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Integracja danych skanowania w celu ulepszenia BIM: przegląd technik, strategii mapowania i aplikacji cyklu życia budynku
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In the fields of AEC (Architecture, Engineering and Construction), BIM (Building Information Modeling) is developing quickly. Integrating point clouds from 3-D laser scanners with BIM is a potent solution with a range of applications across the building life cycle. This is due to LiDAR technology, which has become a significant component in BIM, obtaining 3D point clouds – a geometric representation of 3D models that is semantically rich. It is essential to understand the current status of 3-D laser scanning and BIM applications as well as their integrated tactics. In this review paper, the first section of the study summarizes the different approaches on Scan to BIM technique. Then, a detailed explanation of the various methods of mapping the scanned data with BIM is given. Proceeded by summarizing the various applications throughout the building life cycle.
W obszarach AEC (architektura, inżynieria i budownictwo) szybko rozwija się BIM (modelowanie informacji o budynku). Integracja chmur punktów ze skanerów laserowych 3D z BIM to skuteczne rozwiązanie z szeregiem zastosowań w całym cyklu życia budynku. Dzieje się tak dzięki technologii LiDAR, która stała się istotnym elementem BIM, uzyskując chmury punktów 3D – bogatą semantycznie geometryczną reprezentację modeli 3D. Niezbędne jest zrozumienie obecnego stanu zastosowań skanowania laserowego 3D i BIM, a także ich zintegrowanej taktyki. W tym artykule przeglądowym pierwsza część badania podsumowuje różne podejścia do techniki skanowania do BIM. Następnie podano szczegółowe wyjaśnienie różnych metod mapowania zeskanowanych danych za pomocą BIM. Następnie podsumowano różne zastosowania w całym cyklu życia budynku.
Wydawca
Czasopismo
Rocznik
Tom
Strony
93--100
Opis fizyczny
Bibliogr. 84 poz., rys.
Twórcy
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
- Karunya Institute of Technology and Sciences, Coimbatore 641114
autor
Bibliografia
- [1] S. A. Biancardo, A. Capano, S. G. de Oliveira, and A. Tibaut, “Integration of BIM and Procedural Modeling Tools for Road Design,” Infrastructures (Basel), 2020, doi: 10.3390/INFRASTRUCTURES5040037.
- [2] S. Kurwi, P. Demian, K. B. Blay, and T. M. Hassan, “Collaboration through Integrated BIM and GIS for the Design Process in Rail Projects: Formalising the Requirements,” Infrastructures (Basel), vol. 6, no. 4, p. 52, Mar. 2021, doi: 10.3390/infrastructures6040052.
- [3] M. Pasetto, A. Giordano, P. Borin, and G. Giacomello, “Integrated railway design using Infrastructure-Building Information Modeling. The case study of the port of Venice,” Transportation research procedia, 2020, doi: 10.1016/J.TRPRO.2020.02.084.
- [4] G. L. Rocha, L. Mateus, J. H. Fernandez, and V. Ferreira, “A Scan-to-BIM Methodology Applied to Heritage Buildings,” Heritage, 2020, doi: 10.3390/HERITAGE3010004.
- [5] V. Badenko et al., “SCAN-TO-BIM METHODOLOGY ADAPTED FOR DIFFERENT APPLICATION,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, doi: 10.5194/ISPRSARCHIVES-XLII-5-W2-1-2019.
- [6] M. Khanal, M. Hasan, N. Sterbentz, R. Johnson, and J. Weatherly, “Accuracy Comparison of Aerial Lidar, Mobile-Terrestrial Lidar, and UAV Photogrammetric Capture Data Elevations over Different Terrain Types,” Infrastructures (Basel), 2020, doi: 10.3390/INFRASTRUCTURES5080065.
- [7] H. K. Heidemann, “Lidar base specification version 1.0,” Techniques and Methods, 2012, doi: 10.3133/TM11B3.
- [8] A. Sánchez-Rodríguez, B. Riveiro, M. Soilán, and L. M. González-deSantos, “Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets,” Autom Constr, 2018, doi: 10.1016/J.AUTCON.2018.09.014.
- [9] F. Poux, “The Smart Point Cloud: Structuring 3D intelligent point data,” 2019, doi: 10.2312/2633000.
