Nokta bulutları kullanılarak hafif çelik elemanlardaki şekilsel kusurların doğru tespit edilmesi ve boyutlandırılması

Yıl 2023, Cilt: 38 Sayı: 3, 1561 - 1576, 06.01.2023

Burcu Güldür Erkal

https://doi.org/10.17341/gazimmfd.1086437

Öz

Son yıllarda az ve orta katlı yapılarda hafif çelik kullanımı yaygınlaşmıştır. Hafif çelik elamanlar yüksek dayanım-ağırlık oranına sahiptir ve bu elamanları kullanarak oluşturulan yapıların inşaası kısa sürdüğünden hızlı yapılanma gereksinimleri karşılamak açısından etkili bir çözüm sunmaktadır. Hafif çelik yapımın avantajlarının yanında dezavantajları da bulunmaktadır ve bu dezavantajlardan bir tanesi imalat, nakliye ve kurulum süreçleri sırasında elemanda meydana gelen şekilsel kusurların eleman davranışını etkilemesidir. Bu araştırma, C-kesitli hafif çelik elemanlarda bulunan şekilsel kusurların doğru bir şekilde tespit edilmesi ve boyutlandırılması üzerine yoğunlaşmaktadır. İyileştirilmiş otomatik şekilsel kusur tespit ve boyutlandırma yöntemi kullanılarak hafif çelik elemanlarda bulunan lokal ve global hasarlar tespit edilmiştir. Elde edilen sonuçlar, literatürdeki çalışmalar taban alınarak oluşturulmuş ikinci bir şekilsel kusur tespit ve boyutlandırma yönteminin sonuçları ile karşılaştırılmıştır. Yürütülen çalışma sonucunda, formulasyonu değişmeyen ve başlangıçta uygulanan ideal geometri modeli yerleştirme işleminden birebir etkilenmeyen şekilsel kusurlar dışında bütün elemanlar için iyileştirilmiş şekilsel kusur çıkarımı yöntemi ile hesaplanan maksimum ve ortalama şekilsel kusur değerlerinde eskiye oranla %50 ve üzerinde düşüş görülmüştür. İyileştirilmiş yöntemin hem lokal hem de global şekilsel kusurları gerçeğe uygun tespit ettiği gözlemlenmiştir.

Anahtar Kelimeler

Hafif çelik, c-kesitli eleman, şekilsel kusur, nokta bulutu, otomatik kusur tespiti

Destekleyen Kurum

TÜBİTAK

Proje Numarası

217M513

Teşekkür

Yazarlar bu araştırmaya katkılarından dolayı Arkitech Ileri Yapı Teknolojileri ve Polygon Mühendislik’e teşekkür eder. Bu makalede sunulanlar, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 217M513 numaralı TÜBİTAK 3501 projesi kapsamında Hacettepe Üniversitesi’nde yürütülen çalışmalara dayanmaktadır. Makalede verilen görüş, bulgu, sonuç ve tavsiyeler yazarlara aittir, TÜBİTAK'ın görüşlerini yansıtmayabilir.

Accurate geometric imperfection detection and quantification of cold-formed steel members from point clouds

Öz

In recent years, the use of cold-formed steel (CFS) in low and medium-rise buildings has become widespread. CFS members have a high strength-to-weight ratio, and since the construction of the structures performed using these elements takes a short time, it offers an effective solution in terms of meeting the requirements of rapid construction. CFS construction has advantages as well as disadvantages, and one of these disadvantages is that the geometric imperfections that occur in the member during the manufacturing, transportation, and installation processes affect the element's behavior. This research focuses on accurately detecting and quantifying the geometric imperfections found in C-sectioned CFS members. Local and global imperfections in CFS members are determined using the improved automatic geometric imperfection detection and quantification method. The results obtained are compared with a previously developed, literature-based geometric imperfection detection and quantification method. As a result of this study, it is observed that the maximum and average geometric imperfection values calculated by the improved geometric imperfection detection and quantification method for all elements decreased by 50% or more, except for the geometric imperfections whose formulation remain same and which are not directly affected by the initial ideal geometric model placement process. It has been verified that the improved method accurately detects both local and global geometric imperfections.

