Hafif Çelik Elemanlar için İki Boyutlu Görüntülerden Üç Boyutlu Yüzey Ağı Elde Edilmesi

Abstract

Hafif çelik yapılarla ilgili yürütülen araştırmalardaki son gelişmeler ve güncellenen tasarım yönergeleri sayesinde hafif çelik elemanların inşaat süreçlerinde kullanımı önemli ölçüde artmıştır. Değişken kesit özelliklerine sahip hafif çelik elemanlar ince çelik sacların soğuk şekillendirme aracılıyla bükülmesiyle elde edilmektedir. Bu elemanların yapılarda kullanılmasıyla ekonomik, hafif, verimli ve esnek yapılar tasarlanabilmektedir. Fakat, yapısı gereği hafif çelik elemanları oluşturan ince çelik saclarda nakliye, kurulum ve hatta üretim nedeniyle şekilsel kusurlar oluşabilmektedir. Bu şekilsel kusurlar hafif çelik elemanların fiziksel davranışlarını etkileyebilmektedir. Bu nedenle, hafif çelik bir elemanın fiziksel davranışını doğru bir şekilde değerlendirebilmek için mevcut şekilsel kusurları belirlemek ve bu şekilsel kusurların hafif çelik eleman davranışı üzerindeki etkisini araştırmak gerekir. Ancak, hafif çelik elemanların kapsamlı şekilsel kusur dağılımlarını çıkarabilmek için detaylı yüzey verisine ihtiyaç vardır. Son yıllarda yürütülen çalışmalarda yüzey verisi üç boyutlu (3B) tarayıcı sistemler kullanılarak elde edilmektedir. Fakat, 3B veri toplama işlemi kullanılan özel ekipmanlar sebebiyle maliyetli bir işlemdir. Bu çalışmada, incelenen hafif çelik elemanların yüzeyinden standart bir kamera ile toplanan iki boyutlu (2B) görüntülerden kapsamlı 3B yüzey verilerinin çıkarılması için düşük maliyetli bir metodoloji önerilmiştir. Oluşturulan 3B verinin ileriki çalışmalarda şekilsel kusur çıkarımı için kullanılacak detay ve çözünürlükte olması hedeflenmiştir.

Keywords

• C-kesitli eleman
• Hafif çelik
• Nokta bulutu
• Şekilsel kusur
• Yüzey ağı

Supporting Institution

TÜBİTAK

Project Number

217M513

Thanks

Bu çalışma Hacettepe Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP) tarafından FHD-2017-15248 No'lu proje kapsamında desteklenmiştir. Yazar, test kurulumu tasarımı ve üretimi için destek ve rehberliklerinden dolayı Parkon İnşaat firmasına teşekkür eder. Bu makalede ifade edilen görüşler, bulgular ve sonuçlar yazara aittir ve Hacettepe Üniversitesi'nin görüşlerini yansıtmayabilir.

Obtaining Three-Dimensional Surface Meshes from Two-Dimensional Images for Cold-Formed Steel Members

Abstract

The use of cold-formed steel (CFS) in construction processes has increased significantly, thanks to the latest research on CFS structures and updated design guidelines. CFS members are obtained by forming thin steel sheets into different cross-section geometries. Using these elements in buildings, economic, light, efficient, and flexible structures could be designed. However, due to its nature, thin steel sheets that make up CFS members may have geometric imperfections resulting from transportation, installation, and production. These geometric imperfections could affect the physical behavior of CFS members. Therefore, to accurately evaluate a CFS member's physical behavior, it is necessary to identify the existing geometric imperfections and investigate their effect on the CFS member's behavior. However, detailed surface data is needed to extract comprehensive geometric imperfection distributions of CFS members. In studies carried out in recent years, surface data is obtained by using three-dimensional scanning systems. However, three-dimensional data collection is costly due to the special equipment used. In this study, a low-cost methodology is proposed to extract comprehensive three-dimensional (3D) surface data from two-dimensional (2D) images collected with a standard camera from the surface of the investigated CFS members. It is aimed that the created 3D representation will be in detail and resolution to be used for geometric imperfection extraction in future studies.

