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Elephant Herding Optimization Based Neural Network to Predict Elastic Modulus of Concrete

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

Abstract

The concrete requirement is proliferating, especially in developing countries. The elastic modulus of concrete plays a vital role as a design parameter in construction applications. The experimental procedures for finding the elastic modulus of concrete are quite complicated and expensive. Thus, researchers have always been looking for better and efficient methods to replace the traditional methods. In the present study, the issue is addressed by hybridized soft computing technique called Elephant Herding Optimization Based Artificial Neural Network (EHO-ANN). The developed model is then validated with linear regression and standard empirical formulas used for estimation of elastic modulus of concrete. The performances are evaluated by statistic measures like Correlation Coefficient (CC), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). The developed EHO-ANN model (Train CC 0.9102 and Test CC 0.9095) has outperformed linear regression (Train CC 0.8287 and Test CC 0.7575) and empirical formula (Train CC 0.8170 and Test CC 0.7645) in predicting the elastic modulus of concrete. Hence, the EHO-ANN model can be used as an alternative method to predict the elastic modulus of concrete.

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References

  1. Sarıdemir, M.: Effect of silica fume and ground pumice on compressive strength and modulus of elasticity of high strength concrete. Constr. Build. Mater. 49, 484–489 (2013)

    Article  Google Scholar 

  2. Mesbah, H.A., Lachemi, M., Aitcin, P.C.: Determination of elastic properties of high-performance concrete at early ages. Mater. J. 99(1), 37–41 (2002)

    Google Scholar 

  3. Kocab, D., Kucharczykova, B., PetrMisak, P., Kralikova, M.: Development of the elastic modulus of concrete under different curing conditions. Proc. Eng. 195, 96–101 (2017)

    Article  Google Scholar 

  4. American Concrete Institute: Building code requirements for structural concrete (ACI 318M-95) and commentary (ACI318RM-95) (1995)

    Google Scholar 

  5. Standard, C.S.A.: A23. 3-04. Canadian Standard Association, 232 (2004)

    Google Scholar 

  6. Code, C.F.M.: Comite euro-international du beton. Bull. d’information 213, 214 (1993)

    Google Scholar 

  7. Indian Standard, I.S.: 456: 2000. Plain and reinforced concrete code of practice (2000)

    Google Scholar 

  8. Turkish Standards Institute: Requirements for design and construction of reinforced concrete structures. TS500-2000 (2000)

    Google Scholar 

  9. De Normalisation, C.E.: Eurocode 2: design of concrete structures—part 1-1: general rules and rules for buildings. Belgium, Brussels (2004)

    Google Scholar 

  10. Demir, F.: Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Constr. Build. Mater. 22(7), 1428–1435 (2008)

    Article  Google Scholar 

  11. Topçu, İ.B., Bilir, T., Boğa, A.R.: Estimation of the modulus of elasticity of slag concrete by using composite material models. Constr. Build. Mater. 24(5), 741–748 (2010)

    Article  Google Scholar 

  12. Ibrahim, D.: An overview of soft computing. Proc. Comput. Sci. 102, 34–38 (2016)

    Article  Google Scholar 

  13. Demir, F., Korkmaz, K.A.: Prediction of lower and upper bounds of elastic modulus of high strength concrete. Constr. Build. Mater. 22(7), 1385–1393 (2008)

    Article  Google Scholar 

  14. Golafshani, E.M., Behnood, A.: Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete. Appl. Soft Comput. 64, 377–400 (2018)

    Article  Google Scholar 

  15. Rinchon, J.P.M., Concha, N.C., Calilung, M.G.V.: Reinforced concrete ultimate bond strength model using hybrid neural network-genetic algorithm. In: IEEE 9th international conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM), pp. 1–6 (2017)

    Google Scholar 

  16. Ahmadi-Nedushan, B.: Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models. Constr. Build. Mater. 36, 665–673 (2012)

    Article  Google Scholar 

  17. Wang, G.G., Deb, S., Coelho, L.D.S.: Elephant herding optimization. In: 3rd international symposium on december computational and business intelligence (ISCBI), pp. 1–5. IEEE (2015)

    Google Scholar 

  18. Sahlol, A.T., Ismail, F.H., Abdeldaim, A., Hassanien, A.E.: Elephant herd optimization with neural networks: a case study on acute lymphoblastic leukemia diagnosis. In: IEEE 12th international conference on computer engineering and systems (ICCES), pp. 657–662 (2017)

    Google Scholar 

  19. Meena, N.K., Parashar, S., Swarnkar, A., Gupta, N., Niazi, K.R.: Improved elephant herding optimization for multiobjective DER accommodation in distribution systems. IEEE Trans. Industr. Inf. 14(3), 1029–1039 (2018)

    Article  Google Scholar 

  20. Moretti, J.F., Minussi, C.R., Akasaki, J.L., Fioriti, C.F., Pinheiro Melges, J.L., Mitsuuchi Tashima, M.: Prediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networks. Acta Sci. Technol. 38(1) (2016)

    Article  Google Scholar 

  21. Mandal, S., Rao, S., Harish, N.: Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models. Int. J. Naval Arch. Ocean Eng. 4(2), 112–122 (2012)

    Article  Google Scholar 

  22. Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2(2), 164–168 (1944)

    Article  MathSciNet  Google Scholar 

  23. Mavrovouniotis, M., Yang, S.: Training neural networks with ant colony optimization algorithms for pattern classification. Soft. Comput. 19(6), 1511–1522 (2015)

    Article  Google Scholar 

  24. Bal, L., Buyle-Bodin, F.: Artificial neural network for predicting drying shrinkage of concrete. Constr. Build. Mater. 38, 248–254 (2013)

    Article  Google Scholar 

  25. Dantas, A.T.A., Leite, M.B., de Jesus Nagahama, K.: Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Constr. Build. Mater. 38, 717–722 (2013)

    Article  Google Scholar 

  26. Zhang, J.R., Zhang, J., Lok, T.M., Lyu, M.R.: A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Appl. Math. Comput. 185(2), 1026–1037 (2007)

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, M S Ramaiah Institute of Technology, Bengaluru, India

    B. S. Adarsha & Narayana Harish

  2. Department of Civil Engineering, National Institute of Technology Silchar, Assam, India

    Prashanth Janardhan

  3. Department of Civil Engineering, Presidency University, Bengaluru, India

    Sukomal Mandal

Authors
  1. B. S. Adarsha
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  2. Narayana Harish
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  3. Prashanth Janardhan
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  4. Sukomal Mandal
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Corresponding author

Correspondence to Narayana Harish .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Adarsha, B.S., Harish, N., Janardhan, P., Mandal, S. (2020). Elephant Herding Optimization Based Neural Network to Predict Elastic Modulus of Concrete. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_28

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