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Yapay sinir ağları ile Marshall stabilite değerinin tahmini

Year 2021, Volume: 10 Issue: 2, 627 - 633, 27.07.2021
https://doi.org/10.28948/ngumuh.866566

Abstract

Bu makalede, Niğde-Adana otoyolu inşaatında yapılan Marshall stabilite deneylerinden faydalanılmıştır. Karot numune üzerinde yapılan deneylerin sonuçları Yapay Sinir Ağları (YSA) ile modellenmiştir. Asfalt betonu karışımı numunesi üzerinde yapılan Marshall stabilite deneyinde; agrega ağırlığına göre bitüm yüzdesi, karışım içindeki bitümün ağırlıkça yüzdesi, hacim özgül ağırlığı ve boşluk değerlerine bağlı olarak bulunan Marshall stabilite değeri YSA yöntemine göre tahmin edilmiştir. Yapılan eğitim sonucu elde edilen tahmin modeli, önceden ayrılan deney sonuçları ile denetlendiğinde, YSA modelinin tahmin ettiği Marshall stabilite değerleri ile deneysel olarak elde edilen değerlerin arasında iyi bir ilişkinin olduğu belirlenmiştir.

References

  • P. S. Kandhall and R. B. Mallick, Aggregate Tests for Hot Mix Asphalt: State of Practice. NCAT Report No.97-6, 10-15, 1997.
  • F.L. Roberts, L.N. Mohammad, L.B. Wang, History of hot mix asphalt mixture design in the United States. Journal of Materials in Civil Engineering. 14(4), 279-293, 2002. https://doi.org/10.1061/(ASCE)0899-1561(2002)14:4(279).
  • P. G. Lavin, Asphalt Pavements. Spon Pres. London and New York, 2003.
  • The Asphalt Institute. Superpave Mix Design. Superpave Series No.2 (SP-2). U.S.A., 1996.
  • ASTM D 242, Standard Specification for Mineral Filler for Bituminous Paving Mixtures. Annual Book of ASTM Standards. USA, 1992.
  • AASHTO T166, Bulk Specific Gravity of Compacted Bituminous Mixtures Using Saturated Surface-Dry Specimens. FHWA Multi-Regional Asphalt Training and Certification Group. 9p., 1999.
  • AASHTO T209, Bulk Specific Gravity of Bituminous Paving Mixtures. FHWA Multi-Regional Asphalt Training and Certification Group. 5p., 1999.
  • F. Umar ve E. Ağar, Yol Üstyapısı. İstanbul Teknik Üniversitesi. İnşaat Fakültesi Matbası, İstanbul, 1991.
  • M. Karasahin, Resilient behaviour of granular materials for analysis of highway pavements. PhD thesis, University of Nottingham, 1993.
  • ASTM D1559, Test Method for Resistance of Plastic Flow of Bituminous Mixtures Using Marshall Apparatus, 1998.
  • A. Önal ve S. Karaca, Asfalt Betonu Karışım Dizayn Metotları. KGM Yayınları, Ankara, 1990.
  • M. Önal ve M. Kahramangil, Bitümlü karışımlar laboratuvar el kitabı. KGM Teknik Araştırma Dairesi Başkanlığı, Ankara, 1993.
  • TS 3720, Bitümlü Kaplama Karışımlarının Hesap Esasları Marshall ve Hubbart Field Metotları. Türk Standartları Enstitüsü, Ankara,1983.
  • E. Ağar, İ. Sütaş ve G. Öztaş, Beton Yollar (Rijit Yol Üstyapıları). İTÜ Basımevi, İstanbul, 1998.
  • A. Tunç, Yol Malzemeleri ve Uygulamaları. Atlas Yayınevi, İstanbul, 2001.
  • İstanbul Büyükşehir Belediyesi, Asfalt ve Uygulamaları. İSFALT Bilimsel Yayın No:1, İstanbul, 2001.
  • Yollar Fenni Şartnamesi, Bayındırlık ve İskan Bakanlığı, KGM, 2000.
  • R. Baş, Superpave: Sıcak Karışım Asfalt Üstyapının Geleceği, Teknik Çeviri, Karayolları 2. Bölge. Müdürlüğü, Asfalt Başmühendisliği, KGM, Ankara, 1999.
