Advertisement

Prediction of Blasting Vibration Intensity by Improved PSO-SVR on Apache Spark Cluster

  • Yunlan Wang
  • Jing Wang
  • Xingshe Zhou
  • Tianhai Zhao
  • Jianhua Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

In order to predict blasting vibration intensity accurately, support vector machine regression (SVR) was adopted to predict blasting vibration velocity, vibration frequency and vibration duration. The mutation operation of genetic algorithm (GA) is used to avoid the local optimal solution of particle swarm optimization (PSO). The improved PSO algorithm is used to search for the best parameters of SVR model. In the experiments, the improved PSO-SVR algorithm was realized on the Apache Spark platform. The execution time and prediction accuracy of the sadovski method, the traditional SVR algorithm, the neural network (NN) algorithm and the improved PSO-SVR algorithm were compared. The results show that the improved PSO-SVR algorithm on Spark is feasible and efficient, and the SVR model can predict the blasting vibration intensity more accurately than other methods.

Keywords

Blasting vibration intensity Prediction algorithm PSO-SVR Spark Big data 

References

  1. 1.
    Jinxi, Z.: Applicability research of Sadov’s vibration formula in analyzing of tunnel blasting vibration velocity. Fujian Constr. Sci. Technol. 5, 68–70 (2011)Google Scholar
  2. 2.
    Lv, T., Shi, Y.-Q., Huang, C., Li, H., Xia, X., Zhou, Q.-C., Li, J.: Study on attenuation parameters of blasting vibration by nonlinear regression analysis. Geomechanics 28(9), 1871–1878 (2007)Google Scholar
  3. 3.
    Shi, X., Dong, K., Qiu, X., Chen, X.: Analysis of the PPV prediction of blasting vibration based on support vector machine regression. Blasting 15(3), 28–30 (2009)Google Scholar
  4. 4.
    Chen, S., Wei, H., Qian, Q.: The study on effect of structure vibration response by blast vibration duration. In: National Coal Blasting Symposium (2008)Google Scholar
  5. 5.
    Badrakh-Yeruul, T., Xia, A., Zhang, J., Wang, T.: Application of neural network based on genetic algorithm in prediction of blasting vibration. Blasting 3, 140–144 (2014)Google Scholar
  6. 6.
    Xiuzhi, Z., Jianguang, X., Shouru, C.: Study of time and frequency analysis of blasting vibration signal and the prediction of blasting vibration characteristic parameters and damage. Vibr. Shock 28(7), 73–76 (2009)Google Scholar
  7. 7.
    Wang, J., Huang, Y., Zhou, J.: BP neural network prediction for blasting vibration in open-pit coal mine (3), 322–328 (2016)Google Scholar
  8. 8.
    Mohamadnejad, M., Gholami, R., Ataei, M.: Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunn. Undergr. Space Technol. 28, 238–244 (2012)CrossRefGoogle Scholar
  9. 9.
    Qingjie, L., Guiming, C., Xiaofang, L., Qing, Y.: Genetic algorithm based SVM parameter composition optimization. Comput. Appl. Softw. 29(4), 94–96 (2012)Google Scholar
  10. 10.
    Vol. N.: Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond/Learning Kernel Classifiers (2003). (J. Am. Stat. Assoc. 98, 489–490)Google Scholar
  11. 11.
    Xiao, J., Yu, L., Bai, Y.: Survey of the selection of kernels and hyper-parameters in support vector regression. J. Southwest Jiaotong Univ. 43(3), 297–303 (2008)MATHGoogle Scholar
  12. 12.
    Üstün, B., Melssen, W.J., Oudenhuijzen, M., et al.: Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Anal. Chim. Acta 544(1), 292–305 (2005)CrossRefGoogle Scholar
  13. 13.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory (1995)Google Scholar
  14. 14.
    Karau, H.: Learning Spark - Lightning-Fast Big Data Analysis. Oreilly & Associates Inc., Newton (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yunlan Wang
    • 1
  • Jing Wang
    • 1
  • Xingshe Zhou
    • 1
  • Tianhai Zhao
    • 1
  • Jianhua Gu
    • 1
  1. 1.School of Computer Science, Center for High Performance ComputingNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations