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Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini

Year 2021, Volume: 9 Issue: 5, 1995 - 2011, 31.10.2021
https://doi.org/10.29130/dubited.919452

Abstract

Bu çalışmada Manyetik Aktif Karbon (MAK) ile modifiye edilmiş bitümlü bağlayıcının reolojik özellikleri araştırılmış ve sonuçlar yapar sinir ağları ile tahmin edilmiştir. Çalışma kapsamında B160/220 penetrasyon sınıfı bitümlü bağlayıcıya %5, %10 ve %15 oranlarında MAK ilave edilerek modifiye bitümler elde edilmiş, ardından bitümler üzerinde Dinamik Kayma Reometresi (DSR) cihazı ile on farklı frekansta (0.01-10Hz) ve dört farklı sıcaklıkta (40°,50°,60°,70°C) frekans taraması testi gerçekleştirilmiştir. Sonuçlar, MAK ilavesinin kompleks modül değerlerini artırıp, faz açısı değerlerini azaltarak bitümlü bağlayıcının elastik özelliklerini geliştirdiğini göstermiştir. Daha sonra frekans, katkı oranı ve sıcaklık değerlerine bağlı olarak değişen kompleks modül ve faz açısı değerleri yapay sinir ağları yöntemi ile tahmin edilmiştir. Sonuçlar, kompleks modül ve faz açısı değerlerinin oldukça yüksek doğrulukta düşük hata ile elde edilebileceğini göstermiştir.

Thanks

Manyetik Aktif Karbon (MAK) katkısının sentezlenmesindeki katkılarından dolayı Kırşehir Ahi Evran Üniversitesi Mühendislik-Mimarlık Fakültesi, Kimya ve Proses Mühendisliği Bölümü Öğretim Üyesi Doç. Dr. Hasan Arslanoğlu’na teşekkür ederiz.