- [10] I. Puente, H. González-Jorge, J. Martínez-Sánchez, and P. Arias, “Review of mobile mapping and surveying technologies,” Measurement, 2013, doi: 10.1016/J.MEASUREMENT.2013.03.006.
- [11] M. M. Shanoer and F. M. Abed, “Evaluate 3D laser point clouds registration for cultural heritage documentation,” The Egyptian Journal of Remote Sensing and Space Science, 2017, doi: 10.1016/J.EJRS.2017.11.007.
- [12] X.-F. Han, J. S. Jin, M. Wang, W. Jiang, L. Gao, and L. Xiao, “A review of algorithms for filtering the 3D point cloud,” Signal Processing-image Communication, 2017, doi: 10.1016/J.IMAGE.2017.05.009.
- [13] M. M. Al-Durgham, “The Registration and Segmentation of Heterogeneous Laser Scanning Data,” 2014.
- [14] Y.-J. Lin, R. R. Benziger, and A. Habib, “Planar-Based Adaptive Down-Sampling of Point Clouds,” 2016, doi: 10.14358/PERS.82.12.955.
- [15] A. Al-Rawabdeh, F. He, and A. Habib, “Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds,” Remote. Sens., 2020, doi: 10.3390/RS12071224.
- [16] S. Rusinkiewicz and M. Levoy, “Efficient variants of the ICP algorithm,” Proceedings Third International Conference on 3-D Digital Imaging and Modeling, 2001, doi: 10.1109/IM.2001.924423.
- [17] T.-H. Kwok, “DNSS: Dual-Normal-Space Sampling for 3-D ICP Registration,” IEEE Transactions on Automation Science and Engineering, 2019, doi: 10.1109/TASE.2018.2802725.
- [18] L. Cheng et al., “Registration of Laser Scanning Point Clouds: A Review,” Sensors, 2018, doi: 10.3390/S18051641.
- [19] G. Tam et al., “Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid,” IEEE Trans Vis Comput Graph, 2013, doi: 10.1109/TVCG.2012.310.
- [20] V. Villena-Martinez, S. Oprea, M. Saval-Calvo, J. Azorin-Lopez, A. Fuster-Guillo, and R. B. Fisher, “When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs),” Applied Sciences, 2020, doi: 10.3390/APP10217524.
- [21] Z. Zhang, Y. Dai, and J. Sun, “Deep learning based point cloud registration: an overview,” Virtual Reality & Intelligent Hardware, vol. 2, no. 3, pp. 222–246, Jun. 2020, doi: 10.1016/j.vrih.2020.05.002.
- [22] F. Lateef and Y. Ruichek, “Survey on semantic segmentation using deep learning techniques,” Neurocomputing, 2019, doi: 10.1016/J.NEUCOM.2019.02.003.
- [23] Y. Xie, J. Tian, and X. X. Zhu, “Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation,” IEEE Geosci Remote Sens Mag, 2020, doi: 10.1109/MGRS.2019.2937630.
- [24] Q. Wang, Y. Tan, and Z. Mei, “Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications,” Archives of Computational Methods in Engineering, 2020, doi: 10.1007/S11831-019-09320-4.
- [25] B. Koo, S. La, N.-W. Cho, and Y. Yu, “Using support vector machines to classify building elements for checking the semantic integrity of building information models,” Autom Constr, 2019, doi: 10.1016/J.AUTCON.2018.11.015.
- [26] Z. Zhu and I. Brilakis, “Automated Detection of Concrete Columns from Visual Data,” 2009, doi: 10.1061/41052(346)14.
- [27] A.-V. Vo, L. Truong-Hong, D. F. Laefer, and M. Bertolotto, “Octree-based region growing for point cloud segmentation,” Isprs Journal of Photogrammetry and Remote Sensing, 2015, doi: 10.1016/J.ISPRSJPRS.2015.01.011.
- [28] F. Tarsha-Kurdi, T. Landes, and P. Grussenmeyer, “Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from Lidar Data,” 2007.
- [29] A. Guillén, A. Crespo, J. Gómez, V. González-Prida, K. A. H. Kobbacy, and S. M. Shariff, “Building Information Modeling as Assest Management Tool,” IFAC-PapersOnLine, 2016, doi: 10.1016/J.IFACOL.2016.11.033.
- [30] S. Jiang, L. Jiang, Y. Han, Z. Wu, and N. Wang, “OpenBIM: An Enabling Solution for Information Interoperability,” 2019, doi: 10.3390/APP9245358.