Keywords

• Cold-formed steel
• C-sectioned member
• Geometric imperfection
• Point cloud
• Surface mesh

Project Number

217M513

References

1. [1] B. Schafer and T. Peköz, "Computational modeling of cold-formed steel: Characterizing geometric imperfections and residual stresses," Journal of Constructional Steel Research, vol. 47, no. 3, pp. 193-210, 1998, doi: 10.1016/S0143-974X(98)00007-8.
2. [2] D. Dubina and V. Ungureanu, "Effect of imperfections on numerical simulation of instability behaviour of cold-formed steel members," Thin-Walled Structures, vol. 40, no. 3, pp. 239-262, 2002, doi: 10.1016/S0263-8231(01)00046-5.
3. [3] K. D. Peterman, "Experiments on the stability of sheathed cold-formed steel studs under axial load and bending," Ph.D. dissertation, Department of Civil Engineering, John's Hopkins University, Baltimore, MD, USA, 2012.
4. [4] Z. Sadovský, J. Kriváček, V. Ivančo, and A. Ďuricová, "Computational Modelling of Geometric Imperfections and Buckling Strength of Cold-Formed Steel," Journal of Constructional Steel Research, vol. 78, pp. 1-7, 2012.
5. [5] M. Lecce and K. J. Rasmussen, "Finite element modelling and design of cold-formed stainless steel sections," , Centre for Advanced Structural Engineering, Sydney, Australia, R845, 2005.
6. [6] V. Zeinoddini and B. Schafer, "Simulation of geometric imperfections in cold-formed steel members using spectral representation approach," Thin-Walled Structures, vol. 60, pp. 105-117, 2012, doi: 10.1016/j.tws.2012.07.001.
7. [7] X. Zhao, M. Tootkaboni, and B. Schafer, "Development of a laser-based geometric imperfection measurement platform with application to cold-formed steel construction," Experimental Mechanics, vol. 55, no. 9, pp. 1779-1790, 2015, doi: 10.1007/s11340-015-0072-7.
8. [8] L. McAnallen, D. Padilla-Llano, X. Zhao, C. Moen, B. Schafer, and M. Eatherton, "Initial geometric imperfection measurement and characterization of cold-formed steel C-section structural members with 3D non-contact measurement techniques," in Proceedings of the Structural Stability Research Council, Toronto, Canada, March 25-28, 2014.

