Volume 12 Issue 3
Sep.  2019
Turn off MathJax
Article Contents
Article Navigation >  Water Science and Engineering  > 2019  >  12(3): 205-212
Jie Yang, Xu-dong Qu, Meng Chang. 2019: An intelligent singular value diagnostic method for concrete dam deformation monitoring. Water Science and Engineering, 12(3): 205-212. doi: 10.1016/j.wse.2019.09.006
Citation: Jie Yang, Xu-dong Qu, Meng Chang. 2019: An intelligent singular value diagnostic method for concrete dam deformation monitoring. Water Science and Engineering, 12(3): 205-212. doi: 10.1016/j.wse.2019.09.006
shu

An intelligent singular value diagnostic method for concrete dam deformation monitoring

doi: 10.1016/j.wse.2019.09.006

  • a Institute of Water Resources and Hydro-electric Engineering, Xi’an University of Technology, Xi’an 710048, China

  • b Sinohydro Engineering Bureau 15 Co., Ltd., Xi’an 710016, China

Funds:  This work was supported by the National Natural Science Foundation of China (Grant No. 51579207), the Open Foundation of State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area (Grant No. 2016ZZKT-8), and the Key Projects of Natural Science Basic Research Program of Shaanxi Province (Grant No. 2018JZ5010).
More Information
  • Corresponding author: Xu-dong Qu
  • Received Date: 2018-10-30
  • Rev Recd Date: 2019-08-22
    Abstract
  • Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status. As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction. To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study. An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) curve coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91. Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods. Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.

     

    • FullText(HTML)
  • loading
    • References(22)
  • Abid, A., Zhang, M.J., Bagaria, V.K., Zou, J., 2018. Exploring patterns enriched in a dataset with contrastive principal component analysis. Nature Communications 9(1). https://doi.org/10.1038/s41467-018-04608-8.
    Du, Y.C., Du, D.P., 2018. Fault detection and diagnosis using empirical mode decomposition based principal component analysis. Computers & Chemical Engineering 115, 1-21. https://doi.org/10.1016/j.compchemeng.2018.03.022.
    Gu, C.S., Zhao, E.F., Jin, Y., Su, H.Z., 2011. Singular value diagnosis in dam safety monitoring effect values. Science China Technological Sciences 54(5), 1169-1176. https://doi.org/10.1007/s11431-011-4339-7.
    Han, T., Yuan, J.H., Tang, J., An, L.Z., 2016. An approach of intelligent compound fault diagnosis of rolling bearing based on MWT and CNN. Journal of Mechanical Transmission 40(12), 139-143 (in Chinese).
    Jasem, A.A., 2017. Simulation of outflow reservoir level and hydro-power at Dukan Dam using artificial neural network. Journal of Computational and Theoretical Nanoscience 14(10), 4686-4693. https://doi.org/10.1166/jctn.2017.6883.
    Li, J., Qiu, T., Wen, C., Xie, K., Wen, F.Q., 2018. Robust face recognition using the deep C2D-CNN model based on decision-level fusion. Sensors 18(7), 2080. https://doi.org/10.3390/s18072080.
    Li, N., Li, P., Shi, X.L., Yan, K., Ren, W.P., 2010. Outlier identify based on BP neural network in dam safety monitoring. In: Proceedings of the 2nd International Asia Conference on Informatics in Control, Automation and Robotics. IEEE, pp. 210-214. https://doi.org/10.1109/CAR.2010.5456564.
    Liang, M.J., Cui, X.Y., Song, Q.S., Zhao, X.M., 2017. Traffic sign recognition method based on HOG-Gabor feature fusion and SoftMax classifier. Journal of Traffic and Transportation Engineering 17(03), 151-158 (in Chinese). https://doi.org/10.3969/j.i ssn.1671-1637.2017.03.016.
    Ma, C.H., Yang, J., Cheng, L., Li, T., Wang, L., 2017. Slope stability analysis based on HS-RVM with mixed kernel. Chinese Journal of Rock Mechanics and Engineering 36(s1), 3409-3415 (in Chinese). https://doi.org/10.13722/j.cnki.jrme.2016.0306.
    Polson, N.G., Sokolov, V.O., 2017. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies 79, 1-17. https://doi.org/10.1016/j.trc.2017.02.024.
    Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural Networks 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003.
    Su, H.Z., Wu, Z., Wen, Z.P., 2001. Analysis of genesis of doubtful points by use of safety monitoring system for Shuikou Dam. Journal of Hohai University (Natural Sciences) 29(3), 55-59 (in Chinese).
    Tang, M., He, J.P., Li, Z.Z., Wu, Y.F., 2005. Analysis to abnormal values in dam safety monitoring and its module creation. Journal of Yangtze River Scientific Research Institute 22(3), 29-31 (in Chinese). https://doi.org/10.3969/j.issn.1001-5485.2005.03.008.
    Wei, X.X., Zhou, Y.Q., 2010. A new performance categories evaluation method based on ROC curve. Computer Technology and Development 20(11), 47-50 (in Chinese).
    Yin, F., Feng, D.Z., 2008. Identification of face based on PCA algorithm. Computer Technology and Development 10, 31-33 (in Chinese).
    Zhang, J.J., Zong, C.Q., 2015. Deep neural networks in machine translation: an overview. IEEE Intelligent Systems 30(5), 16-25. https://doi.org/10.1109/MIS.2015.69.
    Zhang, K., Zuo, W.M., Gu, S.H., Zhang, L., 2017. Learning deep CNN denoiser prior for image restoration. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp. 2808-2817. https://doi.org/10.1109/CVPR.2017.300.
    Zhang, Q.C., Yang, L.T., Chen, Z.K., Li, P., 2018. A survey on deep learning for big data. Information Fusion 42, 146-157. https://doi.org/10.1016/j.inffus.2017.10.006.
    Zhao, R., Yan, R.Q., Chen, Z.H., Mao, K.Z., Wang, P., Gao, R.X., 2019. Deep learning and its applications to machine health monitoring. Mechanical Systems & Signal Processing 115, 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050.
    Zhou, F.Y., Jin, L.P., Dong, J., 2017. Review of convolutional neural networks. Chinese Journal of Computers 40(6), 1229-1251 (in Chinese).
    Zhou, J., Li, E.M., Yang, S., Wang, M.Z., Shi, X., Yao, S.Z., Mitri, H.S., 2019. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science 118, 505-518. https://doi.org/10.1016/j.ssci.2019.05.046.
    Zubair, S., Yan, F., Wang, W.W., 2013. Dictionary learning based sparse coefficients for audio classification with max and average pooling. Digital Signal Processing 23(3), 960-970. https://doi.org/10.1016/j.dsp.2013.01.004.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 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
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (680) PDF downloads(499) Cited by()
    Proportional views
    Related

    Copyright © 2009 Editorial office of Water Science and Engineering 苏ICP备12023610号-1

    Supported by: Beijing Renhe Information Technology Co. Ltd   support: info@rhhz.net

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return