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

Year 2023, Volume: 38 Issue: 3, 1561 - 1576, 06.01.2023

Burcu Güldür Erkal

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

Abstract

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.

Keywords

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

Supporting Institution

TÜBİTAK

Project Number

217M513

Thanks

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

Abstract

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.

Keywords

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

Project Number

217M513

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