9. [9] A. L. Salomon, D. Fratamico, B. W. Schafer, and C. D. Moen, "Full field cold-formed steel column buckling measurements with high resolution image-based reconstruction," in Proceedings of the Annual Stability Conference Structural Stability Research Council, Orlando, FL, USA, April 12-15, 2016.
10. [10] G. P. Mulligan, "The influence of local buckling on the structural behavior of singly-symmetric cold-formed steel columns," Ph.D. dissertation, Department of Civil Engineering, Cornell University,, Ithaca, NY, USA, 1983.
11. [11] B. Young, "The behaviour and design of the cold formed channel columns," Ph.D. dissertation, Department of Civil Engineering, University of Sydney, Sydney, Australia, 1997.
12. [12] X. Zhao, M. Tootkaboni, and B. W. Schafer, "Laser-based cross-section measurement of cold-formed steel members: model reconstruction and application," Thin-Walled Structures, vol. 120, pp. 70-80, 2017, doi: 10.1016/j.tws.2017.08.016.
13. [13] S. Selvaraj and M. Madhavan, "Geometric imperfection measurements and validations on cold-formed steel channels using 3D noncontact laser scanner," Journal of Structural Engineering, vol. 144, no. 3, 2018, doi: 10.1061/(ASCE)ST.1943-541X.0001993.
14. [14] V. M. Zeinoddini, "Geometric imperfections in cold-formed steel members," Ph.D dissertation, Civil Engineering, Johns Hopkins University, Baltimore, MD, 2011.
15. [15] S. Farzanian, A. Louhghalam, B. Schafer, and M. Tootkaboni, "Geometric imperfections in CFS structural members: Part I: A review of the basics and some modeling strategies," Thin-Walled Structures, vol. 186, p. 110619, 2023, doi: 10.1016/j.tws.2023.110619.
16. [16] M. R. Jahanshahi and S. F. Masri, "Adaptive vision-based crack detection using 3d scene reconstruction for condition assessment of structures," Automation in Construction, vol. 22, pp. 567-576, 2012, doi: 10.1016/j.autcon.2011.11.018.
17. [17] M. M. Torok, M. Golparvar-Fard, and K. B. Kochersberger, "Image-based automated 3D crack detection for post-disaster building assessment," Journal of Computing in Civil Engineering, vol. Technical Paper A4014004, pp. 1-13, 2013, doi: 10.1061/(ASCE)CP.1943-5487.0000334.
18. [18] Y.-F. Liu, S. Cho, B. Spencer Jr, and J.-S. Fan, "Concrete crack assessment using digital image processing and 3D scene reconstruction," Journal of Computing in Civil Engineering, vol. 30, no. 1, p. 04014124, 2016, doi: 10.1061/(ASCE)CP.1943-5487.0000446.
19. [19] Z. Zhou, J. Gong, and M. Guo, "Image-based 3D reconstruction for posthurricane residential building damage assessment," Journal of Computing in Civil Engineering, vol. 30, no. 2, p. 04015015, 2016, doi: 10.1061/(ASCE)CP.1943-5487.0000480.
20. [20] R. Kalfarisi, Z. Y. Wu, and K. Soh, "Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization," Journal of Computing in Civil Engineering, vol. 34, no. 3, p. 04020010, 2020, doi: 10.1061/(ASCE)CP.1943-5487.0000890.
21. [21] C. Koch, K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth, "A Review on Computer Vision Based Defect Detection and Condition Assessment of Concrete and Asphalt Civil Infrastructure," Advanced Engineering Informatics, vol. 29, no. 2, pp. 196-210, 2015, doi: 10.1016/j.aei.2015.01.008.
22. [22] Z. Ma and S. Liu, "A review of 3D reconstruction techniques in civil engineering and their applications," Advanced Engineering Informatics, vol. 37, pp. 163-174, 2018, doi: 10.1016/j.aei.2018.05.005.
23. [23] MatLab, MATLAB and Statistics Toolbox Release 2019a. Natick, MA:The MathWorks, Inc., 2019.
24. [24] Regard3D, Regard3D: A Structure-from-motion Program. Regard3D, 2018.
25. [25] Meshroom, Meshroom. AliceVision, 2021.
26. [26] D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2: IEEE, pp. 1150-1157.
27. [27] P. F. Alcantarilla and T. Solutions, "Fast explicit diffusion for accelerated features in nonlinear scale spaces," IEEE Trans. Patt. Anal. Mach. Intell, vol. 34, no. 7, pp. 1281-1298, 2011, doi: 10.1109/83.661190.
28. [28] MeshLab, MeshLab. CNR, 2022.
29. [29] G. A. Hansen, R. W. Douglass, and A. Zardecki, Mesh enhancement: selected elliptic methods, foundations and applications.London, United Kingdom:Imperial College Press, 2005.
30. [30] Y. Zhang, "Challenges and Advances in Image-Based Geometric Modeling and Mesh Generation," in Image-Based Geometric Modeling and Mesh Generation, Y. Zhang Ed. Dordrecht: Springer Netherlands, 2013, pp. 1-10..
31. [31] B. Guldur, "Laser-based Structural Sensing and Surface Damage Detection," Ph.D. Dissertation, Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts, 2014.
32. [32] B. Guldur Erkal, "Image-Based 3D Surface Reconstruction of Cold-Formed Steel C-Sections," in Proceedings of the 2019 European Conference on Computing in Construction, Chania, Crete, Greece 2019.
33. [33] B. Güldür Erkal and Ö. G. Çağrıcı, "Automated geometric imperfection detection and quantification of CFS members from point clouds," KSCE Journal of Civil Engineering, vol. 26, no. 9, pp. 3888-3904, 2022, , doi: 10.1007/s12205-022-0795-9.
34. [34] O. G. Cagrici, "3D point cloud-based imperfection determination of cold-formed steel members for numerical modeling," M.S. thesis, Department of Civil Engineering, Hacettepe University, Ankara, Turkey, 2021.
35. [35] V. Zeinoddini and B. W. Schafer, "Global imperfections and dimensional variations in cold-formed steel members," International Journal of Structural Stability and Dynamics, vol. 11, no. 05, pp. 829-854, 2011, doi: 10.1142/S0219455411004361.
36. [36] ECCS, European recommendations for the design for the design of light gauge steel members. Brussels, Belgium:European Convention for Constructional Steelwork, 1987.
37. [37] Execution of steel structures and aluminium structures - Part 2: Technical requirements for steel structures, EN 1090-2:2008, British Standard, UK, 2008.
38. [38] Eurocode 3: Design of steel structures - Part 1-3: General rules – Supplementary rules for cold-formed members and sheeting, EN-1993-1-3, Brussels, Belgium, 2006.
39. [39] Eurocode 3: Design of steel structures. I–V: Plated structural elements, EN-19931-5:2006/AC:2009 , Brussels, Belgium, 2009.
40. [40] North American Standard for Cold-Formed Steel Structural Framing, AISI-S240, Washington, D.C., USA, 2015.
41. [41] Standard specification for cold-formed steel structural framing members, ASTM C955-17, West Conshohocken, PA, USA, 2017.