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Otomatik Üretim Teknolojisine Uygun Betonların Basınç Dayanımlarının Makine Öğrenmesi Yöntemiyle Belirlenmesi

Yıl 2021, Sayı: 32, 728 - 735, 31.12.2021
https://doi.org/10.31590/ejosat.1041528

Öz

Beton, yapı malzemeleri arasında mekanik özellikleri en karmaşık kompozitlerin başında gelmektedir. Betonun en temel mekanik özelliği ise basınç dayanımıdır. Betonarme yapılarda betonun basınç dayanımını hızlı, doğru, maliyetsiz ve zahmetsizce belirlemek oldukça önemli bir hedeftir. Bu çalışmada, beton basınç dayanımı belirtilen amaçlar göz önünde bulundurularak görüntü işleme ve makine öğrenmesi yöntemleri ile tahmin edilmektedir. Önerilen yöntemde uygulama yapılabilmesi için otomatik üretim teknolojisine uygun 48 adet küp numune hazırlanmıştır. Hazırlanan beton numunelerinde uçucu kül ve lif değişkenlerinin oluştuğu 6 farklı beton tipi kullanılmıştır. Beton numunelerinden elde edilen görüntüler ilk olarak ön işlem adımlarından geçerek veriseti hazırlanmıştır. Sonraki aşamada, beton görüntülerinin öznitelikleri Gri Seviye Eş Oluşum Matrisi (GLCM) yöntemi kullanılarak çıkarılmıştır. Elde edilen öznitelikler içinden rasgele seçilen %80’i eğitim, geri kalanı ise test seti olarak belirlenmiştir. Son adımda ise, K-En Yakın Komşu (KNN) algoritması uygulanarak betonda kullanılan malzemelerin değişimi ile beton basınç dayanımı ve görüntülerinde oluşan farklılıklar araştırılmıştır. Önerilen yöntem 100 kez tekrarlanarak doğruluk oranlarının ortalaması alınmıştır. Bulunan sonuçlar otomatik üretim teknolojisine uygun beton görüntüleri ile basınç dayanımı arasında %79.7’lik başarım oranı sağlanmıştır. Bu sayede gelişimini her geçen gün artıran otomatik üretim teknolojisine uygun yapıların betonun en önemli mekanik özelliklerinden biri olan basınç dayanımı tahminleri, görüntüler üzerinden yapılabileceği ortaya konulmuştur.

