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Derin Öğrenme ile Beton Yapılarda Otonom Çatlak Tespiti

Year 2022, Volume: 9 Issue: 2, 607 - 624, 31.05.2022
https://doi.org/10.31202/ecjse.983908

Abstract

Bu çalışma beton yapılarda çatlak tespitinin bilgisayarlı görü ve derin öğrenme kullanılarak otonom gerçekleştirilmesi konusundadır. Çatlakların hızlı ve başarılı şekilde tespit edilmesi yapısal hasar tespitinin erken ve etkili yapılabilmesi için oldukça önemlidir. Bu kapsamda çevrim içi kaynaklardan elde edilen, içerisinde çatlak fotoğrafları bulunan 3 farklı veri seti ve içerisinde çatlak fotoğrafı bulunmayan farklı yapıların farklı bölgelerine ait fotoğraflar bulunan bir veri seti kullanılmıştır. Derin öğrenme mimarisinin eğitimi ve testi için veri setlerinin farklı kombinasyonlarda kullanımı ile elde edilen sonuçlar tartışılmıştır. Derin öğrenme mimarisi olarak görüntü bölütlemesi (image segmentation) için tasarlanmış olan U-Net kullanılmıştır. Elde edilen bulgular betonarme yapılarda çatlak tespiti için eğitilen evrişimli sinir ağlarında eğitim verisi içerisinde yapıların farklı bölgelerine ait çatlak içermeyen fotoğrafların kullanılması ile çatlak tespiti konusundaki başarı oranının arttığını göstermiştir. Ayrıca evrişimli sinir ağının eğitimi ve testi için birbirinden farklı veri setlerinin kullanılması ile elde edilen sonuçlar U-Net mimarisinin gerçek dünyadaki çatlak tespit problemlerinde kullanılabilir olduğu konusunda ciddi bir öngörü kazandırmıştır. Yapılan bu çalışmanın yapısal çatlak tespiti konusunda araştırma yapmak veya proje geliştirmek isteyen araştırmacılar için faydalı olması ve araştırmacıların yararlanabilecekleri noktaları tanıtması umulmaktadır.

References

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  • Dung, C. V., Anh, L. D., “Autonomous concrete crack detection using deep fully convolutional neural network”, Automation in Construction, 2019, 99: 52–58.
  • Li, S., Zhao, X., Zhou, G., “Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network”, Computer-Aided Civil and Infrastructure Engineering, 2019, 34(7): 616–634.
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  • Li, B., Wang, K. C. P., Zhang, A., Yang, E., Wang, G., “Automatic classification of pavement crack using deep convolutional neural network”, International Journal of Pavement Engineering, 2020, 21(4): 457–463.
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Autonomous Crack Detection in Concrete Structures with Deep Learning

Year 2022, Volume: 9 Issue: 2, 607 - 624, 31.05.2022
https://doi.org/10.31202/ecjse.983908

Abstract

This study is about autonomous crack detection in concrete structures using computer vision and deep learning. Fast and successful detection of cracks is very important for early and effective detection of structural damage. In this context, 3 different data sets obtained from the online sources with crack photos and a data set with photos of different parts of different structures without crack photos were used. The results obtained by using different combinations of datasets for training and testing of deep learning architecture are discussed. U-Net, designed for image segmentation, was used as a deep learning architecture. The findings showed that the success rate of crack detection increased with the use of crack-free photographs of different parts of the structures in the training data in convolutional neural networks trained for crack detection in reinforced concrete structures. In addition, the results obtained by using different data sets for the training and testing of the convolutional neural network have given a serious prediction that the U-Net architecture can be used in real-world crack detection problems. It is hoped that this study will be useful for researchers who want to research or develop projects on structural crack detection and introduce the points that researchers can benefit from.

