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Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model

Year 2018, Volume: 33 Issue: 3, 187 - 196, 30.09.2018

Mehmet Uğraş Cuma , Hayrullah Özel Tahsin Köroğlu

https://doi.org/10.21605/cukurovaummfd.504649

Abstract

The iron and steel industry is one of the essential sector for the industrial and economic development of a country. The most common problem in iron and steel industry is to determine the ultimate tensile strength of the product. The raw materials that are used in the Prestressed Concrete (PC) strand product are deformed under force and their shape and size are changed since the characteristics of them are not constant. To understand the material properties of the product such as the yield and the ultimate tensile strength, some mechanical tests are carried out. The product, the time and the labor loss occured in these mechanical tests reveal the need to develop a prediction method based on non-destructive measurement. In this study, the mechanical properties of PC strand product is predicted by using artificial neural networks (ANN). 'Feed-Forward Backpropagation (FFBP)' has been preferred since it is the most accurate network type for the current process. To determine the ultimate tensile strength, the data such as the load applied to the material (loadcell output), the DC voltage and the DC current of the induction furnace, the speed of the PC strand line, the temperature of the induction furnace, the temperature of the quench tank and the diamater of the PC strand product are collected from a real production line and are utilized as the input parameters of the ANN in the simulation environment. The study illustrates that the ANN model give a very good prediction of the ultimate tensile strength of PC strand.

 

Keywords

Prestressed concrete strand Tensile test Artificial neural networks Feed-forward backpropagation

References

  • 1. Malinov, S., Sha, W., 2004. Application of Artificial Neural Networks for Modelling Correlations in Titanium Alloys. Materials Science and Engineering: A, 365(1-2), 202-211.
  • 2. Fujii, H., Mackay, D.J.C., Bhadeshia, H.K.D.H., 1996. Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys. ISIJ International, 36(11), 1373-1382.
  • 3. Cool, T., Bhadeshia, H.K.D.H., Mackay, D.J.C., 1997. The Yield and Ultimate Tensile Strength of Steel Welds. Materials Science and Engineering: A, 223(1-2), 186–200.
  • 4. Al-Assaf, Y., El-Kadi, H., 2001. Fatigue Life Prediction of Unidirectional Glass Fiber/epoxy Composite Laminae Using Neural Networks. Composite Structures, 53(1), 65-71.
  • 5. Akbari, M.K., Shirvanimoghaddam, K., Hai, Z., Zhuiykov, S., Khayyam, H., 2017. Nano TiB2 and TiO2 Reinforced Composites: A Comparative Investigation on Strengthening Mechanisms and Predicting Mechanical Properties Via Neural Network Modeling. Ceramics International, 43, 16799-16810.
  • 6. Malinov, S., Sha, W., Mckeown, J.J., 2001. Modelling the Correlation Between Processing Parameters and Properties in Titanium Alloys Using Artificial Neural Network. Computational Materials Science, 21(3), 375–394.
  • 7. Mcbride, J., Malinov, S., Sha, W., 2004. Modelling Tensile Properties of Gamma-Based Titanium Aluminides Using Artificial Neural Network. Materials Science and Engineering: A, 384(1-2), 129–137.
  • 8. Akbari, J., Rakhshan, N., Ahmadvand, M., 2013. Evaluation of Ultimate Torsional Strength of Reinforcement Concrete Beams Using Finite Element Analysis and Artificial Neural Network. IJE Transactions B: Applications, 26(5), 501-508.
  • 9. Jeyasehar, C.A., Sumangala, K., 2006. Nondestructive Evaluation of Prestressed Concrete Beams Using an Artificial Neural Network (ANN) Approach. Structural Health Monitoring, 5(4), 313-323.
  • 10. Esfahani, M.B., Toroghinejad, M.R, Abbasi, S., 2009. Artificial Neural Network Modeling the Tensile Strength of Hot Strip Mill Products. ISIJ International, 49(10), 1583-1587.
  • 11. Lalam, S., Tiwari, P.K., Sahoo, S., Dalal, A.K., 2017. Online Prediction and Monitoring of Mechanical Properties of Industrial Galvanised Steel Coils Using Neural Networks. Ironmaking & Steelmaking.
  • 12. Khayyam, H., Fakhrhoseini, S.M., Church, J.S., Milani, A.S., Bab-Hadiashar, A., Jazar, R.N., Naebe M., 2017. Predictive Modelling and Optimization of Carbon Fiber Mechanical Properties Through High Temperature Furnace, Applied Thermal Engineering, 125, 1539-1554.
  • 13. http://www.strongwillwire.com/
  • 14. ASTM International, 2017. ASTM A416 A416M-17 Standard Specification for Low Relaxation, Seven-wire Steel Strand for Prestressed Concrete. West Conshohocken, PA.
  • 15. Saravanakumar, P., Jothimani, V., Sureshbabu, L., Ayyappan, S., Noorullah, D., Venkatakrishnan, P.G., 2012. Prediction of Mechanical Properties of Low Carbon Steel in Hot Rolling Process Using Artificial Neural Network Model. Procedia Engineering, 38, 3418-3425.
  • 16. Denizer, B., 2008. Artificial Neural Network Analysis of the Mechanical Properties of Tungsten Fiber/Bulk Metallic Glass Matrix Composites via Neutron Diffraction and Finite Element Modeling., Iowa State University, Ames, Iowa, 33.
  • 17. Beale, M.H., Hagan, M.T., Demuth, H.B., 2017. Neural Network Toolbox User's Guide the MathWorks Inc., Natick, MA, 512.

