Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 17 Issue 4
Dec 2006
Turn off MathJax
Article Contents
Shuhua ZHAI, Qian GAO, Jianguo SONG. Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining. Journal of Earth Science, 2006, 17(4): 361-366.
Citation: Shuhua ZHAI, Qian GAO, Jianguo SONG. Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining. Journal of Earth Science, 2006, 17(4): 361-366.

Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining

Funds:

Jinchuan Group Ltd. 2004-01D

More Information
  • Corresponding author: Zhai Shuhua: zhaishuhuahbu@163.com
  • Received Date: 28 Jun 2006
  • Accepted Date: 25 Sep 2006
  • The surface subsidence induced by mining is a complex problem, which is related with many complex and uncertain factors. Genetic programming (GP) has a good ability to deal with complex and nonlinear problems, therefore genetic programming approach is proposed to predict mining induced surface subsidence in this article. First genetic programming technique is introduced, second, surface subsidence genetic programming model is set up by selecting its main affective factors and training relating to practical engineering data, and finally, predictions are made by the testing of data, whose results show that the relative error is approximately less than 10%, which can meet the engineering needs, and therefore, this proposed approach is valid and applicable in predicting mining induced surface subsidence. The model offers a novel method to predict surface subsidence in mining.

     

  • loading
  • Bi, Z. W., Wang, C. L., 2002. The Application of the MATLAB ANN Toolbox on the Surface Subsidence. World Mining Express, (4): 51-54 (in Chinese).
    Cao, L. W., Jiang, Z. Q., 2002. Research on Application of Artificial Neural Network in Predicting Mining Subsidence. Journal of China University of Mining & Technology, (1): 23-26 (in Chinese with English Abstract).
    Li, M. Q., Kou, J. S., 2001. The Basic Theory and Its Application of Genetic Algorithm. Beijing Science and Technology Press, Beijing (in Chinese).
    Koza, J. R., 1992. Genetic Programming: On the Programming of Computer by Means of Nature Selection. The MIT Press, Cambridge.
    Koza, J. R., 1994. Genetic Programming. The MIT Press, Cambridge.
    Schwefel, H. P., 1995. Evolution and Optomum Seeking. John Wiley & Sons, Inc. New York.
    Silva, S., Almeida, J., 2003. Dynamic Maximum Tree Depth-A Simple Technique for Avoiding Bloat in Tree-Based GP. In : Cantú-Paz, E., Foster, J.A., Deb, K., et al., eds., Proceedings of GECCO-2003. SpringerVerlag., 1776-1787.
    Silva, S., Costa, E., 2004. Dynamic Li mits for Bloat Control-Variations on Size and Depth. Proceedings of GECCO-2004. Springer-Verlag. 666-677.
    Yin, Z. M., Ding, C. L., 2002. Mastery of MATLAB. Tsinghua University Press, Beijing (in Chinese).
    Yun, Q. X., 2000. Evolutionary Algorithm. Metallurgy Industry Publishing House, Beijing (in Chinese).
    Yun, Q. X., Wang, Z. Q., 1997. Genetic Algorithmand Genetic Programming. Metallurgy Industry Publishing House, Beijing (in Chinese).
    Wang, W. H., Ding, D. X., 2001. Studies of an Artificial Neural Network Method for Inversing Mechanical Parameters of Rock Mass from Measured Mining-Induced Surface Subsidence. Journal of Central-South Institute of Technology, (1): 10-14 (in Chinese with English Abstract).
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)  / Tables(3)

    Article Metrics

    Article views(577) PDF downloads(6) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return