References

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  • [2] D. N. Little, D. H. Allen, and A. Bhasin, Modeling and design of flexible pavements and materials, Springer International Publishing AG, 2017.
  • [3] Y. Erkuş, B. V. Kök, and M. Yilmaz, “Evaluation of performance and productivity of bitumen modified by three different additives,” Construction and Building Materials, vol. 261, pp. 120553, 2020.
  • [4] B. V. Kök, M. Yılmaz, and M. Akpolat, “Evaluation of the conventional and rheological properties of SBS+Sasobit modified binder,” Construction and Building Materials, vol. 63, pp. 174–179, 2014.
  • [5] P. Cong, P. Xu, and S. Chen, “Effects of carbon black on the anti aging, rheological and conductive properties of SBS/asphalt/carbon black composites,” Construction and Building Materials, vol. 52, pp. 306–313, 2014.
  • [6] S. Zhao, B. Huang, X. P. Ye, X. Shu, and X. Jia, “Utilizing bio-char as a bio-modifier for asphalt cement: A sustainable application of bio-fuel by-product,” Fuel, vol. 133, pp. 52–62, 2014.
  • [7] Ç. Muhammed Ertugrul, Y. Mehmet, B. V. Kök, and E. Yalçin, “Effects of various biochars on the high temperature performance of bituminous binder,” Jun. 2016.
  • [8] X. Hu, K. Dai, and P. Pan, “Investigation of engineering properties and filtration characteristics of porous asphalt concrete containing activated carbon,” Journal of Cleaner Production, vol. 209, pp. 1484–1493, 2019.
  • [9] Y. Rew, A. Baranikumar, A. V. Tamashausky, S. El-Tawil, and P. Park, “Electrical and mechanical properties of asphaltic composites containing carbon based fillers,” Construction and Building Materials, vol. 135, pp. 394–404, 2017.
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  • [15] Ö. Gerçel ve G. Seydioğlu, “Kiraz çekirdeğinden granül aktif karbon üretimi,” Anadolu Üniversitesi Bilim ve Teknoloji Dergisi A- Uygulamalı Bilimler ve Mühendislik, c. 16, s. 2, ss. 189, 2015.
  • [16] İ. Demiral, C. Şamdan, ve H. Demiral, “Şeftali çekirdeğinden çinko klorür aktivasyonu ile aktif karbon üretimi ve karakterizasyonu,” Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 28, s. 1, ss. 73–82, 2020.
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  • [18] Ö. Açışlı, “Doum palm meyve kabuklarından aktif karbon üretimi ve karakterizasyonu,” Avrupa Bilim ve Teknoloji Dergisi, s. 16, ss. 544–551, 2019.
  • [19] Z. E. Sayın, C. Kumaş, ve B. Ergül, “Fındık kabuğundan aktif karbon üretimi,” Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, c. 16, s. 2, ss. 409–419, 2016.
  • [20] E. Seyma Seyrek, E. Yalcin, M. Yilmaz, B. Vural Kök, and H. Arslanoglu, “Effect of activated carbon obtained from vinasse and marc on the rheological and mechanical characteristics of the bitumen binders and hot mix asphalts,” Construction and Building Materials, vol. 240, pp. 117921, 2020.
  • [21] R. Zhang, Q. Dai, Z. You, H. Wang, and C. Peng, “Rheological performance of bio-char modified asphalt with different particle sizes,” Applied Sciences, vol. 8, no. 9, pp. 1665, 2018.
  • [22] A. Kumar, R. Choudhary, R. Narzari, R. Kataki, and S. K. Shukla, “Evaluation of bio-asphalt binders modified with biochar: a pyrolysis by-product of Mesua ferrea seed cover waste,” Cogent Engineering, vol. 5, no. 1, pp. 1548534, 2018.
  • [23] A. Behnood and E. Mohammadi Golafshani, “Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming,” Construction and Building Materials, vol. 266, pp. 120983, 2021.
  • [24] F. Hussain, Y. Ali, M. Irfan, M. Ashraf, and S. Ahmed, “A data-driven model for phase angle behaviour of asphalt concrete mixtures based on convolutional neural network,” Construction and Building Materials, vol. 269, p. 121235, 2021.
  • [25] M. Yilmaz, B. V. Kok, B. Sengoz, A. Sengur, and E. Avci, “Investigation of complex modulus of base and EVA modified bitumen with adaptive-network-based fuzzy ınference system,” Expert Systems with Applications, vol. 38, no. 1, pp. 969–974, 2011.