Anahtar Kelimeler

Cold-formed steel, c-sectioned member, geometric imperfection, point cloud, automated imperfection detection

Proje Numarası

217M513

Kaynakça

• Schafer, B. and T. Peköz. "Computational Modeling of Cold-Formed Steel: Characterizing Geometric Imperfections and Residual Stresses," Journal of Constructional Steel Research, 47(3): p. 193-210, 1998.
• Dubina, D. and V. Ungureanu. "Effect of Imperfections on Numerical Simulation of Instability Behaviour of Cold-Formed Steel Members," Thin-Walled Structures, 40(3): p. 239-262, 2002.
• Young, B. and K.J. Rasmussen. "Measurement techniques in the testing of thin-walled structural members," Experimental mechanics, 43(1): p. 32-38, 2003.
• Peterman, K.D., "Experiments on the stability of sheathed cold-formed steel studs under axial load and bending", John's Hopkins University, 2012.
• Zeinoddini, V. and B. Schafer. "Simulation Of Geometric Imperfections in Cold-Formed Steel Members using Spectral Representation Approach," Thin-Walled Structures, 60: p. 105-117, 2012.
• Sadovský, Z., J. Kriváček, V. Ivančo, and A. Ďuricová. "Computational Modelling of Geometric Imperfections and Buckling Strength of Cold-Formed Steel," Journal of Constructional Steel Research, 78: p. 1-7, 2012.
• Lecce, M. and K.J. Rasmussen. "Finite Element Modelling and Design of Cold-Formed Stainless Steel Sections (No. R845)," 2005.
• Zeinoddini, V. and B.W. Schafer. "Global Imperfections and Dimensional Variations in Cold-Formed Steel Members," International Journal of Structural Stability and Dynamics, 11(05): p. 829-854, 2011.
• Zhao, X., M. Tootkaboni, and B. Schafer. "Development of a laser-based geometric imperfection measurement platform with application to cold-formed steel construction," Experimental Mechanics, 55(9): p. 1779-1790, 2015.
• Zhao, X., M. Tootkaboni, and B.W. Schafer. "Laser-Based Cross-Section Measurement of Cold-Formed Steel Members: Model Reconstruction and Application," Thin-Walled Structures, 120: p. 70-80, 2017.
• McAnallen, L., 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, 2014.
• Salomon, A.L., 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, Florida, 2016.
• Mulligan, G.P. and T. Peköz, "The influence of local buckling on the structural behavior of singly-symmetric cold-formed steel columns", Missouri University of Science and Technology, 1983.
• Young, B., "The behaviour and design of the cold formed channel columns", in Department of Civil Engineering, Univ. of Sydney: Sydney, 1997.
• Selvaraj, S. and M. Madhavan. "Geometric Imperfection Measurements and Validations on Cold-Formed Steel Channels Using 3D Noncontact Laser Scanner," Journal of Structural Engineering, 144(3), 2018.
• Zhao, X., M.P. Tootkaboni, and B.W. Schafer. "High Fidelity Imperfection Measurements and Characterization for Cold-Formed Steel Members," in Proceedings of the 7th International Conference on Coupled Instabilities in Metal Structures, 2016.
• Farzanian, S., A. Louhghalam, B.W. Schafer, and M. Tootkaboni, "Geometric Imperfections in Shell Finite Element Models of CFS Members - A Review of Current State of Practice", in Proceedings of the Annual Stability Conference Structural Stability Research Council: Baltimore, Maryland, 2018.
• Zeinoddini-Meimand, V., "Geometric imperfections in cold-formed steel members. 2011: The Johns Hopkins University, 2011. Jahanshahi, M.R. and S.F. Masri. "Adaptive Vision-based Crack Detection using 3D Scene Reconstruction for Condition Assessment of Structures," Automation in Construction, 22: p. 567-576, 2012.
