Sürdürülebilir Kalkınma Amaçları

• 2

Yazarlar

• Reis, Hatice Catal(2)
• Alparslan Kiliç(1)
• Bozkurt, Muhammet Furkan(1)
• Karagoz, Seray Nur(1)
• Kaya Yildiz, Cagla Melisa(1)
• Khoshelham, Kourosh(1)
• Turk, Veysel(1)
• Ustuner, Mustafa(1)

• Yüksek Lisans TeziYalnız künye2026/r/gumushane/20501
  Akıllı telefon görüntüleri kullanılarak yapılardaki kırıkların otomatik tespiti

  Alparslan Kiliç

  Yapılardaki çatlakların/kırıkların erken ve doğru tespiti, yapı sağlığı ve bakım süreçlerinin planlanması açısından önem taşımaktadır. Bu çalışmada, akıllı telefon görüntüleri kullanılarak duvar ve köprü yüzeylerindeki çatlakların derin öğrenme yöntemleriyle otomatik olarak tespit edilmesi amaçlanmıştır. Duvar ve köprü veri setleri “çatlak” ve “çatlaksız” sınıflardan oluşmaktadır. Yerel veri seti ile SDNET2018 üzerinde evrişimli sinir ağı, dönüştürücü ve hibrit yapılardan oluşan on sekiz mimari; doğruluk, pozitif kestirim değeri, duyarlılık, özgüllük, F1 skoru, AUC ve görüntü başına çıkarım süresi bakımından karşılaştırılmıştır. Yerel deneylerde önerilen model, duvar veri setinde %98,50 ve köprü veri setinde %100,00 doğruluk sağlamıştır. Buna karşılık sıfır örnekli dış veri seti testlerinde doğruluk %52,43–%73,53 aralığına düşmüştür. Ham ortak eğitim sonrasında SDNET2018 duvar, köprü tabliyesi ve yol kaplama testlerinde sırasıyla %86,05, %81,35 ve %87,21 doğruluk elde edilmiştir; dengeli ortak eğitimde bu değerler %82,84, %80,86 ve %86,57 olarak gerçekleşmiştir. Bulgular, veri kaynağı farklılıklarının model genellemesi üzerinde belirleyici olduğunu ve çoklu alan ortak eğitimin dış veri seti başarısını artırabildiğini göstermektedir. Sonuç olarak, akıllı telefon görüntülerine dayalı çatlak/kırık tespiti afetlerde ve yapı sağlığı yönetiminde uzman değerlendirmesini destekleyen hızlı ve düşük maliyetli bir yaklaşım olarak sunulabilir.

  RDL DOI10.71008/gumushane.thesis.2026.258indekslendi
  İncele
• MakaleYalnız künye2025Scopus SJRQ1/r/gumushane/14331
  A deep neural network combined with a two-stage ensemble model for detecting cracks in concrete structures

  Reis, Hatice Catal; Turk, Veysel; Kaya Yildiz, Cagla Melisa; Bozkurt, Muhammet Furkan; Karagoz, Seray Nur; Ustuner, Mustafa

  Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes. Traditional inspection techniques are costly, time-consuming, and inefficient regarding human resources. Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency. Deep learning models (DLMs) stand out as an effective solution in crack detection due to their features such as end-to-end learning capability, model adaptation, and automatic learning processes. However, providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic. In this article, three different methods are proposed for detecting cracks in concrete structures. In the first method, a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network (SCAMEFNet) deep neural network, which has a deep architecture and can provide a balance between the depth of DLMs and model parameters, has been developed. This model was designed using a convolutional neural network, multi-head attention, and various fusion techniques. The second method proposes a modified vision transformer (ViT) model. A two-stage ensemble learning model, deep feature-based two-stage ensemble model (DFTSEM), is proposed in the third method. In this method, deep features and machine learning methods are used. The proposed approaches are evaluated using the Concrete Cracks Image Data set, which the authors collected and contains concrete cracks on building surfaces. The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%, the ViT model 97.33%, and the DFTSEM model 99.00%. These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to real-world problems. In addition, the developed methods can contribute as a tool for BIM platforms in smart cities for building health.

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  AtıfScopus2·WoS2·OpenAlex1

  İncele
• MakaleYalnız künye2021Scopus SJRQ2/r/gumushane/14585
  ReCRNet: a deep residual network for crack detection in historical buildings

  Reis, Hatice Catal; Khoshelham, Kourosh

  In historical buildings, surface cracks are important indicators of potential structural damage. Natural disasters and indirect human factors, which are frequently encountered in recent periods, negatively affect historical buildings and structures. Fast and cost-effective crack detection methods play a key role in structural health monitoring of historical buildings. This paper presents methodologies for identifying concrete cracks using deep learning. We propose ReCRNet, a deep learning architecture designed for classifying images of cracks. The performance of the proposed method is evaluated and compared with state-of-the-art methods such as AlexNet, VGG 19, linear support vector machine (SVM), and decision tree (DT). The results show that ReCRNet achieves better performance in terms of accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC), in comparison with the other crack classifiers. Accordingly, the proposed approach is recommended for automatic monitoring of historical buildings and building condition assessments.

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  AtıfScopus17·OpenAlex15

  İncele