  • H. Varol, Bitümlü sıcak kaplamalı üst yapıların yapım kriterlerinin araştırılması. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2000.
  • M. Ilıcalı, S. Tayfur ve H. Özen, Soğuk karışımlarda agrega gradasyonunun optimum bitüm muhtevasına etkisi. II. Ulusal Asfalt Sempozyumu, 1999.
  • H. Polat, Ankara Gerede- Ankara Çevre Otoyolu bitümlü karışım üstyapı tabakalarının fiziksel özellikleri ve sıkışabilirliğinin analizi. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 1994.
  • M. Uluçay, Bitümlü Karışımların Tasarımında Yeni Gelişmeler Yoğurmalı Pres. Yollar. Türk Milli Komitesi, Ankara, 1997.
  • Z. Şen, Yapay Sinir Ağları. Su Vakfı Yayınları, İstanbul, 2004.
  • J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proceedings. National Academy of Sciences. 79. National Academy of Sciences. Washington. D. C.. . 2554–2558, 1992 https://doi.org/10.1073/pnas. 79.8.2554
  • D. E. Rumelhart, G. E. Hinton and J. L. McClelland, A general framework for parallel distributed Processing. in Parallel Distributed Processing: Explorations in the icrostructure of Cognition. I: Foundations. MIT Press. Cambridge. MA. . 45–76. 1986.
  • P. Mehra and B. W. Wah. Artificial neural networks: concepts and theory. IEEE Computer Society Press. Los Alamitos. CA. 1–8. 1992. https://doi.org/ 10.1109/TMTT.2003.809179
  • J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings. National Academy of Sciences. 81. National Academy of Sciences. Washington. D. C.. . 3088–3092. May 1984; https://doi.org/10.1073/pnas.81.10.3088
  • J. J. Hopfield and D. W. Tank, Computing with neural circuits: A model. Science. 233, 625–633, August 1986. http://doi.org/10.1126/science.3755256
  • G. A. Carpenter and S. A. Grossberg, A Massively parallel architecture for a self-organizing neural pattern recognition machine. Computer vision graphics and image processing. 37, 54—115, 1987. https://doi.org/10.1016/S0734-189X(87)80014-2.
  • G. A. Carpenter and S. Grossberg, ART2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics. 26, 4919–4930.1987. https://doi.org/10.1364/AO.26.004919
  • G. A. Carpenter and S. Grossberg, The ART of adaptive pattern recognition by a selforganizing neural network. Computer, March 1988. https://doi.org/10.1109/2.33
  • R. Hecht-Nielsen. Neurocomputing. Addison-Wesley Publishing Company, New York, 1990.
  • P. Werbos. Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph. D. Dissertation, Harvard University, 1974.
  • D. B. Parker. Learning Logic. Technical Report TR-47. Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA,1985.
  • D. E. Rumelhart, G. E. Hinton and R. J. Williams. Learning representations by backpropagating errors. Nature. 323, 1986, 533–536. https://doi.org/10.1038/ 323533a0.
  • J. R. Boyce. A non-linear model for the elastic behaviour of granular materials under repeated loading. Proc. Int. Symp. Soils under Cyclic & Transient Loading. Swansea. 285-294, 1980.
  • S. F. Brown and J. W. Pappin, Analysis of pavements with granular bases. Transportation Research Record 810. 17-22, 1981.