- [31] C. Thomson, G. Apostolopoulos, D. Backes, and J. Boehm, “Mobile Laser Scanning for Indoor Modelling,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. II-5/W2, pp. 289–293, Oct. 2013, doi: 10.5194/isprsannals-II-5-W2-289-2013.
- [32] M. Chen, E. Koc, Z. Shi, and L. Soibelman, “Proactive 2D model-based scan planning for existing buildings,” Autom Constr, vol. 93, pp. 165–177, Sep. 2018, doi: 10.1016/j.autcon.2018.05.010.
- [33] B. Oehler, J. Stueckler, J. Welle, D. Schulz, and S. Behnke, “Efficient multi-resolution plane segmentation of 3D point clouds,” in Intelligent Robotics and Applications: 4th International Conference, ICIRA 2011, Aachen, Germany, December 6-8, 2011, Proceedings, Part II 4, Springer, 2011, pp. 145–156.
- [34] C. Kim, A. Habib, M. Pyeon, G. Kwon, J. Jung, and J. Heo, “Segmentation of planar surfaces from laser scanning data using the magnitude of normal position vector for adaptive neighborhoods,” Sensors, vol. 16, no. 2, p. 140, 2016.
- [35] J. Xiao, J. Zhang, B. Adler, H. Zhang, and J. Zhang, “Three-dimensional point cloud plane segmentation in both structured and unstructured environments,” Rob Auton Syst, vol. 61, no. 12, pp. 1641–1652, Dec. 2013, doi: 10.1016/j.robot.2013.07.001.
- [36] A.-V. Vo, L. Truong-Hong, D. F. Laefer, and M. Bertolotto, “Octree-based region growing for point cloud segmentation,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 104, pp. 88–100, Jun. 2015, doi: 10.1016/j.isprsjprs.2015.01.011.
- [37] Y. Cui, Q. Li, B. Yang, W. Xiao, C. Chen, and Z. Dong, “Automatic 3-D reconstruction of indoor environment with mobile laser scanning point clouds,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 12, no. 8, pp. 3117–3130, 2019.
- [38] S. Ochmann, R. Vock, and R. Klein, “Automatic reconstruction of fully volumetric 3D building models from oriented point clouds,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 151, pp. 251–262, May 2019, doi: 10.1016/j.isprsjprs.2019.03.017.
- [39] Y.-J. Cheng, W.-G. Qiu, and D.-Y. Duan, “Automatic creation of as-is building information model from single-track railway tunnel point clouds,” Autom Constr, vol. 106, p. 102911, Oct. 2019, doi: 10.1016/j.autcon.2019.102911.
- [40] M. Bassier and M. Vergauwen, “Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields,” Remote Sens (Basel), vol. 11, no. 13, p. 1586, Jul. 2019, doi: 10.3390/rs11131586.
- [41] H. Tran and K. Khoshelham, “Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo,” Remote Sens (Basel), vol. 12, no. 5, p. 838, Mar. 2020, doi: 10.3390/rs12050838.
- [42] J. Chen, Z. Kira, and Y. K. Cho, “Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction,” Journal of Computing in Civil Engineering, vol. 33, no. 4, Jul. 2019, doi: 10.1061/(ASCE)CP.1943-5487.0000842.
- [43] J. W. Ma, T. Czerniawski, and F. Leite, “Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds,” Autom Constr, vol. 113, p. 103144, May 2020, doi: 10.1016/j.autcon.2020.103144.
- [44] Q. Wang, H. Sohn, and J. C. P. Cheng, “Automatic As-Built BIM Creation of Precast Concrete Bridge Deck Panels Using Laser Scan Data,” Journal of Computing in Civil Engineering, vol. 32, no. 3, May 2018, doi: 10.1061/(ASCE)CP.1943-5487.0000754.
- [45] J. Jung and et al., “Productive modeling for development of as built BIM of existing indoor structures,” Automat. Construction, vol. 42, pp. 68–77, 2014.
- [46] M. Soilán, A. Justo, A. Sánchez-Rodríguez, and B. Riveiro, “3D point cloud to BIM: Semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads,” Remote Sens., vol. 12, no. 14, 2020.
- [47] E. Valero, A. Adán, and F. Bosché, “Semantic 3D reconstruction of furnished interiors using laser scanning and RFID technology,” J. Comput. Civil Eng., vol. 30, no. 4, pp. 1–19, 2016.