Kaynakça

  • Akhnoukh, A. K. (2020). Advantages of Contour Crafting in Construction Applications. Recent Patents on Engineering, 15(3), 294–300. doi:10.2174/1872212114666200218111631
  • Ayata, F. (2020). İçerik Tabanlı Görüntü Erişim Yöntemleriyle Aile Bireylerinde Yüz Tanıma Sistemi. Van yüzüncü Yıl Üniversitesi, Fen Bilimleri Üniversitesi Doktora Tezi.
  • Breysse, D. (2012). Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods. Construction and Building Materials, 33, 139–163. doi:10.1016/j.conbuildmat.2011.12.103
  • Chang, S., Cohen, T. and Ostdiek, B. (2018). What is the machine learning? Physical Review D, 97(5). doi:10.1103/PhysRevD.97.056009
  • Flah, M., Suleiman, A. R. and Nehdi, M. L. (2020). Classification and quantification of cracks in concrete structures using deep learning image-based techniques. Cement and Concrete Composites, 114. doi:10.1016/j.cemconcomp.2020.103781
  • Gibson, I., Rosen, D. and Stucker, B. (2015). Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing, second edition. Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, Second Edition. doi:10.1007/978-1-4939-2113-3
  • Gul, J. Z., Sajid, M., Rehman, M. M., Siddiqui, G. U., Shah, I., Kim, K. H., … Choi, K. H. (2018). 3D printing for soft robotics–a review. Science and Technology of Advanced Materials. doi:10.1080/14686996.2018.1431862
  • Hager, I., Golonka, A. and Putanowicz, R. (2016). 3D Printing of Buildings and Building Components as the Future of Sustainable Construction? Procedia Engineering in (Vol. 151, pp. 292–299). doi:10.1016/j.proeng.2016.07.357
  • Haralick, R. M., Dinstein, I. and Shanmugam, K. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610–621. doi:10.1109/TSMC.1973.4309314
  • Kabay, N. and Aköz, F. (2003). Yapıda beton kalitesinin tahribatlı ve tahribatsız yöntemlerle belirlenmesi. TMMOB İnşaat Mühendisleri Odası XVII.Teknik Kongresi in .
  • Kang, M. C., Yoo, D. Y. and Gupta, R. (2021). Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Construction and Building Materials, 266. doi:10.1016/j.conbuildmat.2020.121117
  • Kazemian, A., Yuan, X., Cochran, E. and Khoshnevis, B. (2017). Cementitious materials for construction-scale 3D printing: Laboratory testing of fresh printing mixture. Construction and Building Materials, 145, 639–647. doi:10.1016/j.conbuildmat.2017.04.015
  • Khorramshahi, M. R. and Mokhtari, A. (2017). Automatic construction by contour crafting technology. Emerging Science Journal, 1(1), 28–33. doi:10.28991/esj-2017-01113
  • Khoshnevis, B., Carlson, A., Leach, N. and Thangavelu, M. (2012). Contour crafting simulation plan for lunar settlement infrastructure buildup. Earth and Space 2012 - Proceedings of the 13th ASCE Aerospace Division Conference and the 5th NASA/ASCE Workshop on Granular Materials in Space Exploration in (pp. 1458–1467). doi:10.1061/9780784412190.155
  • Khoshnevis, Behrokh. (2004). Automated construction by contour crafting - Related robotics and information technologies. Automation in Construction in (Vol. 13, pp. 5–19). doi:10.1016/j.autcon.2003.08.012
  • Khoshnevis, Behrokh, Hwang, D., Yao, K. T. and Yeh, Z. (2006). Mega-scale fabrication by Contour Crafting. International Journal of Industrial and Systems Engineering, 1(3), 301–320. doi:10.1504/IJISE.2006.009791
  • Khoshnevis, Behrokh, Thangavelu, M., Yuan, X. and Zhang, J. (2013). Advances in contour crafting technology for extraterrestrial settlement infrastructure buildup. AIAA SPACE 2013 Conference and Exposition in . doi:10.2514/6.2013-5438
  • Kim, C. N., Kawamura, K., Nakamura, H. and Tarighat, A. (2020). Automatic Crack Detection for Concrete Infrastructures Using Image Processing and Deep Learning. IOP Conference Series: Materials Science and Engineering in (Vol. 829). doi:10.1088/1757-899X/829/1/012027
  • Kruger, J., Cicione, A., Bester, F., van den Heever, M., Cho, S., Walls, R. and van Zijl, G. (2020). Facilitating Ductile Failure of 3D Printed Concrete Elements in Fire. RILEM Bookseries in (Vol. 28, pp. 449–458). doi:10.1007/978-3-030-49916-7_46
  • LaMonica, M. (2013). Additive Manufacturing-Innovations, Advances, and Applications. MIT Technology Review.
  • Leach, N., Carlson, A., Khoshnevis, B. and Thangavelu, M. (2012). Robotic construction by contour crafting: The case of lunar construction. International Journal of Architectural Computing, 10(3), 423–438. doi:10.1260/1478-0771.10.3.423
  • Li, M., Yang, B., Wang, L., Liu, Y., Zhao, X., Zhou, J. and Zhang, L. (2016). The prediction of cement compressive strength based on gray level images and neural network. 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016 in (pp. 103–108). doi:10.1109/ICCSS.2016.7586432
  • Mahesh, B. (2020). Machine Learning Algorithms-A Review. International Journal of Science and Research, 9(1), 381–386.
  • Mechtcherine, V., Grafe, J., Nerella, V. N., Spaniol, E., Hertel, M. and Füssel, U. (2018). 3D-printed steel reinforcement for digital concrete construction – Manufacture, mechanical properties and bond behaviour. Construction and Building Materials, 179, 125–137. doi:10.1016/j.conbuildmat.2018.05.202
  • Nazmul, I. M. and Matsumoto, T. (2008). High resolution COD image analysis for health monitoring of reinforced concrete structures through inverse analysis. International Journal of Solids and Structures, 45(1), 159–174. doi:10.1016/j.ijsolstr.2007.07.014
  • Özen, M. (2007). Investigation of relationship between aggregate shape parameters and concrete strength using imaging techniques. Doctoral Thesis. https://etd.lib.metu.edu.tr/upload/12608324/index.pdf from retrieved.
  • Parmar, H., Khan, T., Tucci, F., Umer, R. and Carlone, P. (2021). Advanced robotics and additive manufacturing of composites: towards a new era in Industry 4.0. Materials and Manufacturing Processes. doi:10.1080/10426914.2020.1866195
  • Salet, T. A. M., Ahmed, Z. Y., Bos, F. P. and Laagland, H. L. M. (2018). Design of a 3D printed concrete bridge by testing*. Virtual and Physical Prototyping. doi:10.1080/17452759.2018.1476064
  • Shirooyeh, M., Vali, M., Shackleford, D., Torabi, P., Rehrig, P. W., Kwon, O. H. and Khoshnevis, B. (2015). Contour Crafting of Advanced Ceramic Terials. Advanced Processing and Manufacturing Technologies for Nanostructured and Multifunctional Materials II: A Collection of Papers Presented at the 39th International Conference on Advanced Ceramics and Composites January 25-30, 2015 Daytona Beach, Florida in (pp. 159–168). doi:10.1002/9781119211662.ch18
  • Sinha, S. K. and Fieguth, P. W. (2006). Automated detection of cracks in buried concrete pipe images. Automation in Construction, 15(1), 58–72. doi:10.1016/j.autcon.2005.02.006
  • Sousa, J. P., Palop, C. G., Moreira, E., Pinto, A. M., Lima, J., Costa, P., … Paulo Moreira, A. (2016). The SPIDERobot: A Cable-Robot System for On-site Construction in Architecture. Robotic Fabrication in Architecture, Art and Design 2016 in (pp. 230–239). doi:10.1007/978-3-319-26378-6_17
  • Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., … Martina, F. (2016). Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals - Manufacturing Technology, 65(2), 737–760. doi:10.1016/j.cirp.2016.05.004
  • Toklu, Y. C. and Çerçevik, A. E. (2017). Space research and extraterrestrial construction industry. Proceedings of 8th International Conference on Recent Advances in Space Technologies, RAST 2017 in (pp. 11–15). doi:10.1109/RAST.2017.8002938
  • Toklu, Y. C., Çerçevik, A. E. and Şahinöz, M. (2016). Otomatik Yapı Üretim Teknolojisinde Kullanılabilecek Malzemelerin Belirlenmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(1), 51. doi:10.19113/sdufbed.73967
  • TS 1247. (2018). Beton Yapım, Döküm Ve Bakım Kuralları (Normal Hava Koşullarında). Wiley, V. and Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. doi:10.29099/ijair.v2i1.42
  • Yamaguchi, T. and Hashimoto, S. (2009). Practical image measurement of crack width for real concrete structure. Electronics and Communications in Japan, 92(10), 1–12. doi:10.1002/ecj.10151
  • Zhang, J. and Khoshnevis, B. (2013). Optimal machine operation planning for construction by Contour Crafting. Automation in Construction, 29, 50–67. doi:10.1016/j.autcon.2012.08.006

