Analysis of Research Trend for the Application of Scan-to-BIM Technologies in Civil Infrastructure

토목시설물의 Scan-to-BIM 기술 적용을 위한 연구동향 분석

  • 안효준 (인하대학교 스마트시티공학과) ;
  • 정현진 (인하대학교 스마트시티공학과) ;
  • 이민진 (인하대학교 사회인프라공학과) ;
  • 양다현 (인하대학교 사회인프라공학과) ;
  • 이종한 (인하대학교 스마트시티공학과)
  • Published : 2022.09.15

Abstract

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 RS-2022-00142566).

References

  1. Robinson, C. (2007) Structural BIM: Discussion, Case Studies and Latest Developments, Struct. Des. Tall Spec. Build., 16, 519-533. https://doi.org/10.1002/tal.417
  2. Bosche, F., Ahmed, M., Turkan, Y., Haas, C.T. and Haas, R. (2015) The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components, Autom. Constr, 49, 201-213. https://doi.org/10.1016/j.autcon.2014.05.014
  3. Patraucean V., Armeni I., Nahangi M., Yeung J., Brilakis I. and Haas C. (2015) State of research in automatic as-built modelling. Advanced Engineering Informatics, 29 (2), pp. 162-171. https://doi.org/10.1016/j.aei.2015.01.001
  4. Badenko, V., Fedotov, A., Zotov, D., Lytkin, S., Volgin, D., Garg, R. D. and Liu, M. (2019) Scan-to-BIM methodology adapted for different app lication, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, 42 (5/W2), 1-7.
  5. He, Y., Liang, B., Yang, J., Li, S., and He, J. (2017) An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features, Sensors, 17( 8).
  6. Agarwal, S. and Bhowmick, B. (2017) 3D point cloud registration with shape constraint, 2017 IEEE International Conference on Image Processing, 17-20.
  7. Liu, J., Shang, X., Yang, S., Shen, Z., Liu, X., Xiong, G. and Nyberg, T.R. (2017) Research on Optimization of Point Cloud Registration ICP Algorithm, Image and Video Technology, 81-90.
  8. Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2016) Pointnet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), 1, 4.
  9. Perez-Perez, Yeritza, Mani Golparvar-Fard, and Khaled El-Rayes. (2021) Scan2BIM-NET: deep learning method for segmentation of point clouds for scan-to-BIM, Journal of Construction Engineering and Management, 147, 9
  10. Yang L., Cheng J. C. and Wang Q. (2020) Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data. Automation in Construction, 112, 103037. https://doi.org/10.1016/j.autcon.2019.103037
  11. M.E. Esfahani, C. Rausch, M.M. Sharif, Q. Chen, C. Haas and B.T. Adey, (2021) Quantitative investigation on the accuracy and precision of scan-to-BIM under different modelling scenarios, Autom. Constr., 126.
  12. Park, J., Kim, J., Lee, D., Jeong, K., Lee, J., Kim, H., and Hong, T. (2022) Deep Learning-Based Automation of Scan-to-BIM with Modeling Objects from Occluded Point Clouds. Journal of Management in Engineering, 38(4), 04022025.
  13. Xie, X., Zhao, M., He, J., and Zhou, B. (2018) Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data. Preprints.
  14. P. Kontothanasis, V. Krommyda, and N. Roussos (2019) BIM and advanced computer-based tools for the design and construction of underground structures and tunnels, Tunnel Engineering-Selected Topics.
  15. Yang, L., Cheng, J. C., and Wang, Q. (2020) Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data. Automation in Construction, 112, 103037. https://doi.org/10.1016/j.autcon.2019.103037
  16. Qin, G., Zhou, Y., Hu, K., Han, D., and Ying, C. (2021) Automated reconstruction of parametric bim for bridge based on terrestrial laser scanning data. Advances in Civil Engineering, 2021.