  • S. Ishak, H. Al-Deek. Performance of automatic ANN-based incident detection on freeways. Journal of Transportation Engineering., 125(4). 281-290. 1999. https://doi.org/10.1061/(ASCE)0733947X(1999)125:4(281)
  • J. Uzan. Resilient characterization of pavement materials. Int. Journal of Numerical and Analytical Methods in Geomechanics. 16. 453-459. 1. 334-350, 1992. https://doi.org/10.1002/nag.1610160605
  • J. Uzan, M. W. Witczak. Scullion T & Lytton RL. Development and validation of realistic pavement response models. Proc. of 7th. Int. Conf. On Asphalt Pavements, 1992.
  • E. Özgan, T. Kap, A. Beycioğlu ve M. Miroğlu. Asfalt betonunda Marshall stabilitesinin uyarmalı bulanık mantık yaklaşımı ile tahmini. 5. Uluslararası ileri teknolojiler sempozyumu (İATS’09), 2009.
  • N. Morova, S. Serin, S. Terzi. Bitüm miktarının asfalt betonu dayanımına etkisinin bulanık mantık yaklaşımı ile değerlendirilmesi. 6th International Advanced Technologies Symposium (IATS’11), 2011.
  • S. Serin, N. Morova, Ş. Sargın, S. Terzi, M. Saltan. The fuzzy logic model for prediction of marshall stability of lightweight asphalt concretes fabricated using expanded clay aggregate. SDÜ Fen Bilimleri Enstitüsü Dergisi. 2013. https://doi.org/10.19113/sdufbed.79420
  • P. Lingras. Classifying highways: hierarchical grouping versus kohonen neural networks. Journal of Transportation Engineering. 121(4), 364-368, 1995.
  • H. C. Mayhew. Resilient properties of unbound roadbase under repeated triaxial loading. TRRL Laboratory Report 1088, 1983.
  • J. W. Pappin. Characteristics of a granular material for pavement analysis. PhD thesis, University of Nottingham, 1979.
  • J. W. Pappin, SF. Brown. Resilient stress-strain behaviour of a crushed rock. Int. Symp. On Soils and Transient Loading. Swansea. 169-177, 1980.
  • B. Stackel. The derivation of complex stress-strain relations. Int. Conf. On Soil Mech. and Foun. Eng.. Volume 1, Moscow, 353-359, 1973.
  • J. H. Tsoukalas, ER. Uhrig. Fuzzy and neural approaches in engineering. John Wiley & Sons. Inc, 1997.
  • The European Economic Community. A European approach to road pavement design. Progress report 2. 1991.
  • J. Xu, S. C. Wong., H. Yang and C. O. Tong. Modeling level of urban taxi services using neural network. Journal of Transportation Engineering. 125(3), 216-223. 1999.
  • J. Uzan. Characterization of granular material. Transportation Research Record. 1022. 52-59, 1985.
  • M. Karasahin, A. R. Dawson and J. T. Holden. The applicability of resilient constitutive models of granular material for unbound base layers. Transportation Research Record. No 1406, 98-107, 1993.
  • M. Karasahin, A. R. Dawson. Resilient behaviour of cohesionless soil. XII Int. Conf. On Soil Mech. And Foun. Eng.. Delhi. India. 1827-1830, 1994.
  • M. S. Kaseko, Z. P. Lo, S. G. Ritchie. Comparison of traditional ans neural classifiers for pavement-crack detection. Journal of Transportation Engineering. 120(4), 552-569, 1994. https://doi.org/10.1061/ (ASCE)0733947X(1994)120:4(552)
  • PV. Lade, R. D. Nelson. Modelling the elastic behavior of granular materials. Int. Journal for Numerical and Analytical Methods in Geomechanics. 2. 521-542.1987.