  • [26] B. V. Kok, M. Yilmaz, B. Sengoz, A. Sengur, and E. Avci, “Investigation of complex modulus of base and SBS modified bitumen with artificial neural networks,” Expert Systems with Applications, vol. 37, no. 12, pp. 7775–7780, 2010.
  • [27] H. Sebaaly, S. Varma, and J. W. Maina, “Optimizing asphalt mix design process using artificial neural network and genetic algorithm,” Construction and Building Materials, vol. 168, pp. 660–670, 2018.
  • [28] S. Lv et al., “Performance and optimization of bio-oil/Buton rock asphalt composite modified asphalt,” Construction and Building Materials, vol. 264, pp. 120235, 2020.
  • [29] C. Xing, H. Xu, Y. Tan, D. Wang, and C. Zhai, “Strain field distribution of asphalt mortar using digital image processing,” Construction and Building Materials, vol. 238, pp. 117624, 2020.
  • [30] F. Tang, C. Han, T. Ma, T. Chen, and Y. Jia, “Quantitative analysis and visual presentation of segregation in asphalt mixture based on image processing and BIM,” Automation in Construction, vol. 121, pp. 103461, 2021.
  • [31] N.-D. Hoang, “Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression,” Automation in Construction, vol. 105, pp. 102843, 2019.
  • [32] B. V. Kök, M. Yilmaz, M. Çakiroğlu, N. Kuloğlu, and A. Şengür, “Neural network modeling of SBS modified bitumen produced with different methods,” Fuel, vol. 106, pp. 265–270, 2013.
  • [33] M. Saltan and S. Terzi˙, “Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli,” Advances in Engineering Software, vol. 39, no. 7, pp. 588–592, 2008.
  • [34] D. Singh, M. Zaman, and S. Commuri, “Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape properties,” Journal of Materials in Civil Engineering, vol. 25, no. 1, pp. 54–62, 2013.
  • [35] L. P. Specht, O. Khatchatourian, L. A. T. Brito, and J. A. P. Ceratti, “Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks,” Materials Research, vol. 10, no. 1, pp. 69–74, 2007.
  • [36] N. Kamboozia, H. Ziari, and H. Behbahani, “Artificial neural networks approach to predicting rut depth of asphalt concrete by using of visco-elast“ic parameters,” Construction and Building Materials, vol. 158, pp. 873–882, 2018.
  • [37] M. A. Abed, Z. N. M. Taki, and A. H. Abed, “Artificial neural network modeling of the modified hot mix asphalt stiffness using bending beam rheometer,” Materials Today: Proceedings, 2021.
  • [38] E. Ozgan, “Artificial neural network based modelling of the Marshall Stability of asphalt concrete,” Expert Systems with Applications, vol. 38, no. 5, pp. 6025–6030, 2011.
  • [39] J. Zaniewski and M. Pumphrey, “Evaluation of performance graded asphalt binder equipment and testing protocol,” 2004.
  • [40] E. Yalçın, “Saf ve modifiye bitümlerin farklı frekans ve sıcaklıklardaki reolojik özelliklerinin incelenmesi,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 9, s. 2, ss. 901–909, 2020.
  • [41] W. Huang et al., “Rheological characteristics evaluation of bitumen composites containing rock asphalt and diatomite,” Applied Sciences, vol. 9, no. 5, pp. 1023, 2019.
  • [42] E. Öztemel, Yapay si̇ni̇r ağları, İstanbul, Türkiye: Papatya Yayıncılık Eğitim, 2008.
  • [43] D. Graupe, Principles of artificial neural networks, 3rd edition.,World Scıentıfıc Publishing Company, 2013.
  • [44] F. Sönmez Çakir, Yapay sinir ağları matlab kodları ve matlab toolbox çözümleri, Ankara, Türkiye: Nobel Akademik Yayıncılık, 2019.
  • [45] D. Aşkin, İ. İskender, ve A. Mamizadeh, “Farklı yapay sinir ağları yöntemlerini kullanarak kuru tip transformatör sargısının termal analizi,” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 26, s.4, 2011.
  • [46] H. Yu and B. M. Wilamowski, “Levenberg–marquardt training,” in Intelligent Systems, CRC Press, 2018, pp. 12-1-12–16.
  • [47] B. Eren, M. Yaqub, and V. Eyüpoğlu, “Assessment of neural network training algorithms for the prediction of polymeric ınclusion membranes efficiency,” SAÜ Fen Bilimleri Enstitüsü Dergisi, vol. 20, no. 3, 2016.
  • [48] M. F. Møller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, vol. 6, no. 4, pp. 525–533, 1993.