Yapay Sinir Ağ Modeli Kullanılarak Ön Germeli Beton Demeti Maksimum Çekme Mukavemetinin Tahmini

Year 2018, Volume: 33 Issue: 3, 187 - 196, 30.09.2018

Mehmet Uğraş Cuma , Hayrullah Özel Tahsin Köroğlu

https://doi.org/10.21605/cukurovaummfd.504649

Abstract

Demir ve çelik endüstrisi, bir ülkenin endüstriyel ve ekonomik kalkınması için vazgeçilmez sektörlerden biridir. Demir ve çelik endüstrisindeki en yaygın sorun, ürünün maksimum çekme mukavemetini belirlemektir. Ön germeli beton demeti (ÖGBD) ürününde kullanılan hammaddeler kuvvet altında deforme olmakta ve karakteristikleri sabit olmadığından şekilleri ve boyutları değişmektedir. Ürünün, akma ve maksimum çekme mukavemeti gibi malzeme özelliklerini anlamak için bazı mekanik testler gerçekleştirilir. Bu mekanik testlerde ortaya çıkan ürün, zaman ve iş gücü kaybı, tahribatsız ölçümlere dayanan bir tahmin metodu geliştirme ihtiyacını ortaya koymaktadır. Bu çalışmada, ön germeli beton demeti ürününün mekanik özellikleri yapay sinir ağları (YSA) kullanılarak tahmin edilmiştir. Mevcut işlem için en doğru ağ tipi olduğundan 'İleri Beslemeli Geri Yayılım (İBGY)' tercih edilmiştir. Maksimum çekme mukavemetini belirlemek için, malzeme üzerine uygulanılan yük (yük hücresi çıkışı), indüksiyon fırınının DC gerilimi ve DC akımı, ÖGBD hattının hızı, indüksiyon fırınının sıcaklığı, soğutma tankının sıcaklığı ve ÖGBD ürününün çapı gibi veriler gerçek bir üretim hattından toplanmakta ve simülasyon ortamında YSA’nın girdi parametreleri olarak kullanılmaktadır. Çalışma, ANN modelinin, ön gerilmeli beton demetinin maksimum çekme mukavemetine dair çok iyi tahminde bulunulduğunu göstermektedir. 

Keywords

Öngermeli beton demeti Çekme testi Yapay sinir ağları İleri beslemeli geri yayılım

