Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
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Integration of 3D Laser Scanner and BIM Process for Visualization of Building Defective Condition

3D 레이저 스캐닝과 BIM 연동을 통한 건축물 노후 상태 정보 시각화 프로세스

  • Received : 2022.03.02
  • Accepted : 2022.04.06
  • Published : 2022.04.20
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Abstract

The regular assessment of a building is important to understand structural safety and latent risk in the early stages of building life cycle. However, methods of traditional assessment are subjective, atypical, labor-intensive, and time-consuming and as such the reliability of these results has been questioned. This study proposed a method to bring accurate results using a 3D laser scanner and integrate them in Building Information Modeling (BIM) to visualize defective condition. The specific process for this study was as follows: (1) semi-automated data acquisition using 3D laser scanner and python script, (2) scan-to-BIM process, (3) integrating and visualizing defective conditions data using dynamo. The method proposed in this study improved efficiency and productivity in a building assessment through omitting the additional process of measurement and documentation. The visualized 3D model allows building facility managers to make more effective decisions. Ultimately, this is expected to improve the efficiency of building maintenance works.

주기적인 건축물 안전진단은 구조적 안전성 및 잠재적 위험을 조기에 파악할 수 있다. 하지만 기존의 수집 방식은 비정형화된 형태의 주관적 데이터가 주로 사용되며, 노동집약적이고 시간 소모적이기 때문에 신뢰성이 떨어진다. 이에 본 연구는 3D 레이저 스캐너를 이용하여 건축물 노후 상태 정보를 수집하고 Building Information Modeling(BIM)으로 통합하여 시각화하는 방안을 제안하며, 순서는 다음과 같다: (1) 3D 레이저 스캐너와 파이썬 스크립트를 통한 데이터 수집, (2) Scan-to-BIM 프로세스, (3) 다이나모를 이용한 상태 데이터 시각화 및 정보 통합. 이를 통해 데이터 저장과 보고서 및 도면 작성 과정의 생략에 따른 시간 단축 효과를 확인하였다. 또한 시각화된 3D 모델은 건축물 유지관리자가 효율적인 결정을 할 수 있도록 한다. 이를 통해 유지관리 업무 효율성이 향상될 것으로 예상된다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT)(No. 2020R1F1A1058136).