Predicting The Complex Modulus of Magnetic Activated Carbon Modified Bitumen Using Artificial Neural Networks

Year 2021, Volume: 9 Issue: 5, 1995 - 2011, 31.10.2021
https://doi.org/10.29130/dubited.919452

Abstract

In this study, the rheological properties of the bitumen modified with Magnetic Activated Carbon (MAC) were investigated and the results were predicted with artificial neural networks. Within the scope of the study, modified bitumens were obtained by adding 5%, 10% and 15% MAC to the B160/220 penetration grade bituminous binder. Then, a frequency sweep test at ten different frequencies (0.01-10Hz) and four different temperatures (40 °, 50 °, 60 °, 70 ° C) was carried out on original and modified bitumens with the Dynamic Shear Rheometer (DSR) device. The results showed that the addition of magnetic activated carbon improves the elastic properties of the binder by increasing the complex modulus and decreasing the phase angle values. Then, complex modulus and phase angle values, which vary depending on the frequency, addition content and temperature value, were predicted by artificial neural networks. The results showed that complex modulus and phase angle values of the bitumen can be obtained with very high accuracy and low error with artificial neural networks.



References

  • [1] R. Hunter, A. Self, and J. Read, The shell bitumen handbook, 6th edition, London, UK: ICE Publishing, 2015.
  • [2] D. N. Little, D. H. Allen, and A. Bhasin, Modeling and design of flexible pavements and materials, Springer International Publishing AG, 2017.
  • [3] Y. Erkuş, B. V. Kök, and M. Yilmaz, “Evaluation of performance and productivity of bitumen modified by three different additives,” Construction and Building Materials, vol. 261, pp. 120553, 2020.
  • [4] B. V. Kök, M. Yılmaz, and M. Akpolat, “Evaluation of the conventional and rheological properties of SBS+Sasobit modified binder,” Construction and Building Materials, vol. 63, pp. 174–179, 2014.
  • [5] P. Cong, P. Xu, and S. Chen, “Effects of carbon black on the anti aging, rheological and conductive properties of SBS/asphalt/carbon black composites,” Construction and Building Materials, vol. 52, pp. 306–313, 2014.
  • [6] S. Zhao, B. Huang, X. P. Ye, X. Shu, and X. Jia, “Utilizing bio-char as a bio-modifier for asphalt cement: A sustainable application of bio-fuel by-product,” Fuel, vol. 133, pp. 52–62, 2014.
  • [7] Ç. Muhammed Ertugrul, Y. Mehmet, B. V. Kök, and E. Yalçin, “Effects of various biochars on the high temperature performance of bituminous binder,” Jun. 2016.
  • [8] X. Hu, K. Dai, and P. Pan, “Investigation of engineering properties and filtration characteristics of porous asphalt concrete containing activated carbon,” Journal of Cleaner Production, vol. 209, pp. 1484–1493, 2019.
  • [9] Y. Rew, A. Baranikumar, A. V. Tamashausky, S. El-Tawil, and P. Park, “Electrical and mechanical properties of asphaltic composites containing carbon based fillers,” Construction and Building Materials, vol. 135, pp. 394–404, 2017.
  • [10] B. Huang, X. Chen, and X. Shu, “Effects of electrically conductive additives on laboratory-measured properties of asphalt mixtures,”Journal of Materials in Civil Engineering, vol. 21, no. 10, pp. 612–617, 2009.
  • [11] M. Bostancioğlu and Ş. Oruç, “Effect of activated carbon and furan resin on asphalt mixture performance,” Road Materials and Pavement Design, vol. 17, no. 2, pp. 512–525, 2016.
  • [12] C. Li, F. Ning, and Y. Li, “Effect of carbon black on the dynamic moduli of asphalt mixtures and its master curves,” Frontiers of Structural and Civil Engineering, vol. 13, no. 4, pp. 918–925, 2019. [13] R. Casado-Barrasa, P. Lastra-González, I. Indacoechea-Vega, and D. Castro-Fresno, “Assessment of carbon black modified binder in a sustainable asphalt concrete mixture,” Construction and Building Materials, vol. 211, pp. 363–370, 2019.
  • [14] M. A. Notani et al., “Investigating the high-temperature performance and activation energy of carbon black-modified asphalt binder, ” SN Applied Sciences, vol. 2, no. 2, pp. 303, 2020.
  • [15] Ö. Gerçel ve G. Seydioğlu, “Kiraz çekirdeğinden granül aktif karbon üretimi,” Anadolu Üniversitesi Bilim ve Teknoloji Dergisi A- Uygulamalı Bilimler ve Mühendislik, c. 16, s. 2, ss. 189, 2015.
  • [16] İ. Demiral, C. Şamdan, ve H. Demiral, “Şeftali çekirdeğinden çinko klorür aktivasyonu ile aktif karbon üretimi ve karakterizasyonu,” Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 28, s. 1, ss. 73–82, 2020.
  • [17] E. Ülkeryıldız Balçık, M. Torun, ve H. Şahin Nadeem, “Gıda atıklarından aktif karbon üretimi ve aktif karbonun gıda endüstrisinde uygulamaları,” Gıda, c. 45, s.2, ss. 217–229, 2020.
  • [18] Ö. Açışlı, “Doum palm meyve kabuklarından aktif karbon üretimi ve karakterizasyonu,” Avrupa Bilim ve Teknoloji Dergisi, s. 16, ss. 544–551, 2019.
  • [19] Z. E. Sayın, C. Kumaş, ve B. Ergül, “Fındık kabuğundan aktif karbon üretimi,” Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, c. 16, s. 2, ss. 409–419, 2016.
  • [20] E. Seyma Seyrek, E. Yalcin, M. Yilmaz, B. Vural Kök, and H. Arslanoglu, “Effect of activated carbon obtained from vinasse and marc on the rheological and mechanical characteristics of the bitumen binders and hot mix asphalts,” Construction and Building Materials, vol. 240, pp. 117921, 2020.
