A method of automatic extraction of parameters of multi-LoD BIM models for typical components in wooden architectural-heritage structures. (October 2019)

• Record Type: Journal Article
• Authors: Liu, Haoyu; Xie, Linlin; Shi, Liwen; Hou, Miaole; Li, Aiqun; Hu, Yungang
• Is Part Of: Advanced engineering informatics. Volume 42(2019)
• Publication Date: 2019-10
• Subjects: Wooden architectural heritage -- Multi-LoD parametric BIM model -- Automatic extraction method -- Point cloud; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2019.101002
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Multi-LoD parametric models were proposed for typical wooden components. A method of automatic extraction of parameters from point clouds was developed. Values of critical parameters of this method were recommended and validated. Abstract: A building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are multiple-level-of-detail (multi-LoD) parametric models that emphasize different protection requirements for typical components, and a method for automatically extracting the corresponding parameters from a high-fidelity point cloud. Taking typical Chinese wooden architectural-heritage structures as an example, multi-LoD principles for typical components without damage are proposed according to the different requirements. Then, a framework of multi-LoD parametric models was developed and implemented in BIM. Based on this, a method for automatically extracting the abovementioned parameters is developed and the critical parameters of this method are recommended. To validate the reliability and efficiency of this method, the parameters of multi-LoD models of typical components are extracted. The results indicate that the relative and absolute errors of values of such parameters are mostly less than 2% and 0.5 mm, respectively. Moreover, this method is capable of extracting parameters from millions of point-cloud data within 7 min, thus validating the high efficiency and reliability of the proposed method.