Estimation of Marshall stability value with artificial neural networks

Year 2021, Volume: 10 Issue: 2, 627 - 633, 27.07.2021
https://doi.org/10.28948/ngumuh.866566

Abstract

In this article, Marshall stability tests carried out in Niğde-Adana highway construction site were used. The results of the experiments performed on the core sample were modelled by Artificial Neural Networks (ANN). In Marshall stability test performed on the specimen of asphalt concrete mixture; the Marshall stability values, which are determined by the percentage of bitumen according to the weight of aggregate, the percentage by weight of the bitumen in the mixture, the volume specific gravity and the void values, were estimated by using the ANN method. When the prediction model obtained as a result of the training was inspected with the previously separated experimental results, it was determined that there was a good relationship between the Marshall stability values estimated by the ANN model and the experimentally obtained values.

References

  • P. S. Kandhall and R. B. Mallick, Aggregate Tests for Hot Mix Asphalt: State of Practice. NCAT Report No.97-6, 10-15, 1997.
  • F.L. Roberts, L.N. Mohammad, L.B. Wang, History of hot mix asphalt mixture design in the United States. Journal of Materials in Civil Engineering. 14(4), 279-293, 2002. https://doi.org/10.1061/(ASCE)0899-1561(2002)14:4(279).
  • P. G. Lavin, Asphalt Pavements. Spon Pres. London and New York, 2003.
  • The Asphalt Institute. Superpave Mix Design. Superpave Series No.2 (SP-2). U.S.A., 1996.
  • ASTM D 242, Standard Specification for Mineral Filler for Bituminous Paving Mixtures. Annual Book of ASTM Standards. USA, 1992.
  • AASHTO T166, Bulk Specific Gravity of Compacted Bituminous Mixtures Using Saturated Surface-Dry Specimens. FHWA Multi-Regional Asphalt Training and Certification Group. 9p., 1999.
  • AASHTO T209, Bulk Specific Gravity of Bituminous Paving Mixtures. FHWA Multi-Regional Asphalt Training and Certification Group. 5p., 1999.
  • F. Umar ve E. Ağar, Yol Üstyapısı. İstanbul Teknik Üniversitesi. İnşaat Fakültesi Matbası, İstanbul, 1991.
  • M. Karasahin, Resilient behaviour of granular materials for analysis of highway pavements. PhD thesis, University of Nottingham, 1993.
  • ASTM D1559, Test Method for Resistance of Plastic Flow of Bituminous Mixtures Using Marshall Apparatus, 1998.
  • A. Önal ve S. Karaca, Asfalt Betonu Karışım Dizayn Metotları. KGM Yayınları, Ankara, 1990.
  • M. Önal ve M. Kahramangil, Bitümlü karışımlar laboratuvar el kitabı. KGM Teknik Araştırma Dairesi Başkanlığı, Ankara, 1993.
  • TS 3720, Bitümlü Kaplama Karışımlarının Hesap Esasları Marshall ve Hubbart Field Metotları. Türk Standartları Enstitüsü, Ankara,1983.
  • E. Ağar, İ. Sütaş ve G. Öztaş, Beton Yollar (Rijit Yol Üstyapıları). İTÜ Basımevi, İstanbul, 1998.
  • A. Tunç, Yol Malzemeleri ve Uygulamaları. Atlas Yayınevi, İstanbul, 2001.
  • İstanbul Büyükşehir Belediyesi, Asfalt ve Uygulamaları. İSFALT Bilimsel Yayın No:1, İstanbul, 2001.
  • Yollar Fenni Şartnamesi, Bayındırlık ve İskan Bakanlığı, KGM, 2000.
  • R. Baş, Superpave: Sıcak Karışım Asfalt Üstyapının Geleceği, Teknik Çeviri, Karayolları 2. Bölge. Müdürlüğü, Asfalt Başmühendisliği, KGM, Ankara, 1999.
  • H. Varol, Bitümlü sıcak kaplamalı üst yapıların yapım kriterlerinin araştırılması. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2000.
  • M. Ilıcalı, S. Tayfur ve H. Özen, Soğuk karışımlarda agrega gradasyonunun optimum bitüm muhtevasına etkisi. II. Ulusal Asfalt Sempozyumu, 1999.
  • H. Polat, Ankara Gerede- Ankara Çevre Otoyolu bitümlü karışım üstyapı tabakalarının fiziksel özellikleri ve sıkışabilirliğinin analizi. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 1994.
  • M. Uluçay, Bitümlü Karışımların Tasarımında Yeni Gelişmeler Yoğurmalı Pres. Yollar. Türk Milli Komitesi, Ankara, 1997.
  • Z. Şen, Yapay Sinir Ağları. Su Vakfı Yayınları, İstanbul, 2004.
  • J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proceedings. National Academy of Sciences. 79. National Academy of Sciences. Washington. D. C.. . 2554–2558, 1992 https://doi.org/10.1073/pnas. 79.8.2554
  • D. E. Rumelhart, G. E. Hinton and J. L. McClelland, A general framework for parallel distributed Processing. in Parallel Distributed Processing: Explorations in the icrostructure of Cognition. I: Foundations. MIT Press. Cambridge. MA. . 45–76. 1986.
  • P. Mehra and B. W. Wah. Artificial neural networks: concepts and theory. IEEE Computer Society Press. Los Alamitos. CA. 1–8. 1992. https://doi.org/ 10.1109/TMTT.2003.809179
  • J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings. National Academy of Sciences. 81. National Academy of Sciences. Washington. D. C.. . 3088–3092. May 1984; https://doi.org/10.1073/pnas.81.10.3088
