Cover
Vol. 11 No. 2 (2020)

Published: November 15, 2020

Pages: 192-201

Research Paper

A Neural Model to Estimate Carrying Capacity of Rectangular Steel Tubular Columns Filled with Concrete

Kadhim Zuboon Nasser 1 Aqeel H. Chkheiwer Mohammed F. Ojaimi
1Department of Civil Engineering, College of Engineering, University of Basrah, Al-Basrah, 61004, Iraq
History
  • Received: August 25, 2020
  • Revised: September 23, 2020
  • Accepted: October 8, 2020
  • Available Online: November 15, 2020
DOI

https://doi.org/10.37649/aengs.2020.171222

Abstract

The goal of the current investigation is to construct an artificial neural network (ANN) to estimate the ultimate capacity of the composite columns consisting of a rectangular steel tube filled with concrete (RSTFC) under concentric loads. The experimental results of (222) samples collected from previous researches were used in constructing the proposed network. Totally (45) specimens were randomly chosen for network testing while the remaining (177) speci-mens were used to train the network. The information used to create the ANN model is ar-ranged into (6) variables represents the different dimensions and properties of the RSTFC col-umns. Based on the input information, a formulated network was used to estimate the columns' ultimate capacity. Results obtained from the formulated network, available laboratory tests, and Eurocode 4 and AISC equations were compared. The network values were closer to the laboratory values than the calculated values according to the specifications of the mentioned codes. It has been shown that the formulated ANN model has a high ability to estimate the RCFST ultimate capacity under concentric loads

Keywords

References

  1. Georgios Giakoumelis, and Dennis Lam, “Axial capacity of circular concrete- filled tube columns” Journal of Constructional Steel Research, 60,2004, pp 1049–1068.
  2. K. Z. Nasser, "Experimental and Computational Study of Concrete Fill Aluminum Tubular Column under Axial Loads", Kufa Journal of Engineering (K.J.E), Vol. 5, June, 2014.
  3. Hong, W.K., and Kim, H.C., "Behavior of concrete columns confined by carbon composite tubes", Canadian Journal of Civil Eng., 31, 2, 2004, pp. 178–188.
  4. Knowles, R. B., and Park, R., “Strength of Concrete Filled Steel Tubular Columns” Journal of the Structural Division, ASCE, Vol. 95, No. ST12, 1969, pp 2565-2587.
  5. K. Z. Nasser “A Fuzzy Interface System to Predict Ultimate Strength of Circular Concrete Filled Steel Tubular Columns” Eng. & Tech. Journal, Vol. 30, No.3, 2012.
  6. Ge, H. B., and Usami, T., “Strength of Concrete-Filled Thin-Walled Steel Box Columns: Experiment,” Journal of Structural Engineering, ASCE, Vol. 118, No 1, 1992, pp 3036- 054.
  7. Schneider, S.P., "Axially loaded concrete-filled steel tubes", Journal of Structural Engineering, Vol. 124, No. 10, October 1998.
  8. K. Z. Nasser “Structural Behavior of Concrete Filled Aluminum Tubular Columns” Basrah Journal for Engineering Science, 2012.
  9. Bradford, M. A., “Design Strength of Slender Concrete-Filled Rectangular Steel Tubes,” ACI Structural Journal, Vol. 93, No. 2, 1996, pp 229-235.
  10. Garder J, Jacobson R. "Structural behavior of concrete filled steel tubes." ACI J Struct Div 1967;64(7):404–13,
  11. Lam, D., and Wong, K.K.Y., "Axial capacity of concrete filled stainless steel columns", ASCE Journal of Structures 2005, pp. 1107-1120.
  12. P.K. Gupta, S.M. Sarda, and M.S. Kumar “Experimental and computational study of concrete filled steel tubular columns under axial loads”, Journal of Constructional Steel Research 63, 2007, pp 182–193.
  13. Y.C. Yeh, Y.H. Kuo, D.S. Hsu, Building an expert system for debugging FEM input data with artificial neural networks, J. Expert Syst. Appl. 5 (1992) 59–70.
  14. I. Yeh, Design of high-performance concrete mixture using neural networks and nonlinear programming, J. Comp. Civ. Eng. 13 (1) (1999) 36–42.
  15. U. K. Nath, M. K. Goyal, and T. P. Nath,. “Prediction of Compressive Strength of Concrete using Neural Network”, International Journal of emerging trends in engineering and development Issue 1, Vol. 1, August-2011, pp 32-43.
  16. P. Ch. Deka,. And S. N. Diwate “Modeling Compressive Strength of Ready Mix Concrete Using Soft Computing Techniques”., International Journal of Earth Sciences and Engineering 793 ISSN 0974-5904, Volume 04, No 06 SPL, October 2011, pp. 793-796.
  17. A. Abdollah zadeha, R.Masoudniaa, And S. Aghababaeib “Predict Strength Of Rubberized Concrete Using Atrificial Neural Network”, Wseas Transactions On COMPUTERS , Issue 2, Volume 10, February 2011. , pp. 31-40.
  18. M. H. Ayazi1, V. R. Tosee 2, And M. Z. Jumaat 3 “Application of Artificial Neural Networks In Compressive Strength Prediction of Lightweight Concrete With Various Percentage Of Scoria Instead of Sand”., Engineering E-Transaction (Issn 1823-6379) Vol. 4, No. 2, December 2009, pp 64-68.
  19. M. M. Alshihri a, Ahmed M. Azmy b, and Mousa S. El-Bisy “Neural networks for predicting compressive strength of structural light weight concrete” Construction and Building Materials, 23(2009):2214–2219.
  20. Mostafa Erfani, Ehsan Noroozinejad Farsangi “Fuzzy Neural Network Utilization in Prediction of Compressive Strength of Slag-Cement Based Mortars”, Australian Journal of Basic and Applied Sciences, 4(10): 4962-4970, 2010.
  21. C. Bas¸yigit Æ Iskender Akkurt Æ S. Kilincarslan Æ A. Beycioglu “Prediction of compressive strength of heavyweight concrete by ANN and FL models” Neural Comput & Applic (2010) 19:507–513.
  22. M. Sarıdemir “Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic”, Advances in Engineering Software, 40(2009): 920–927.
  23. A. A. Khalaf, K. Z. Naser and F. Kamil “Prediction the Ultimate Strength of Circular Concrete Filled Steel Tubular Columns by Using Artificial Neural Networks”. International Journal of Civil Engineering and Technology (IJCIET), Volume 9, Issue 7, July 2018, pp. 1724–1736.
  24. Razavi S. V.1, Jumaat M. Z.1 and Ahmed H. EI-Shafie “Using feed-forward back propagation (FFBP) neural networks for compressive strength prediction of lightweight concrete made with different percentage of scoria instead of sand” International Journal of the Physical Sciences, Vol. 6(6), pp. 1325-1331, 18 March, 2011.
  25. Kim, D.K., “A Database for Composite Columns”, M.Sc thesis, School of Civil and Environmental Engineering, Georgia Institute of Technology, 2005.
  26. AISC, 2005, "Specification for Structural Steel Buildings", American Institute of Steel Construction, Chicago, IL.
  27. Eurocode 4, 1994, DD ENV 1994-1-1, "Design of Composite Steel and Concrete Structures, Part 1.1: General Rules and Rules for Buildings (with U.K National Application Document)", British Standards Institution, London.