Determination of Compressive Strength of Concretes Suitable for Automatic Production Technology by Machine Learning Method

Yıl 2021, Sayı: 32, 728 - 735, 31.12.2021
https://doi.org/10.31590/ejosat.1041528

Öz

Concrete is one of the most complex composites with mechanical properties among building materials. The most basic mechanical property of concrete is compressive strength. It is a very important goal to determine the compressive strength of concrete in reinforced concrete structures quickly, accurately, inexpensively and effortlessly. In this study, concrete compressive strength is estimated by image processing and machine learning methods, taking into account the mentioned purposes. In order to be able to apply the proposed method, 48 cube samples were prepared in accordance with automatic production technology. In the prepared concrete samples, 6 different concrete types were used in which fly ash and fiber variables were formed. The images obtained from the concrete samples were first prepared by going through the preprocessing steps. In the next step, the features of the concrete images were extracted using the Gray Level Co-occurrence Matrix (GLCM) method. Of the features obtained, 80% randomly selected were determined as training and the rest as test set. In the last step, by applying the K-Nearest Neighbor (KNN) algorithm, the changes in the materials used in the concrete and the differences in the concrete compressive strength and appearance were investigated. The proposed method was repeated 100 times and the average of the accuracy rates was taken. The results obtained showed a accuracy rate of 79.7% between the concrete images suitable for automatic production technology and the compressive strength. In this way, it has been revealed that the pressure strength estimations, which is one of the most important mechanical properties of concrete, can be made through images of structures suitable for automatic production technology, which increases its development day by day.