References

  1. Napolitano R, Hess M, Glisic B. Quantifying the differences in documentation and modeling levels for building pathology and diagnostics. Archives of Computational Methods in Engineering. 2019 Sep;27(2):1135-52. https://doi.org/10.1007/s11831-019-09350-y
  2. Pereira C, Silva A, Ferreira C, de Brito J, Flores-Colen I, Silvestre JD. Uncertainty in building inspection and diagnosis: A probabilistic model quantification. infrastructures. 2021 Sep;6(9):124-48. https://doi.org/10.3390/infrastructures6090124
  3. Nowak R, Orlowicz R, Rutkowski R. Use of TLS (LiDAR) for building diagnostics with the example of a historic building in karlino. Buildings. 2020 Feb;10(2):24-38. https://doi.org/10.3390/buildings10020024
  4. Borin P, Cavazzini F. Condition assessment of RC bridges. Integrating machine learning, photogrammetry and BIM. 27th CIPA International Symposium; 2019 Sep 1-5; Avila, Spain. Germany: Remote Sensing and Spatial Information Sciences; 2019. p. 201-8. https://doi.org/10.5194/isprs-archives-XLII-2-W15-201-2019
  5. Chen HM, Hou CC, Wang YH. A 3D visualized expert system for maintenance and management of existing building facilities using reliability-based method. Expert Systems with Applications. 2013 Jan;40(1):287-99. https://doi.org/10.1016/j.eswa.2012.07.045
  6. Shi Z, Ergan S. Towards point cloud and model-based urban facade inspection: challenges in the urban facade inspection process. In Construction Research Congress 2020: Safety, Workforce, and Education; 2020 May 8-10; Tempe, Arizona. Reston (USA): American Society of Civil Engineers; 2020. p. 385-94.
  7. Law D W, Silcock D, Holden L. Terrestrial laser scanner assessment of deteriorating concrete structures. Structural Control and Health Monitoring. 2018 Feb;25(5):e2156. https://doi.org/10.1002/stc.2156
  8. Chow JK, Liu KF, Tan PS, Su Z, Wu J, Li Z, Wang YH. Automated defect inspection of concrete structures. Automation in Construction. 2021 Dec;132:103959. https://doi.org/10.1016/j.autcon.2021.103959
  9. Hossain MA, Yeoh JK. BIM for existing buildings: potential opportunities and barriers. In IOP Conference Series; 2018 Feb 23-25; Nha Trang, Vietnam. England: Materials Science and Engineering; 2018. p. 012051.
  10. Pishdad-Bozorgi P, Gao X, Eastman C, Self AP. Planning and developing facility management-enabled building information model (FM-enabled BIM). Automation in Construction. 2018 Mar;87:22-38. https://doi.org/10.1016/j.autcon.2017.12.004
  11. Dorafshan S, Maguire M. Bridge inspection: human performance, unmanned aerial systems and automation. Journal of Civil Structural Health Monitoring. 2018 May;8(3): 443-76. https://doi.org/10.1007/s13349-018-0285-4
  12. Kwan AKH, Ng PL. Building diagnostic techniques and building diagnosis: the way forward. Proceedings of the 8th World Congress on Engineering Asset Management (WCEAM 2013) & the 3rd International Conference on Utility Management & Safety (ICUMAS); 2013 Oct 30-Nov 1; Wan Chai, Hongkong. Switzerland: Engineering Asset Management-Systems, Professional Practices and Certification. 2015. p. 849-62. https://doi.org/10.1007/978-3-319-09507-3_74
  13. Valenca J, Puente I, Julio E N B S, Gonzalez-Jorge H, Arias-Sanchez P. Assessment of cracks on concrete bridges using image processing supported by laser scanning survey. Construction and Building Materials. 2017 Aug;146:668-78. https://doi.org/10.1016/j.conbuildmat.2017.04.096
  14. Dias IS, Flores-Colen I, Silva A. Critical analysis about emerging technologies for building's facade inspection. Buildings. 2021 Feb;11(2):53. https://doi.org/10.3390/buildings11020053
  15. Kim MK, Cheng JC, Sohn H, Chang CC. A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning. Automation in Construction. 2015 Jan;49:225-38. https://doi.org/10.1016/j.autcon.2014.07.010
  16. Turkan Y, Hong J, Laflamme S, Puri N. Adaptive wavelet neural network for terrestrial laser scanner-based crack detection. Automation in construction. 2018 Oct;94:191-202. https://doi.org/10.1016/j.autcon.2018.06.017
  17. Li D, Liu J, Feng L, Zhou Y, Liu P, Chen YF. Terrestrial laser scanning assisted flatness quality assessment for two different types of concrete surfaces. Measurement. 2020 Mar;154:107436. https://doi.org/10.1016/j.measurement.2019.107436
  18. Valenca JMDA, Dias-da-Costa D, Julio ENBS. Characterisation of concrete cracking during laboratorial tests using image processing. Construction and Building Materials. 2012 Mar;28(1):607-15. https://doi.org/10.1016/j.conbuildmat.2011.08.082