References

  • 1. Malinov, S., Sha, W., 2004. Application of Artificial Neural Networks for Modelling Correlations in Titanium Alloys. Materials Science and Engineering: A, 365(1-2), 202-211.
  • 2. Fujii, H., Mackay, D.J.C., Bhadeshia, H.K.D.H., 1996. Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys. ISIJ International, 36(11), 1373-1382.
  • 3. Cool, T., Bhadeshia, H.K.D.H., Mackay, D.J.C., 1997. The Yield and Ultimate Tensile Strength of Steel Welds. Materials Science and Engineering: A, 223(1-2), 186–200.
  • 4. Al-Assaf, Y., El-Kadi, H., 2001. Fatigue Life Prediction of Unidirectional Glass Fiber/epoxy Composite Laminae Using Neural Networks. Composite Structures, 53(1), 65-71.
  • 5. Akbari, M.K., Shirvanimoghaddam, K., Hai, Z., Zhuiykov, S., Khayyam, H., 2017. Nano TiB2 and TiO2 Reinforced Composites: A Comparative Investigation on Strengthening Mechanisms and Predicting Mechanical Properties Via Neural Network Modeling. Ceramics International, 43, 16799-16810.
  • 6. Malinov, S., Sha, W., Mckeown, J.J., 2001. Modelling the Correlation Between Processing Parameters and Properties in Titanium Alloys Using Artificial Neural Network. Computational Materials Science, 21(3), 375–394.
  • 7. Mcbride, J., Malinov, S., Sha, W., 2004. Modelling Tensile Properties of Gamma-Based Titanium Aluminides Using Artificial Neural Network. Materials Science and Engineering: A, 384(1-2), 129–137.
  • 8. Akbari, J., Rakhshan, N., Ahmadvand, M., 2013. Evaluation of Ultimate Torsional Strength of Reinforcement Concrete Beams Using Finite Element Analysis and Artificial Neural Network. IJE Transactions B: Applications, 26(5), 501-508.
  • 9. Jeyasehar, C.A., Sumangala, K., 2006. Nondestructive Evaluation of Prestressed Concrete Beams Using an Artificial Neural Network (ANN) Approach. Structural Health Monitoring, 5(4), 313-323.
  • 10. Esfahani, M.B., Toroghinejad, M.R, Abbasi, S., 2009. Artificial Neural Network Modeling the Tensile Strength of Hot Strip Mill Products. ISIJ International, 49(10), 1583-1587.
  • 11. Lalam, S., Tiwari, P.K., Sahoo, S., Dalal, A.K., 2017. Online Prediction and Monitoring of Mechanical Properties of Industrial Galvanised Steel Coils Using Neural Networks. Ironmaking & Steelmaking.
  • 12. Khayyam, H., Fakhrhoseini, S.M., Church, J.S., Milani, A.S., Bab-Hadiashar, A., Jazar, R.N., Naebe M., 2017. Predictive Modelling and Optimization of Carbon Fiber Mechanical Properties Through High Temperature Furnace, Applied Thermal Engineering, 125, 1539-1554.
  • 13. http://www.strongwillwire.com/
  • 14. ASTM International, 2017. ASTM A416 A416M-17 Standard Specification for Low Relaxation, Seven-wire Steel Strand for Prestressed Concrete. West Conshohocken, PA.
  • 15. Saravanakumar, P., Jothimani, V., Sureshbabu, L., Ayyappan, S., Noorullah, D., Venkatakrishnan, P.G., 2012. Prediction of Mechanical Properties of Low Carbon Steel in Hot Rolling Process Using Artificial Neural Network Model. Procedia Engineering, 38, 3418-3425.
  • 16. Denizer, B., 2008. Artificial Neural Network Analysis of the Mechanical Properties of Tungsten Fiber/Bulk Metallic Glass Matrix Composites via Neutron Diffraction and Finite Element Modeling., Iowa State University, Ames, Iowa, 33.
  • 17. Beale, M.H., Hagan, M.T., Demuth, H.B., 2017. Neural Network Toolbox User's Guide the MathWorks Inc., Natick, MA, 512.
There are 17 citations in total.

Details

Primary Language English
Subjects Architecture, Engineering
Journal Section Articles
Authors

Mehmet Uğraş Cuma

Hayrullah Özel This is me

Tahsin Köroğlu

Publication Date September 30, 2018
Published in Issue Year 2018 Volume: 33 Issue: 3

Cite

APA Cuma, M. U., Özel, H., & Köroğlu, T. (2018). Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 33(3), 187-196. https://doi.org/10.21605/cukurovaummfd.504649
AMA Cuma MU, Özel H, Köroğlu T. Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model. cukurovaummfd. September 2018;33(3):187-196. doi:10.21605/cukurovaummfd.504649
Chicago Cuma, Mehmet Uğraş, Hayrullah Özel, and Tahsin Köroğlu. “Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 33, no. 3 (September 2018): 187-96. https://doi.org/10.21605/cukurovaummfd.504649.
EndNote Cuma MU, Özel H, Köroğlu T (September 1, 2018) Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 33 3 187–196.
IEEE M. U. Cuma, H. Özel, and T. Köroğlu, “Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model”, cukurovaummfd, vol. 33, no. 3, pp. 187–196, 2018, doi: 10.21605/cukurovaummfd.504649.
ISNAD Cuma, Mehmet Uğraş et al. “Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 33/3 (September 2018), 187-196. https://doi.org/10.21605/cukurovaummfd.504649.
JAMA Cuma MU, Özel H, Köroğlu T. Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model. cukurovaummfd. 2018;33:187–196.
MLA Cuma, Mehmet Uğraş et al. “Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 33, no. 3, 2018, pp. 187-96, doi:10.21605/cukurovaummfd.504649.
Vancouver Cuma MU, Özel H, Köroğlu T. Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model. cukurovaummfd. 2018;33(3):187-96.