qian (Automation in construction, 2020)
As-built building information models (BIMs) are increasingly needed for construction project handover and facility management. To create as-built BIMs, laser scanning technology has gained popularity in the recent decades due to its high measurement accuracy and high measurement speed. However, most existing methods for creating as-built BIMs from laser scanning data involve plenty of manual work, thus becoming labor intensive and time consuming. To address the problems, this study presents a semi-automated approach that can obtain required parameters to create as-built BIMs for steel structures with complex connections from terrestrial laser scanning data. An algorithm based on principal component analysis (PCA) and cross-section fitting techniques is developed to retrieve the position and direction of each circular structural component from scanning data. An image-assisted edge point extraction algorithm is developed to effectively extract the boundaries of planar structural components. Normal-based region growing algorithm and random sample consensus (RANSAC) algorithm are adopted to model the connections between structural components. The proposed approach was validated on a bridge-like steel structure with four different types of structural components. The extracted as-built geometry was compared with the as-designed geometry to validate the accuracy of the proposed approach. The results showed that the proposed approach could efficiently and accurately extract the geometry information and generate parametric BIMs of steel structures. •Extracting the geometry information for irregular-shaped complex structure•Proposing an automated BIM model generation process using Dynamo•PCA and cross-section fitting based technique to retrieve position and direction•Developing an image-assisted edge point extraction algorithm to extract boundaries•Combing Normal-based region growing and RANSAC to extract connections