- [48] N. Ham, B. Il Bae, and O. K. Yuh, “Phased reverse engineering framework for sustainable cultural heritage archives using laser scanning and BIM: The case of the Hwanggungwoo (Seoul Korea),” Sustainability, vol. 12, no. 19, 2020.
- [49] L. Etxepare, I. Leon, M. Sagarna, I. Lizundia, and E. J. Uranga, “Advanced intervention protocol in the energy rehabilitation of heritage buildings: A Miñones Barracks case study,” Sustainability, vol. 12, no. 15, 2020.
- [50] I. Leon, J. J. Pérez, and M. Senderos, “Advanced techniques for fast and accurate heritage digitisation in multiple case studies,” Sustainability, vol. 12, no. 15, 2020.
- [51] J. Chen and Y. K. Cho, “Point-to-point comparison method for automated scan-vs-BIM deviation detection,” in Proc. 17th Int. Conf. Comput. Civil Build. Eng., 2018.
- [52] C. Zhang and D. Arditi, “Automated progress control using laser scanning technology,” Automat. Construction, vol. 36, pp. 108–116, 2013.
- [53] F. Bosché, M. Ahmed, Y. Turkan, C. T. Haas, and R. Haas, “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,” Automat. Construction, vol. 49, pp. 201–213, 2015.
- [54] F. Tombari, S. Salti, and L. Di Stefano, “Unique signatures of histograms for local surface description,” in Proc. Eur. Conf. Comput. Vis., 2010, pp. 356–369.
- [55] Y. Guo, F. Sohel, M. Bennamoun, M. Lu, and J. Wan, “Rotational projection statistics for 3D local surface description and object recognition,” Int. J. Comput. Vis., vol. 105, no. 1, pp. 63–86, 2013.
- [56] W. Tao, X. Hua, R. Wang, and D. Xu, “Quintuple local coordinate images for local shape description,” Photogramm. Eng. Remote Sens., vol. 86, no. 2, pp. 121–132, 2020.
- [57] Z. Dong, B. Yang, Y. Liu, F. Liang, B. Li, and Y. Zang, “A novel binary shape context for 3D local surface description,” ISPRS J. Photogramm. Remote Sens., vol. 130, pp. 431–452, 2017.
- [58] Y. Turkan, F. Bosché, C. T. Haas, and R. Haas, “Toward automated earned value tracking using 3D imaging tools,” J. Construction Eng. Manag., vol. 139, no. 4, pp. 423–433, 2013.
- [59] F. Bosché, A. Guillemet, Y. Turkan, C. T. Haas, and R. Haas, “Tracking the built status of MEP works: Assessing the value of a scan-vs-BIM system,” J. Comput. Civil Eng., vol. 28, no. 4, pp. 1–14, 2014.
- [60] D. Rebolj, Z. Pučko, N. Č. Babič, M. Bizjak, and D. Mongus, “Point cloud quality requirements for scan-vs-BIM based automated construction progress monitoring,” Automat. Construction, vol. 84, pp. 323–334, 2017.
- [61] F. Bosché and E. Guenet, “Automating surface flatness control using terrestrial laser scanning and building information models,” Automat. Construction, vol. 44, pp. 212–226, 2014.
- [62] J. Guo, Q. Wang, and J. H. Park, “Geometric quality inspection of prefabricated MEP modules with 3D laser scanning,” Automat. Construction, vol. 111, 2020.
- [63] Q. Wang, M. K. Kim, J. C. P. Cheng, and H. Sohn, “Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning,” Automat. Construction, vol. 68, pp. 170–182, 2016.
- [64] M. K. Kim, Q. Wang, J. W. Park, J. C. P. Cheng, H. Sohn, and C. C. Chang, “Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM,” Automat. Construction, vol. 72, pp. 102–114, 2016.
- [65] M. K. Kim, J. C. P. Cheng, H. Sohn, and C. C. Chang, “A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning,” Automat. Construction, vol. 49, pp. 225–238, 2015.
- [66] J. Wang, S. Zhang, and J. Teizer, “Geotechnical and safety protective equipment planning using range point cloud data and rule checking in building information modeling,” Automat. Construction, vol. 49, pp. 250–261, 2015.
- [67] J. Teizer, “Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites,” Adv. Eng. Inform., vol. 29, no. 2, pp. 225–238, 2015.