• Guldur, B. and J.F. Hajjar. "Laser-based Automatic Cross-Sectional Change Detection for Steel Frames," in Proceedings of the 9th International Workshop on Structural Health Monitoring 2013, Stanford University, Stanford, CA, 2013.
• Anil, E.B., B. Akinci, O. Kurc, and J.H. Garrett. "Building-Information-Modeling–Based Earthquake Damage Assessment for Reinforced Concrete Walls," Journal of Computing in Civil Engineering: p. 04015076, 2015.
• Torok, M.M., M. Golparvar-Fard, and K.B. Kochersberger. "Image-Based Automated 3D Crack Detection for Post-Disaster Building Assessment," Journal of Computing in Civil Engineering, Technical Paper A4014004: p. 1-13, 2013.
• Xiong, Z., Q. Li, Q. Mao, and Q. Zou. "A 3D Laser Profiling System for Rail Surface Defect Detection," Sensors, 17(8): p. 1791, 2017. Guldur Erkal, B. and J.F. Hajjar. "Laser-Based Surface Damage Detection and Quantification using Predicted Surface Properties," Automation in Construction, 83: p. 285-302, 2017.
• Feng, P., Y. Zou, L. Hu, and T. Liu. "Use of 3D laser scanning on evaluating reduction of initial geometric imperfection of steel column with pre-stressed CFRP," Engineering Structures, 198: p. 109527, 2019.
• Guldur Erkal, B. and J.F. Hajjar. "Using extracted member properties for laser-based surface damage detection and quantification," Structural Control Health Monitoring, e2616: p. 1-27, 2020.
• Cagrici, O., "3D Point Cloud-Based Imperfection Determination of Cold-Formed Steel Members For Numerical Modeling", in Department of Civil Engineering, Hacettepe University: Ankara, Turkey, 2021.
• AICON 3D. AICON Scanner, Smart Scan. 2017; Available from: http://aicon3d.com/start.html. Jolliffe, I., "Principal Components in Regression Analysis, 2nd Edition. Principal Component Analysis. 2002, Medford, MA: Springer. 129-155, 2002.
• Kashani, A.G., M.J. Olsen, C.E. Parrish, and N. Wilson. "A review of LiDAR radiometric processing: From ad hoc intensity correction to rigorous radiometric calibration," Sensors, 15(11): p. 28099-28128, 2015.
• Laefer, D.F., J. Gannon, and E. Deely. "Reliability of crack detection methods for baseline condition assessments," Journal of Infrastructure Systems, 16(2): p. 129-137, 2010.
• Laefer, D.F., L. Truong-Hong, H. Carr, and M. Singh. "Crack Detection Limits in Unit Based Masonry with Terrestrial Laser Scanning," NDT&E International, 62: p. 66-76, 2014.
• Olsen, M.J., F. Kuester, B.J. Chang, and T.C. Hutchinson. "Terrestrial laser scanning-based structural damage assessment," Journal of Computing in Civil Engineering, 24(3): p. 264-272, 2010.
• ECCS, "European recommendations for the design for the design oflight gauge steel members", European Convention for Constructional Steelwork: Brussels, Belgium, 1987.
• AISI, "North American standard for cold-formed steel structural framing", in AISI S240-2015, American Iron and Steel Institute: Washington, DC, 2015.
• ASTM, "Standard specification for cold-formed steel structural framing members", in ASTM C955-17, American Society for Testing and Materials: West Conshohocken, PA, 2017.
• BS, "Execution of steel structures and aluminium structures. Technical requirements for steel structures", in EN 1090-2:2008, British Standard: UK, 2008.
• CEN, "Eurocode 3: Design of steel structures. I–V: Plated structural elements", in EN-19931-5:2006/AC:2009, European Committee for Standardization: Brussels, Belgium, 2009.