Kaynakça

  • Akhnoukh, A. K. (2020). Advantages of Contour Crafting in Construction Applications. Recent Patents on Engineering, 15(3), 294–300. doi:10.2174/1872212114666200218111631
  • Ayata, F. (2020). İçerik Tabanlı Görüntü Erişim Yöntemleriyle Aile Bireylerinde Yüz Tanıma Sistemi. Van yüzüncü Yıl Üniversitesi, Fen Bilimleri Üniversitesi Doktora Tezi.
  • Breysse, D. (2012). Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods. Construction and Building Materials, 33, 139–163. doi:10.1016/j.conbuildmat.2011.12.103
  • Chang, S., Cohen, T. and Ostdiek, B. (2018). What is the machine learning? Physical Review D, 97(5). doi:10.1103/PhysRevD.97.056009
  • Flah, M., Suleiman, A. R. and Nehdi, M. L. (2020). Classification and quantification of cracks in concrete structures using deep learning image-based techniques. Cement and Concrete Composites, 114. doi:10.1016/j.cemconcomp.2020.103781
  • Gibson, I., Rosen, D. and Stucker, B. (2015). Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing, second edition. Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, Second Edition. doi:10.1007/978-1-4939-2113-3
  • Gul, J. Z., Sajid, M., Rehman, M. M., Siddiqui, G. U., Shah, I., Kim, K. H., … Choi, K. H. (2018). 3D printing for soft robotics–a review. Science and Technology of Advanced Materials. doi:10.1080/14686996.2018.1431862
  • Hager, I., Golonka, A. and Putanowicz, R. (2016). 3D Printing of Buildings and Building Components as the Future of Sustainable Construction? Procedia Engineering in (Vol. 151, pp. 292–299). doi:10.1016/j.proeng.2016.07.357
  • Haralick, R. M., Dinstein, I. and Shanmugam, K. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610–621. doi:10.1109/TSMC.1973.4309314
  • Kabay, N. and Aköz, F. (2003). Yapıda beton kalitesinin tahribatlı ve tahribatsız yöntemlerle belirlenmesi. TMMOB İnşaat Mühendisleri Odası XVII.Teknik Kongresi in .
  • Kang, M. C., Yoo, D. Y. and Gupta, R. (2021). Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Construction and Building Materials, 266. doi:10.1016/j.conbuildmat.2020.121117
  • Kazemian, A., Yuan, X., Cochran, E. and Khoshnevis, B. (2017). Cementitious materials for construction-scale 3D printing: Laboratory testing of fresh printing mixture. Construction and Building Materials, 145, 639–647. doi:10.1016/j.conbuildmat.2017.04.015
  • Khorramshahi, M. R. and Mokhtari, A. (2017). Automatic construction by contour crafting technology. Emerging Science Journal, 1(1), 28–33. doi:10.28991/esj-2017-01113
  • Khoshnevis, B., Carlson, A., Leach, N. and Thangavelu, M. (2012). Contour crafting simulation plan for lunar settlement infrastructure buildup. Earth and Space 2012 - Proceedings of the 13th ASCE Aerospace Division Conference and the 5th NASA/ASCE Workshop on Granular Materials in Space Exploration in (pp. 1458–1467). doi:10.1061/9780784412190.155
  • Khoshnevis, Behrokh. (2004). Automated construction by contour crafting - Related robotics and information technologies. Automation in Construction in (Vol. 13, pp. 5–19). doi:10.1016/j.autcon.2003.08.012
  • Khoshnevis, Behrokh, Hwang, D., Yao, K. T. and Yeh, Z. (2006). Mega-scale fabrication by Contour Crafting. International Journal of Industrial and Systems Engineering, 1(3), 301–320. doi:10.1504/IJISE.2006.009791
  • Khoshnevis, Behrokh, Thangavelu, M., Yuan, X. and Zhang, J. (2013). Advances in contour crafting technology for extraterrestrial settlement infrastructure buildup. AIAA SPACE 2013 Conference and Exposition in . doi:10.2514/6.2013-5438
  • Kim, C. N., Kawamura, K., Nakamura, H. and Tarighat, A. (2020). Automatic Crack Detection for Concrete Infrastructures Using Image Processing and Deep Learning. IOP Conference Series: Materials Science and Engineering in (Vol. 829). doi:10.1088/1757-899X/829/1/012027
  • Kruger, J., Cicione, A., Bester, F., van den Heever, M., Cho, S., Walls, R. and van Zijl, G. (2020). Facilitating Ductile Failure of 3D Printed Concrete Elements in Fire. RILEM Bookseries in (Vol. 28, pp. 449–458). doi:10.1007/978-3-030-49916-7_46
  • LaMonica, M. (2013). Additive Manufacturing-Innovations, Advances, and Applications. MIT Technology Review.
  • Leach, N., Carlson, A., Khoshnevis, B. and Thangavelu, M. (2012). Robotic construction by contour crafting: The case of lunar construction. International Journal of Architectural Computing, 10(3), 423–438. doi:10.1260/1478-0771.10.3.423