  • [21] R. Zhang, Q. Dai, Z. You, H. Wang, and C. Peng, “Rheological performance of bio-char modified asphalt with different particle sizes,” Applied Sciences, vol. 8, no. 9, pp. 1665, 2018.
  • [22] A. Kumar, R. Choudhary, R. Narzari, R. Kataki, and S. K. Shukla, “Evaluation of bio-asphalt binders modified with biochar: a pyrolysis by-product of Mesua ferrea seed cover waste,” Cogent Engineering, vol. 5, no. 1, pp. 1548534, 2018.
  • [23] A. Behnood and E. Mohammadi Golafshani, “Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming,” Construction and Building Materials, vol. 266, pp. 120983, 2021.
  • [24] F. Hussain, Y. Ali, M. Irfan, M. Ashraf, and S. Ahmed, “A data-driven model for phase angle behaviour of asphalt concrete mixtures based on convolutional neural network,” Construction and Building Materials, vol. 269, p. 121235, 2021.
  • [25] M. Yilmaz, B. V. Kok, B. Sengoz, A. Sengur, and E. Avci, “Investigation of complex modulus of base and EVA modified bitumen with adaptive-network-based fuzzy ınference system,” Expert Systems with Applications, vol. 38, no. 1, pp. 969–974, 2011.
  • [26] B. V. Kok, M. Yilmaz, B. Sengoz, A. Sengur, and E. Avci, “Investigation of complex modulus of base and SBS modified bitumen with artificial neural networks,” Expert Systems with Applications, vol. 37, no. 12, pp. 7775–7780, 2010.
  • [27] H. Sebaaly, S. Varma, and J. W. Maina, “Optimizing asphalt mix design process using artificial neural network and genetic algorithm,” Construction and Building Materials, vol. 168, pp. 660–670, 2018.
  • [28] S. Lv et al., “Performance and optimization of bio-oil/Buton rock asphalt composite modified asphalt,” Construction and Building Materials, vol. 264, pp. 120235, 2020.
  • [29] C. Xing, H. Xu, Y. Tan, D. Wang, and C. Zhai, “Strain field distribution of asphalt mortar using digital image processing,” Construction and Building Materials, vol. 238, pp. 117624, 2020.
  • [30] F. Tang, C. Han, T. Ma, T. Chen, and Y. Jia, “Quantitative analysis and visual presentation of segregation in asphalt mixture based on image processing and BIM,” Automation in Construction, vol. 121, pp. 103461, 2021.
  • [31] N.-D. Hoang, “Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression,” Automation in Construction, vol. 105, pp. 102843, 2019.
  • [32] B. V. Kök, M. Yilmaz, M. Çakiroğlu, N. Kuloğlu, and A. Şengür, “Neural network modeling of SBS modified bitumen produced with different methods,” Fuel, vol. 106, pp. 265–270, 2013.
  • [33] M. Saltan and S. Terzi˙, “Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli,” Advances in Engineering Software, vol. 39, no. 7, pp. 588–592, 2008.
  • [34] D. Singh, M. Zaman, and S. Commuri, “Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape properties,” Journal of Materials in Civil Engineering, vol. 25, no. 1, pp. 54–62, 2013.
  • [35] L. P. Specht, O. Khatchatourian, L. A. T. Brito, and J. A. P. Ceratti, “Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks,” Materials Research, vol. 10, no. 1, pp. 69–74, 2007.
  • [36] N. Kamboozia, H. Ziari, and H. Behbahani, “Artificial neural networks approach to predicting rut depth of asphalt concrete by using of visco-elast“ic parameters,” Construction and Building Materials, vol. 158, pp. 873–882, 2018.
  • [37] M. A. Abed, Z. N. M. Taki, and A. H. Abed, “Artificial neural network modeling of the modified hot mix asphalt stiffness using bending beam rheometer,” Materials Today: Proceedings, 2021.
  • [38] E. Ozgan, “Artificial neural network based modelling of the Marshall Stability of asphalt concrete,” Expert Systems with Applications, vol. 38, no. 5, pp. 6025–6030, 2011.
  • [39] J. Zaniewski and M. Pumphrey, “Evaluation of performance graded asphalt binder equipment and testing protocol,” 2004.
  • [40] E. Yalçın, “Saf ve modifiye bitümlerin farklı frekans ve sıcaklıklardaki reolojik özelliklerinin incelenmesi,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 9, s. 2, ss. 901–909, 2020.
  • [41] W. Huang et al., “Rheological characteristics evaluation of bitumen composites containing rock asphalt and diatomite,” Applied Sciences, vol. 9, no. 5, pp. 1023, 2019.
  • [42] E. Öztemel, Yapay si̇ni̇r ağları, İstanbul, Türkiye: Papatya Yayıncılık Eğitim, 2008.
  • [43] D. Graupe, Principles of artificial neural networks, 3rd edition.,World Scıentıfıc Publishing Company, 2013.
  • [44] F. Sönmez Çakir, Yapay sinir ağları matlab kodları ve matlab toolbox çözümleri, Ankara, Türkiye: Nobel Akademik Yayıncılık, 2019.
  • [45] D. Aşkin, İ. İskender, ve A. Mamizadeh, “Farklı yapay sinir ağları yöntemlerini kullanarak kuru tip transformatör sargısının termal analizi,” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 26, s.4, 2011.
  • [46] H. Yu and B. M. Wilamowski, “Levenberg–marquardt training,” in Intelligent Systems, CRC Press, 2018, pp. 12-1-12–16.
  • [47] B. Eren, M. Yaqub, and V. Eyüpoğlu, “Assessment of neural network training algorithms for the prediction of polymeric ınclusion membranes efficiency,” SAÜ Fen Bilimleri Enstitüsü Dergisi, vol. 20, no. 3, 2016.
  • [48] M. F. Møller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, vol. 6, no. 4, pp. 525–533, 1993.
There are 47 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Ahmet Münir Özdemir 0000-0002-4872-154X