References

  • Karaçay, T., Özbaşaran, H., “YAPI MÜHENDİSLİĞİNDE YAPAY ZEKÂ: GEÇMİŞTEN GÜNÜMÜZE TÜRKÇE ÇALIŞMALAR”, MÜHENDİSLİK BİLİMLERİNDE YENİ YAKLAŞIMLAR, LIVRE DE LYON, Lyon, France, 2021.
  • Yang, C.-H., Wen, M.-C., Chen, Y.-C., Kang, S.-C., “An Optimized Unmanned Aerial System for Bridge Inspection”, 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings, Oulu, Finland, 1-6, 2015.
  • Qiao, W., Ma, B., Liu, Q., Wu, X., Li, G., “Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module”, Sensors, 2021, 21(3): 824-840.
  • Liu, Z., Cao, Y., Wang, Y., Wang, W., “Computer vision-based concrete crack detection using U-net fully convolutional networks”, Automation in Construction, 2019, 104: 129–139.
  • Cha, Y. J., Choi, W., Büyüköztürk, O., “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks”, Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361–378.
  • Modarres, C., Astorga, N., Droguett, E. L., Meruane, V., “Convolutional neural networks for automated damage recognition and damage type identification”, Structural Control Health Monitoring, 2018, 25(10): 1–17.
  • Dorafshan, S., Thomas, R. J., Maguire, M., “Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete”, Construction and Building Materials, 2018, 186: 1031–1045.
  • Özgenel, Ç. F., Sorguç, A. G., “Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings”, ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, Oulu, Finland, 693-700, 2018.
  • Özgenel, Ç. F., “Concrete Crack Images for Classification”, https://data.mendeley.com/datasets/5y9wdsg2zt/2 (Son Erişim: 21.06.2021).
  • Wang, N., Zhao, Q., Li, S., Zhao, X., Zhao, P., “Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images”, Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1073–1089.
  • Li, S., Zhao, X., “Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique”, Advances in Civil Engineering, 2019, 2019: 1-12.
  • Dung, C. V., Anh, L. D., “Autonomous concrete crack detection using deep fully convolutional neural network”, Automation in Construction, 2019, 99: 52–58.
  • Li, S., Zhao, X., Zhou, G., “Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network”, Computer-Aided Civil and Infrastructure Engineering, 2019, 34(7): 616–634.
  • Ren, Y., Huang, J., Hong, Z., Lu, W., Yin, J., Zou, L., Shen, Z., “Image-based concrete crack detection in tunnels using deep fully convolutional networks”, Construction and Building Materials, 2020, 234.
  • Li, B., Wang, K. C. P., Zhang, A., Yang, E., Wang, G., “Automatic classification of pavement crack using deep convolutional neural network”, International Journal of Pavement Engineering, 2020, 21(4): 457–463.
  • Özgenel, Ç. F., “Concrete Crack Segmentation Dataset”, https://data.mendeley.com/datasets/jwsn7tfbrp/1 (Son Erişim: 21.06.2021).
  • Middha, L., “Crack Segmentation Dataset Over 11,000 images with masks”, https://www.kaggle.com/lakshaymiddha/crack-segmentation-dataset (Son Erişim: 21.06.2021).
  • Ronneberger, O., Fischer, P., Brox, T., “U-Net: Convolutional Networks for Biomedical Image Segmentation”, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Munich, Germany, 234–241, 2015.
  • Hashmi, A., Golyshew, A., “UNet-Segmentation-Wolfram”, https://github.com/alihashmiii/UNet-Segmentation-Wolfram (Son Erişim: 21.06.2021).
  • Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., Fei-Fei, L., “ImageNet Large Scale Visual Recognition Challenge”, International Journal of Computer Vision, 2015, 115(3): 211–252.
  • He, K., Zhang, X., Ren, S., Sun, J., “Deep Residual Learning for Image Recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 770–778, 2016.
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Tarkan Karaçay 0000-0002-4893-5126

Publication Date May 31, 2022
Submission Date August 17, 2021
Acceptance Date October 22, 2021
Published in Issue Year 2022 Volume: 9 Issue: 2

Cite

IEEE T. Karaçay, “Derin Öğrenme ile Beton Yapılarda Otonom Çatlak Tespiti”, El-Cezeri Journal of Science and Engineering, vol. 9, no. 2, pp. 607–624, 2022, doi: 10.31202/ecjse.983908.
Creative Commons License El-Cezeri is licensed to the public under a Creative Commons Attribution 4.0 license.
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