  19. Liu D, Xia X, Chen J, Li S. Integrating building information model and augmented reality for drone-based building inspection. Journal of Computing in Civil Engineering. 2021 Mar;35(2):04020073. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000958
  20. Ivson P, Moreira A, Queiroz F, Santos W, Celes W. A systematic review of visualization in building information modeling. IEEE transactions on visualization and computer graphics. 2019 Mar;26(10):3109-27. https://doi.org/10.1109/TVCG.2019.2907583
  21. Akinci B, Garrett Jr J H. Integrating and visualizing maintenance and repair work orders in BIM: lessons learned from a prototype. International Conference on Construction Applications of Virtual Reality; 2011 Nov 3-4; Weimar, Germany. US: Academia; 2011. p. 639-49.
  22. Pishdad-Bozorgi P, Gao X, Eastman C, Self AP. Planning and developing facility management-enabled building information model (FM-enabled BIM). Automation in Construction. 2018 Mar;87:22-38. https://doi.org/10.1016/j.autcon.2017.12.004
  23. Naghshbandi SN. BIM for facility management: challenges and research gaps. Civil Engineering Journal. 2016 Dec;2(12):679-84. https://doi.org/10.28991/cej-2016-00000067
  24. Kiviniemi A, Codinhoto R. Challenges in the implementation of BIM for FM-case manchester town hall complex. 2014 International Conference on Computing in Civil and Building Engineering; 2014 Feb 3-6; Honolulu, Hawaii. Reston: American Society of Civil Engineers; 2014. p. 665-72. https://doi.org/10.1061/9780784413616.083
  25. Collao J, Lozano-Galant F, Lozano-Galant JA, Turmo J. BIM Visual programming tools applications in infrastructure projects: A state-of-the-art review. Applied Sciences. 2021 Sep;11(18):8343. https://doi.org/10.3390/app11188343
  26. Valinejadshoubi M, Moselhi O, Bagchi A. Integrating BIM into sensor-based facilities management operations. Journal of Facilities Management. 2021 Jan. https://doi.org/10.1108/JFM-08-2020-0055
  27. Saridaki M, Psarra M, Haugbolle K. Implementing life-cycle costing: Data integration between design models and cost calculations. Journal of Information Technology in Construction. 2019 Feb;24:14-32.
  28. Weng Y, Mohamed NAN, Lee BJS, Gan NJH, Li M, Jen Tan M, Qian S. Extracting BIM information for lattice toolpath planning in digital concrete printing with developed dynamo script: a case study. Journal of Computing in Civil Engineering. 2021 May;35(3):05021001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000964
  29. Oreto C, Massotti L, Biancardo SA, Veropalumbo R, Viscione N, Russo F. BIM-Based pavement management tool for scheduling urban road maintenance. Infrastructures. 2021 Oct;6(11):148. https://doi.org/10.3390/infrastructures6110148
  30. Buonamici F, Carfagni M, Furferi R, Governi L, Lapini A, Volpe Y. Reverse engineering modeling methods and tools: a survey. Computer-Aided Design and Applications. 2018 May;15(3):443-64. https://doi.org/10.1080/16864360.2017.1397894
  31. Yang L, Cheng JC, Wang Q. Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data. Automation in Construction. 2020 Apr;112:103037. https://doi.org/10.1016/j.autcon.2019.103037
  32. Olsen MJ, Kuester F, Chang BJ, Hutchinson TC. Terrestrial laser scanning-based structural damage assessment. Journal of Computing in Civil Engineering. 2010 May;24(3): 264-272. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000028
  33. Kim S, Kim S, Lee DE. Sustainable application of hybrid point cloud and BIM method for tracking construction progress. Sustainability. 2020 May;12(10):4106. https://doi.org/10.3390/su12104106
  34. Qiu Q, Wang M, Tang X, Wang Q. Scan planning for existing buildings without BIM based on user-defined data quality requirements and genetic algorithm. Automation in Construction. 2021 Oct;130:103841. https://doi.org/10.1016/j.autcon.2021.103841
  35. Pan NH, Chen KY. Facility maintenance traceability information coding in bim-based facility repair platform. Advances in Civil Engineering. 2020 Aug;2020(1):1-12. https://doi.org/10.1155/2020/3426563
  36. Kim S, Kim S, Lee DE. 3D point cloud and bim-based reconstruction for evaluation of project by as-planned and as-built. Remote Sensing. 2020 May;12(9):1457. https://doi.org/10.3390/rs12091457
  37. Salamak M, Jasinski M, Plaszczyk T, Zarski M. Analytical modelling in dynamo. Civil Engineering Series. 2018 Jan;18(2):36-43. https://doi.org/10.31490/tces-2018-0014
  38. Mengana S, Mousiadis T. Parametric BIM: Energy performance analysis using dynamo for revit [dissertation]. [Sweden (Stockholm)]: Royal Institute of Technology; 2016. 46 p.