  • J. J. Hopfield and D. W. Tank, Computing with neural circuits: A model. Science. 233, 625–633, August 1986. http://doi.org/10.1126/science.3755256
  • G. A. Carpenter and S. A. Grossberg, A Massively parallel architecture for a self-organizing neural pattern recognition machine. Computer vision graphics and image processing. 37, 54—115, 1987. https://doi.org/10.1016/S0734-189X(87)80014-2.
  • G. A. Carpenter and S. Grossberg, ART2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics. 26, 4919–4930.1987. https://doi.org/10.1364/AO.26.004919
  • G. A. Carpenter and S. Grossberg, The ART of adaptive pattern recognition by a selforganizing neural network. Computer, March 1988. https://doi.org/10.1109/2.33
  • R. Hecht-Nielsen. Neurocomputing. Addison-Wesley Publishing Company, New York, 1990.
  • P. Werbos. Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph. D. Dissertation, Harvard University, 1974.
  • D. B. Parker. Learning Logic. Technical Report TR-47. Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA,1985.
  • D. E. Rumelhart, G. E. Hinton and R. J. Williams. Learning representations by backpropagating errors. Nature. 323, 1986, 533–536. https://doi.org/10.1038/ 323533a0.
  • J. R. Boyce. A non-linear model for the elastic behaviour of granular materials under repeated loading. Proc. Int. Symp. Soils under Cyclic & Transient Loading. Swansea. 285-294, 1980.
  • S. F. Brown and J. W. Pappin, Analysis of pavements with granular bases. Transportation Research Record 810. 17-22, 1981.
  • S. Ishak, H. Al-Deek. Performance of automatic ANN-based incident detection on freeways. Journal of Transportation Engineering., 125(4). 281-290. 1999. https://doi.org/10.1061/(ASCE)0733947X(1999)125:4(281)
  • J. Uzan. Resilient characterization of pavement materials. Int. Journal of Numerical and Analytical Methods in Geomechanics. 16. 453-459. 1. 334-350, 1992. https://doi.org/10.1002/nag.1610160605
  • J. Uzan, M. W. Witczak. Scullion T & Lytton RL. Development and validation of realistic pavement response models. Proc. of 7th. Int. Conf. On Asphalt Pavements, 1992.
  • E. Özgan, T. Kap, A. Beycioğlu ve M. Miroğlu. Asfalt betonunda Marshall stabilitesinin uyarmalı bulanık mantık yaklaşımı ile tahmini. 5. Uluslararası ileri teknolojiler sempozyumu (İATS’09), 2009.
  • N. Morova, S. Serin, S. Terzi. Bitüm miktarının asfalt betonu dayanımına etkisinin bulanık mantık yaklaşımı ile değerlendirilmesi. 6th International Advanced Technologies Symposium (IATS’11), 2011.
  • S. Serin, N. Morova, Ş. Sargın, S. Terzi, M. Saltan. The fuzzy logic model for prediction of marshall stability of lightweight asphalt concretes fabricated using expanded clay aggregate. SDÜ Fen Bilimleri Enstitüsü Dergisi. 2013. https://doi.org/10.19113/sdufbed.79420
  • P. Lingras. Classifying highways: hierarchical grouping versus kohonen neural networks. Journal of Transportation Engineering. 121(4), 364-368, 1995.
  • H. C. Mayhew. Resilient properties of unbound roadbase under repeated triaxial loading. TRRL Laboratory Report 1088, 1983.
  • J. W. Pappin. Characteristics of a granular material for pavement analysis. PhD thesis, University of Nottingham, 1979.
  • J. W. Pappin, SF. Brown. Resilient stress-strain behaviour of a crushed rock. Int. Symp. On Soils and Transient Loading. Swansea. 169-177, 1980.
  • B. Stackel. The derivation of complex stress-strain relations. Int. Conf. On Soil Mech. and Foun. Eng.. Volume 1, Moscow, 353-359, 1973.
  • J. H. Tsoukalas, ER. Uhrig. Fuzzy and neural approaches in engineering. John Wiley & Sons. Inc, 1997.
  • The European Economic Community. A European approach to road pavement design. Progress report 2. 1991.
  • J. Xu, S. C. Wong., H. Yang and C. O. Tong. Modeling level of urban taxi services using neural network. Journal of Transportation Engineering. 125(3), 216-223. 1999.
  • J. Uzan. Characterization of granular material. Transportation Research Record. 1022. 52-59, 1985.
  • M. Karasahin, A. R. Dawson and J. T. Holden. The applicability of resilient constitutive models of granular material for unbound base layers. Transportation Research Record. No 1406, 98-107, 1993.
  • M. Karasahin, A. R. Dawson. Resilient behaviour of cohesionless soil. XII Int. Conf. On Soil Mech. And Foun. Eng.. Delhi. India. 1827-1830, 1994.
  • M. S. Kaseko, Z. P. Lo, S. G. Ritchie. Comparison of traditional ans neural classifiers for pavement-crack detection. Journal of Transportation Engineering. 120(4), 552-569, 1994. https://doi.org/10.1061/ (ASCE)0733947X(1994)120:4(552)
  • PV. Lade, R. D. Nelson. Modelling the elastic behavior of granular materials. Int. Journal for Numerical and Analytical Methods in Geomechanics. 2. 521-542.1987.
There are 56 citations in total.