  • Li, M., Yang, B., Wang, L., Liu, Y., Zhao, X., Zhou, J. and Zhang, L. (2016). The prediction of cement compressive strength based on gray level images and neural network. 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016 in (pp. 103–108). doi:10.1109/ICCSS.2016.7586432
  • Mahesh, B. (2020). Machine Learning Algorithms-A Review. International Journal of Science and Research, 9(1), 381–386.
  • Mechtcherine, V., Grafe, J., Nerella, V. N., Spaniol, E., Hertel, M. and Füssel, U. (2018). 3D-printed steel reinforcement for digital concrete construction – Manufacture, mechanical properties and bond behaviour. Construction and Building Materials, 179, 125–137. doi:10.1016/j.conbuildmat.2018.05.202
  • Nazmul, I. M. and Matsumoto, T. (2008). High resolution COD image analysis for health monitoring of reinforced concrete structures through inverse analysis. International Journal of Solids and Structures, 45(1), 159–174. doi:10.1016/j.ijsolstr.2007.07.014
  • Özen, M. (2007). Investigation of relationship between aggregate shape parameters and concrete strength using imaging techniques. Doctoral Thesis. https://etd.lib.metu.edu.tr/upload/12608324/index.pdf from retrieved.
  • Parmar, H., Khan, T., Tucci, F., Umer, R. and Carlone, P. (2021). Advanced robotics and additive manufacturing of composites: towards a new era in Industry 4.0. Materials and Manufacturing Processes. doi:10.1080/10426914.2020.1866195
  • Salet, T. A. M., Ahmed, Z. Y., Bos, F. P. and Laagland, H. L. M. (2018). Design of a 3D printed concrete bridge by testing*. Virtual and Physical Prototyping. doi:10.1080/17452759.2018.1476064
  • Shirooyeh, M., Vali, M., Shackleford, D., Torabi, P., Rehrig, P. W., Kwon, O. H. and Khoshnevis, B. (2015). Contour Crafting of Advanced Ceramic Terials. Advanced Processing and Manufacturing Technologies for Nanostructured and Multifunctional Materials II: A Collection of Papers Presented at the 39th International Conference on Advanced Ceramics and Composites January 25-30, 2015 Daytona Beach, Florida in (pp. 159–168). doi:10.1002/9781119211662.ch18
  • Sinha, S. K. and Fieguth, P. W. (2006). Automated detection of cracks in buried concrete pipe images. Automation in Construction, 15(1), 58–72. doi:10.1016/j.autcon.2005.02.006
  • Sousa, J. P., Palop, C. G., Moreira, E., Pinto, A. M., Lima, J., Costa, P., … Paulo Moreira, A. (2016). The SPIDERobot: A Cable-Robot System for On-site Construction in Architecture. Robotic Fabrication in Architecture, Art and Design 2016 in (pp. 230–239). doi:10.1007/978-3-319-26378-6_17
  • Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., … Martina, F. (2016). Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals - Manufacturing Technology, 65(2), 737–760. doi:10.1016/j.cirp.2016.05.004
  • Toklu, Y. C. and Çerçevik, A. E. (2017). Space research and extraterrestrial construction industry. Proceedings of 8th International Conference on Recent Advances in Space Technologies, RAST 2017 in (pp. 11–15). doi:10.1109/RAST.2017.8002938
  • Toklu, Y. C., Çerçevik, A. E. and Şahinöz, M. (2016). Otomatik Yapı Üretim Teknolojisinde Kullanılabilecek Malzemelerin Belirlenmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(1), 51. doi:10.19113/sdufbed.73967
  • TS 1247. (2018). Beton Yapım, Döküm Ve Bakım Kuralları (Normal Hava Koşullarında). Wiley, V. and Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. doi:10.29099/ijair.v2i1.42
  • Yamaguchi, T. and Hashimoto, S. (2009). Practical image measurement of crack width for real concrete structure. Electronics and Communications in Japan, 92(10), 1–12. doi:10.1002/ecj.10151
  • Zhang, J. and Khoshnevis, B. (2013). Optimal machine operation planning for construction by Contour Crafting. Automation in Construction, 29, 50–67. doi:10.1016/j.autcon.2012.08.006
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Nihan Kazak Çerçevik 0000-0001-5339-0055

Hüseyin Kayhan 0000-0001-5585-8993

Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 32

Kaynak Göster

APA Kazak Çerçevik, N., & Kayhan, H. (2021). Otomatik Üretim Teknolojisine Uygun Betonların Basınç Dayanımlarının Makine Öğrenmesi Yöntemiyle Belirlenmesi. Avrupa Bilim Ve Teknoloji Dergisi(32), 728-735. https://doi.org/10.31590/ejosat.1041528