Journal of Central South University

, Volume 26, Issue 10, pp 2906–2914 | Cite as

Neuro-fuzzy systems in determining light weight concrete strength

  • Seyed Vahid Razavi ToseeEmail author
  • Mehdi Nikoo


The adaptive neuro-fuzzy inference systems (ANFIS) are widely used in the concrete technology. In this research, the compressive strength of light weight concrete was determined. To this end, the scoria percentage and curing day variables were used as the input parameters, and compressive strength and tensile strength were used as the output parameters. In addition, 100 patterns were used, 70% of which were used for training and 30% were used for testing. To assess the precision of the neuro-fuzzy system, it was compared using two linear regression models. The comparisons were carried out in the training and testing phases. Research results revealed that the neuro-fuzzy systems model offers more potential, flexibility, and precision than the statistical models.

Key words

neuro-fuzzy systems compressive strength light weight concrete linear regression model 



目前,自适应神经模糊推理系统(ANFIS)在混凝土技术中得到了广泛的应用。本研究利用神经 模糊系统确定了轻量化混凝土的抗压强度。以废渣百分率和固化天数作为网络的输入参数,以抗压强 度和抗拉强度作为输出参数。实验选用了100 个模式,其中70%用于训练,30%用于测试。为了评估 神经模糊系统的精度,比较了神经模糊系统和统计模型(LR)两种线性回归模型的训练和测试阶段。结 果表明,神经模糊系统模型比统计模型具有更大的潜力、适应性性和精度。


神经模糊系统 抗压强度 轻量化混凝土 线性回归模型 


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  1. [1]
    RAZAVI S V, EI-SHAFIE A H, MOHAMMADI P. Artificial neural networks for mechanical strength prediction of lightweight mortar [J]. Sci Res Essays, 2011, 6(16): 3406–3417.CrossRefGoogle Scholar
  2. [2]
    TOPÇU I B, SARIDEMIR M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic [J]. Comput Mater Sci, 2008, 41(3): 305–311.CrossRefGoogle Scholar
  3. [3]
    ALSHIHRI M M, AZMY A M, EL-BISY M S. Neural networks for predicting compressive strength of structural light weight concrete [J]. Constr Build Mater, 2009, 23(6): 2214–2219.CrossRefGoogle Scholar
  4. [4]
    SARIDEMIR M, TOPÇU I B, ÖZCAN F, SEVERCAN M H. Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic [J]. Constr Build Mater, 2009, 23(3): 1279–1286.CrossRefGoogle Scholar
  5. [5]
    ÖZCAN F, ATIS C D, KARAHAN O, UNCUOGLU E, TANYILDIZI H. Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete [J]. Adv Eng Softw, 2009, 40(9): 856–863.zbMATHCrossRefGoogle Scholar
  6. [6]
    SLONSKI M. A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks [J]. Comput Struct, 2010, 88(21): 1248–1253.CrossRefGoogle Scholar
  7. [7]
    MADANDOUST R, GHAVIDEL R, NARIMAN-ZADEH N. Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV [J]. Comput Mater Sci, 2010, 49(3): 556–567.CrossRefGoogle Scholar
  8. [8]
    SIDDIQUE R, AGGARWAL P, AGGARWAL Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks [J]. Adv Eng Softw, 2011, 42(10): 780–786.CrossRefGoogle Scholar
  9. [9]
    CHENG M Y, CHOU J S, ROY A F V, WU Y W. High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model [J]. Autom Constr, 2012, 28: 106–115.CrossRefGoogle Scholar
  10. [10]
    ABOLPOUR B, ABOLPOUR B, ABOLPOUR R, BAKHSHI H. Estimation of concrete compressive strength by a fuzzy logic model [J]. Res Chem Intermed, 2013, 39(2): 707–719.CrossRefGoogle Scholar
  11. [11]
    DIAB A M, ELYAMANY H E, ABD ELMOATY A E M, SHALAN A H. Prediction of concrete compressive strength due to long term sulfate attack using neural network [J]. Alexandria Eng J, 2014, 53(3): 627–642.CrossRefGoogle Scholar
  12. [12]
    DESHPANDE N, LONDHE S, KULKARNI S. Modeling compressive strength of recycled aggregate concrete by artificial neural network, model tree and non-linear regression [J]. Int J Sustain Built Environ, 2014, 3(2): 187–198.CrossRefGoogle Scholar
  13. [13]
    SKRZYPCZAK I, BUDA-OZÓG L, PYTLOWANY T. Fuzzy method of conformity control for compressive strength of concrete on the basis of computational numerical analysis [J]. Meccanica, 2016, 51(2): 383–389.CrossRefGoogle Scholar
  14. [14]
    KHADEMI F, AKBARI M, JAMAL S M, NIKOO M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete [J]. Front Struct Civ Eng, 2017, 11(1): 90–99.CrossRefGoogle Scholar
  15. [15]
    NIKOO M, ZARFAM P, SAYAHPOUR H. Determination of compressive strength of concrete using self organization feature map (SOFM) [J]. Eng Comput, 2015, 31(1): 113–121.CrossRefGoogle Scholar
  16. [16]
    ASTERIS P G, ROUSSIS P C, DOUVIKA M G. Feed-forward neural network prediction of the mechanical properties of sandcrete materials [J]. Sensors, 2017, 17(6): 1–21.CrossRefGoogle Scholar
  17. [17]
    ASTERIS P G, CAVALERI L, TRAPANI F D, TSARIS A K. Numerical modelling of out-of-plane response of infilled frames: state of the art and future challenges for the equivalent strut macromodels [J]. Eng Struct, 2017, 132: 110–122.CrossRefGoogle Scholar
  18. [18]
    NIKOO M, SADOWSKI L, KHADEMI F, NIKOO M. Determination of damage in reinforced concrete frames with shear walls using self-organizing feature map [J]. Appl Comput Intell Soft Comput, 2017: 3508189.Google Scholar
  19. [19]
    JANG J S. ANFIS: Adaptive-network-based fuzzy inference system [J]. IEEE Trans Syst Man Cybern, 2002, 23(3): 665–685.CrossRefGoogle Scholar
  20. [20]
    KHADEMI F, JAMAL S M, DESHPANDE N, LONDHE S. Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression [J]. Int J Sustain Built Environ, 2016, 5(2): 355–369.CrossRefGoogle Scholar
  21. [21]
    ABDULSHAHED A M, LONGSTAFF A P, FLETCHER S. The application of ANFIS prediction models for thermal error compensation on CNC machine tools [J]. Appl Soft Comput, 2015, 27: 158–168.CrossRefGoogle Scholar
  22. [22]
    NIKOO M, ZARFAM P, NIKOO M. Determining displacement in concrete reinforcement building with using evolutionary artificial neural networks [J]. World Appl Sci J, 2012, 16(12): 1699–1708.Google Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringJundi-Shapur University of TechnologyDezfulIran
  2. 2.Young Researchers and Elite Club, Ahvaz BranchIslamic Azad UniversityAhvazIran

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