Details

Primary Language Turkish
Subjects Civil Engineering
Journal Section Civil Engineering
Authors

Recep Koray Kıyıldı 0000-0003-4628-0186

Publication Date July 27, 2021
Submission Date January 22, 2021
Acceptance Date February 22, 2021
Published in Issue Year 2021 Volume: 10 Issue: 2

Cite

APA Kıyıldı, R. K. (2021). Yapay sinir ağları ile Marshall stabilite değerinin tahmini. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(2), 627-633. https://doi.org/10.28948/ngumuh.866566
AMA Kıyıldı RK. Yapay sinir ağları ile Marshall stabilite değerinin tahmini. NOHU J. Eng. Sci. July 2021;10(2):627-633. doi:10.28948/ngumuh.866566
Chicago Kıyıldı, Recep Koray. “Yapay Sinir ağları Ile Marshall Stabilite değerinin Tahmini”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10, no. 2 (July 2021): 627-33. https://doi.org/10.28948/ngumuh.866566.
EndNote Kıyıldı RK (July 1, 2021) Yapay sinir ağları ile Marshall stabilite değerinin tahmini. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10 2 627–633.
IEEE R. K. Kıyıldı, “Yapay sinir ağları ile Marshall stabilite değerinin tahmini”, NOHU J. Eng. Sci., vol. 10, no. 2, pp. 627–633, 2021, doi: 10.28948/ngumuh.866566.
ISNAD Kıyıldı, Recep Koray. “Yapay Sinir ağları Ile Marshall Stabilite değerinin Tahmini”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10/2 (July 2021), 627-633. https://doi.org/10.28948/ngumuh.866566.
JAMA Kıyıldı RK. Yapay sinir ağları ile Marshall stabilite değerinin tahmini. NOHU J. Eng. Sci. 2021;10:627–633.
MLA Kıyıldı, Recep Koray. “Yapay Sinir ağları Ile Marshall Stabilite değerinin Tahmini”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 10, no. 2, 2021, pp. 627-33, doi:10.28948/ngumuh.866566.
Vancouver Kıyıldı RK. Yapay sinir ağları ile Marshall stabilite değerinin tahmini. NOHU J. Eng. Sci. 2021;10(2):627-33.

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