海南大学学报（自然科学版中英文）2026，Vol.44Issue(2)：217-227,11.DOI:10.65658/j.hndk.2025090801
基于BIM和点云重建的既有空间网架结构承载力自动化预测
Automated prediction of load-bearing capacity of existing spatial grid structures based on BIM and point cloud reconstruction
摘要
Abstract
To enable automated prediction of the load-bearing capacity of large-span stadium spatial grid structures,thsi study proposes an algorithm integrating Building Information Modeling with 3D point cloud reconstruction.First,a 3D point cloud fitting algorithm for welded spherical joints is established using the least squares and RANSAC methods,while a combined RANSAC-PCA algorithm is developed for circular steel pipe members.Subsequently,taking the Hainan University Comprehensive Gymnasium as a case study,the BIM and finite element enable of its grid structure are reconstructed via Dynamo visual programming.Finally,the static load-bearing capacities of the designed model and the reconstructed models are compared.The results show that the vertical position deviation of the welded spherical joints ranges from(-70～+60)mm,and the diameter fitting deviation ranges from(-20～+10)mm,with a maximum deviation rate of 6.67%.The maximum fitting deviation rate of members is 21.19%,accounting for 0.51%of all members,while 78.85%of member diameters fall within a deviation range of(-6～+6)mm.Considering both joint position deviations and member bending deflection,the actual load-bearing capacity of the existing grid structure is 13.00%lower than the design value.Futhermore,the influence of joint deviation defects on static load-bearing capacity of the structure is much greater than that of member bending deformation.This findings provide critical references for the safety assessment and reinforcement design of existing spatial grid structures.关键词
既有网架结构/BIM/点云重建/承载力/自动化预测Key words
existing grid structure/building information modeling/point cloud reconstruction/bearing capacity/automatic prediction分类
社会科学引用本文复制引用
侯鑫焱,张天龙,汤爱平,胡安奎..基于BIM和点云重建的既有空间网架结构承载力自动化预测[J].海南大学学报（自然科学版中英文）,2026,44(2):217-227,11.基金项目
本文得到海南省自然科学基金项目(520QN232)、天津大学-海南大学自主创新基金项目(RZ2100003553)和横向项目(HD-KYH-2024061)的支持. This study was supported by the Hainan Provincial Natural Science Foundation(520QN232),the Tianjin University-Hainan University Independent Innovation Foundation Project(RZ2100003553),and the Hainan University Horizontal Project(HD-KYH-2024061). （520QN232）
