Advertisement

Classification of Concrete Strength Grade Using Nearest Neighbor Partitioning

Conference paper
  • 3.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10878)

Abstract

Concrete is an important building material in the field of civil engineering. As an important factor, the strength of concrete affects its quality directly. Although conventional methods are made to forecast concrete strength, the classification of its grade is still an important issue in terms of non-uniformity of mortar and the complexity of curing condition. In this study, the classification of strength grade is implemented by employing the nearest neighbor partitioning method-based neural network classifier, which not only produces flexible decision boundaries but also eliminates centroid-based constraints and further enlarges the opportunity for finding optimal solutions. Experimental results manifest that the adopted method improves the performance of concrete grade classification.

Keywords

Neural network Nearest neighbor partitioning Concrete strength 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 81671785, No. 61472164, No. 61472163, No. 61672262. Science and technology project of Shandong Province under Grant No. 2015GGX101025. Project of Shandong Province Higher Educational Science and Technology Program under Grant no. J16LN07. Shandong Provincial Key R&D Program under Grant No. 2016ZDJS01A12, No. 2016GGX101001.

References

  1. 1.
    Shi, X.C., Dong, Y.F.: Support vector machine applied to prediction strength of cement. In: 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp. 1585–1588 (2011)Google Scholar
  2. 2.
    Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28, 1797–1808 (1998)CrossRefGoogle Scholar
  3. 3.
    Wang, L., Yang, B., Wang, S., Liang, Z.: Building image feature kinetics for cement hydration using gene expression programming with similarity weight tournament selection. IEEE Trans. Evol. Comput. 19, 679–693 (2015)CrossRefGoogle Scholar
  4. 4.
    Wang, L., Yang, B., Abraham, A.: Prediction of concrete strength using floating centroids method. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics. Manchester, pp. 988–992 (2013)Google Scholar
  5. 5.
    Wang, L., Yang, B., Chen, Y., Abraham, A., Sun, H., Chen, Z., Wang, H.: Improvement of neural network classifier using floating centroids. Knowl. Inf. Syst. 31, 433–454 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhou, J., Chen, L., Chen, C.L.P., Zhang, Y., Li, H.X.: Fuzzy clustering with the entropy of attribute weights. Neurocomputing 198, 125–134 (2016)CrossRefGoogle Scholar
  7. 7.
    Wang, L., Yang, B., Chen, Y., Zhang, X., Orchard, J.: Improving neural-network classifiers using nearest neighbor partitioning. IEEE Trans. Neural Netw. Learn. Syst. 28, 2255–2267 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Trtnik, G., Kavcic, F., Turk, G.: Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 49, 53–60 (2009)CrossRefGoogle Scholar
  9. 9.
    Lee, S.C.: Prediction of concrete strength using artificial neural networks. Eng. Struct. 25, 849–857 (2003)CrossRefGoogle Scholar
  10. 10.
    Kim, D.K., Lee, J.J., Lee, J.H., Chang, S.K.: Application of probabilistic neural networks for prediction of concrete strength. J. Mater. Civil Eng. 17, 353–362 (2005)CrossRefGoogle Scholar
  11. 11.
    Gupta, R., Kewalramani, M.A., Goel, A.: Prediction of concrete strength using neural-expert system. J. Mater. Civil Eng. 18, 462–466 (2006)CrossRefGoogle Scholar
  12. 12.
    Rajasekaran, S., Lee, S.C.: Prediction of concrete strength using serial functional network model. Struct. Eng. Mech. 16, 83–99 (2003)CrossRefGoogle Scholar
  13. 13.
    Jongjae, L., Dookie, K., Seongkyu, C., Jangho, L.: Application of support vector regression for the prediction of concrete strength. Comput. Concr. 4, 299–316 (2007)CrossRefGoogle Scholar
  14. 14.
    Lai, S., Serra, M.: Concrete strength prediction by means of neural network. Constr. Build. Mater. 11, 93–98 (1997)CrossRefGoogle Scholar
  15. 15.
    Severcan, M.H.: Prediction of splitting tensile strength from the compressive strength of concrete using gep. Neural Comput. Appl. 21, 1937–1945 (2012)CrossRefGoogle Scholar
  16. 16.
    Yu, Z., Liu, Y., Yu, X., Pu, K.Q.: Scalable distributed processing of k nearest neighbor queries over moving objects. IEEE Trans. Knowl. Data Eng. 27, 1383–1396 (2015)CrossRefGoogle Scholar
  17. 17.
    Wang, L., Yang, B., Orchard, J.: Particle swarm optimization using dynamic tournament topology. Appl. Soft Comput. 48, 584–596 (2016)CrossRefGoogle Scholar
  18. 18.
    Booth, H.S., Maindonald, J.H., Wilson, S.R., Gready, J.E.: An efficient z-score algorithm for assessing sequence alignments. J. Comput. Biol. J. Comput. Mol. Cell Biol. 11, 616–625 (2004)CrossRefGoogle Scholar
  19. 19.
    Bridle, J.S.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Soulié, F.F., Hérault, J. (eds.) Neurocomputing. NATO ASI Series (Series F: Computer and Systems Sciences), vol. 68, pp. 227–236. Springer, Berlin, Heidelberg (1990)CrossRefGoogle Scholar
  20. 20.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (2012)zbMATHGoogle Scholar
  21. 21.
    Li, T., Rogovchenko, Y.V.: Oscillation of second-order neutral differential equations. Mathematische Nachrichten 288, 1150–1162 (2015)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Li, T., Rogovchenko, Y.V.: Oscillation criteria for even-order neutral differential equations. Appl. Math. Lett. 61, 35–41 (2016)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Han, S.Y., Chen, Y.H., Tang, G.Y.: Fault diagnosis and fault-tolerant tracking control for discrete-time systems with faults and delays in actuator and measurement. J. Franklin Inst. 354, 4719–4738 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.School of InformaticsLinyi UniversityLinyiChina
  3. 3.Shenzhen Gangchuang Building Material Co., Ltd.ShenzhenChina

Personalised recommendations