- [68] B. Chan, H. Guan, L. Hou, J. Jo, M. Blumenstein, and J. Wang, “Defining a conceptual framework for the integration of modelling and advanced imaging for improving the reliability and efficiency of bridge assessments,” J. Civil Struct. Health Monitoring, vol. 6, no. 4, pp. 703–714, 2016.
- [69] N. Ham and S.-H. Lee, “Empirical Study on Structural Safety Diagnosis of Large-Scale Civil Infrastructure Using Laser Scanning and BIM,” Sustainability, vol. 10, no. 11, p. 4024, Nov. 2018, doi: 10.3390/su10114024.
- [70] F. Banfi, L. Barazzetti, M. Previtali, and F. Roncoroni, “Historic BIM: A new repository for structural health monitoring,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. 42, pp. 269–274, 2017.
- [71] R. Rolin, E. Antaluca, J. L. Batoz, F. Lamarque, and M. Lejeune, “From point cloud data to structural analysis through a geometrical hBIM-oriented model,” J. Comput. Cultural Heritage, vol. 12, no. 2, 2019.
- [72] G. P. Patil, D. Chen, G. Mayo, J. Smithwick, and N. Barclay, “Implementing BIM and finite element analysis in a structural integrity investigation of curtain walls,” J. Facility Manag. Educ. Res., vol. 3, no. 1, pp. 27–37, 2019.
- [73] L. Barazzetti, F. Banfi, R. Brumana, G. Gusmeroli, M. Previtali, and G. Schiantarelli, “Cloud-to-BIM-to-FEM: Structural simulation with accurate historic BIM from laser scans,” Simul. Model. Pract. Theory, vol. 57, pp. 71–87, 2015.
- [74] T. Bloch, R. Sacks, and O. Rabinovitch, “Interior models of earthquake damaged buildings for search and rescue,” Adv. Eng. Inform., vol. 30, no. 1, pp. 65–76, 2016.
- [75] R. Zeibak-Shini, R. Sacks, L. Ma, and S. Filin, “Towards generation of as-damaged BIM models using laser-scanning and as-built BIM: First estimate of as-damaged locations of reinforced concrete frame members in masonry infill structures,” Adv. Eng. Inform., vol. 30, no. 3, pp. 312–326, 2016.
- [76] L. Ma, R. Sacks, R. Zeibak-Shini, A. Aryal, and S. Filin, “Preparation of synthetic as-damaged models for post-earthquake BIM reconstruction research,” J. Comput. Civil Eng., vol. 30, no. 3, pp. 1–12, 2016.
- [77] S. Lagüela, L. D\’\iaz-Vilariño, J. Mart\’\inez, and J. Armesto, “Automatic thermographic and RGB texture of as-built BIM for energy rehabilitation purposes,” Automat. Construction, vol. 31, pp. 230–240, 2013.
- [78] R. Otero, E. Fr\’\ias, S. Lagüela, and P. Arias, “Automatic gbXML modeling from LiDAR data for energy studies,” Remote Sens., vol. 12, no. 17, 2020.
- [79] L. D\’\iaz-Vilariño, S. Lagüela, J. Armesto, and P. Arias, “Semantic as-built 3D models including shades for the evaluation of solar influence on buildings,” Sol. Energy, vol. 92, pp. 269–279, 2013.
- [80] F. Celis and M. De Miguel, “Integrated system for energy optimization and reduction of towards a nearly zero impact built volume I organized by partners,” 2016.
- [81] M. Marzouk and R. Ahmed, “BIM-Based facility management for water treatment plants using laser scanning,” Water Pract. Technol., vol. 14, no. 2, pp. 325–330, Jun. 2019.
- [82] C. Thomson and J. Boehm, “Automatic Geometry Generation from Point Clouds for BIM,” Remote Sens., vol. 7, no. 9, pp. 11753–11775, 2015.
- [83] C. Wang, Y. K. Cho, and C. Kim, “Automatic BIM component extraction from point clouds of existing buildings for sustainability applications,” Automat. Construction, vol. 56, pp. 1–13, 2015.
- [84] X. Xiong, A. Adan, B. Akinci, and D. Huber, “Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data,” Automat. Construction, vol. 31, pp. 325–337, 2013.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-39e43475-38f4-4117-bf32-e39be8910e30
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