Bahadır Yılmaz 0000-0001-8328-5328

Nurten Akgün Tanbay 0000-0003-3888-3913

Publication Date October 31, 2021
Published in Issue Year 2021 Volume: 9 Issue: 5

Cite

APA Özdemir, A. M., Yılmaz, B., & Akgün Tanbay, N. (2021). Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 9(5), 1995-2011. https://doi.org/10.29130/dubited.919452
AMA Özdemir AM, Yılmaz B, Akgün Tanbay N. Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini. DUBİTED. October 2021;9(5):1995-2011. doi:10.29130/dubited.919452
Chicago Özdemir, Ahmet Münir, Bahadır Yılmaz, and Nurten Akgün Tanbay. “Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 9, no. 5 (October 2021): 1995-2011. https://doi.org/10.29130/dubited.919452.
EndNote Özdemir AM, Yılmaz B, Akgün Tanbay N (October 1, 2021) Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 5 1995–2011.
IEEE A. M. Özdemir, B. Yılmaz, and N. Akgün Tanbay, “Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini”, DUBİTED, vol. 9, no. 5, pp. 1995–2011, 2021, doi: 10.29130/dubited.919452.
ISNAD Özdemir, Ahmet Münir et al. “Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9/5 (October 2021), 1995-2011. https://doi.org/10.29130/dubited.919452.
JAMA Özdemir AM, Yılmaz B, Akgün Tanbay N. Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini. DUBİTED. 2021;9:1995–2011.
MLA Özdemir, Ahmet Münir et al. “Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, vol. 9, no. 5, 2021, pp. 1995-11, doi:10.29130/dubited.919452.
Vancouver Özdemir AM, Yılmaz B, Akgün Tanbay N. Manyetik Aktif Karbon Modifiyeli Bitümün Kompleks Modül Değerlerinin Yapay Sinir Ağlarıyla Tahmini. DUBİTED. 2021;9(5):1995-2011.