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Reseach Article

Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete

by Behnam Vakhshouri, Shami Nejadi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 5
Year of Publication: 2014
Authors: Behnam Vakhshouri, Shami Nejadi

Behnam Vakhshouri, Shami Nejadi . Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete. International Journal of Computer Applications. 101, 5 ( September 2014), 39-48. DOI=10.5120/17687-8548

@article{ 10.5120/17687-8548,
author = { Behnam Vakhshouri, Shami Nejadi },
title = { Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 5 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 39-48 },
numpages = {9},
url = { },
doi = { 10.5120/17687-8548 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:30:56.853231+05:30
%A Behnam Vakhshouri
%A Shami Nejadi
%T Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 5
%P 39-48
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Adaptive Neuro-Fuzzy Inference System is growing to predict nonlinear behaviour of construction materials. However due to wide variety of parameters in this type of artificial intelligent machine, selecting the proper optimization methods together with the best fitting membership functions strongly affect the accuracy of prediction. In this study the non-linear relation between splitting tensile strength and modulus of elasticity with compressive strength of high strength concrete is modelled and the effect of different effective parameters of Adaptive Neuro-Fuzzy Inference System is investigated on these models. To specify the best arrangements of parameters in the System to utilize in high strength concrete properties, different combinations of optimization methods and membership functions in the Sugeno system have been applied on more than 300 previously conducted experimental datasets. Both the grid partition and sub-clustering methods have been applied to models and compared to get the best combination of parameters.

  1. ACI-363-10, Report on High-Strength Concrete, Reported by ACI Committee 363, 1st printing, March 2010
  2. Ghosh S. K. (2004), High strength concrete in U. S codes and standards, XIV Congreso Nacional de Ingeniería Estructural, Acapulco, Gro
  3. ACI 211. 4R-08, (2008), Guide for Selecting Proportions for High-Strength Concrete Using Portland Cement and Other Cementitious Materials", Reported by ACI Committee 211
  4. S. Jassar, Z. Liao, Ashrae, L. Zhao (2009), Impact of Data Quality on Predictive Accuracy of ANFIS based Soft Sensor Models, Proceedings of the World Congress on Engineering and Computer Science 2009 Vol II WCECS 2009, October 20-22, San Francisco, USA
  5. Giaccio G. and Zerbino R. (1998), Failure Mechanism of Concrete, Advanced Cement Based Materials, Volume 7(2), March 1998, pp. 41–48
  6. Kim Jin-Kuen and Han Sang-Hun (1999), Mechanical Properties of Self-Flowing Concrete, ACI Special Publication, vol. 172 pp. 637-652
  7. Shannag M. J. (2000), High strength concrete containing natural pozzolan and silica fume, Cement & Concrete Composites, vol. 22, pp. 399-406
  8. Ajdukiewicz A. and Kliszczewicz A. (2002), Influence of recycled aggregates on mechanical properties of HS/HPC, Cement & Concrete Composites 24, pp. 269–279
  9. Kim J. Keun; Lee Y. , Yi, S. Tae (2004), Fracture characteristics of concrete at early ages, Cement and Concrete Research vol. 34 issue 3. pp. 507-519
  10. Bissonnette B. , Pigeon M. and Vaysburd Alexander M. (2007), Tensile Creep of Concrete: Study of Its Sensitivity to Basic Parameters, ACI Materials Journal, V. 104, No. 4, July-August
  11. Almeida Filho F. M. , Barragan B. E. , Casas J. R. , and Eldebs A. L. H. C. (2008), Variability of the bond and mechanical properties of self-compacting concrete, IBRACON structures and materials journal, Volume 1, Number 1,pp. 31–57
  12. Angel Perez J. Pablo (2008), Effect of slag cement on drying shrinkage of concrete, thesis submitted in conformity with the requirements for the degree of Master of Applied Science, September 2008, University of Toronto.
  13. Yin, J. , Chi, Y. , Gong, S. , and Zou, W. (2010), Research and Application of Recycled Aggregate Concrete, Paving Materials and Pavement Analysis, GeoShanghai 2010 International Conference, Shanghai, Chinapp. June 3-5, pp:162-168, 10. 1061/41104(377)19
  14. Ng K. M. , Tam C. M. and Tam V. W. Y. (2010), Studying the production process and mechanical properties of reactive powder concrete: a Hong Kong study, Magazine of Concrete Research, Volume 62, Issue 9, pp. 647 –654
  15. Ozbay E. , Gesoglu M. , Guneyisi E. (2011), Transport properties based multi-objective mix proportioning optimization of high performance concretes, Materials and Structures 44:139–154
  16. Parra C. , Valcuende M. , Gomez F. (2011), Splitting tensile strength and modulus of elasticity of self-compacting concrete, Construction and Building Materials 25, 201–207
  17. Das D. , Chatterjee A. (2012), A comparison of hardened properties of ?y-ash-based self-compacting concrete and normally compacted concrete under different curing conditions, Magazine of Concrete Research, Volume 64, Issue 2 Volume 64, Issue 2, , pages 129 – 141
  18. Ranaivomanana N. , Multon S. , Turatsinze A. (2013), Tensile, compressive and flexural basic creep of concrete at different stress levels, Cement and Concrete Research 52 1–10
  19. Joelianto E. , Rahmat B. (2008), Adaptive Neuro Fuzzy Inference System (ANFIS) with Error Backpropagation Algorithm using Mapping Function, International journal of artificial intelligence, Volume 1, Number A08
  20. Ozel C. , (2011), prediction of compressive strength of concrete from volume ratio and Bingham parameters using adaptive neuro-fuzzy inference system(Anfis) and data mining, Int. J. Physical sciences, Vol. 6(31), pp. 7078-7094
  21. Sadrmomtazi, J. Sobhani, M. A. Mirgozar (2013),Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS, Construction and Building Materials 42, pp. 205–216
  22. Chai Y. , Jia L. and Zhang Z. (2009), Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application, International Journal of Information and Mathematical Sciences, 5:1
  23. Kaur A. , Kaur Amrit (2012), Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System, International Journal of Soft Computing and Engineering (IJSCE), Volume-2, Issue-2
  24. Naderloo L. , Alimardani R. , Omid M. , Sarmadian F. , Javadikia P. , Torabi M. Y. , Alimardani F. (2012), Application of ANFIS to predict crop yield based on different energy inputs, Measurement 45, 1406–1413
  25. Takagi T. and Sugeno M. (1985), Fuzzy identi?cation of systems and its applications to modeling and control, IEEE Trans. Syst. , Man, Cybern, 15:116–132
  26. Duch W. (2004), Uncertainty of data, fuzzy membership functions, and multi-layer perceptrons, IEEE Transaction on neural networks, vol. XX, No. YY, 2004-1
  27. Neshat M. , Adeli A. , Masoumi A. , sargolzae M. , (2011), A Comparative Study on ANFIS and Fuzzy Expert System Models for Concrete Mix Design, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 2
  28. Rezazadeh shirdar M. , Nilashi M. , Bagherifard K. , Ibrahim O. , Izman S. , Moradifard H. , Jamshidi N. , Mehdi Barisamy (2011),Application of ANFIS system in prediction of machining parameter, Journal of Theoretical and Applied Information Technology, Vol. 33 No. 1
  29. Wei M. , Bai B. , Sung A. H. , Liu Q. , Wang J. , Cather M. E. (2007), Predicting injection pro?les using ANFIS, Information Sciences 177,pp. 4445–4461
Index Terms

Computer Science
Information Sciences


ANFIS High strength concrete Compressive strength Splitting tensile strength Modulus of Elasticity