Volume 15 Issue 2
Jun.  2022
Turn off MathJax
Article Contents
Article Navigation >  Water Science and Engineering  > 2022  >  15(2): 170-178
Zhen-xiang Jiang, Hui Chen. 2022: A new early warning method for dam displacement behavior based on non-normal distribution function. Water Science and Engineering, 15(2): 170-178. doi: 10.1016/j.wse.2022.04.001
Citation: Zhen-xiang Jiang, Hui Chen. 2022: A new early warning method for dam displacement behavior based on non-normal distribution function. Water Science and Engineering, 15(2): 170-178. doi: 10.1016/j.wse.2022.04.001
shu

A new early warning method for dam displacement behavior based on non-normal distribution function

doi: 10.1016/j.wse.2022.04.001

  • School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Funds:

This work was supported by the National Natural Science Foundation of China (Grant No. 52109156) and the Science and Technology Project of the Jiangxi Provincial Education Department (Grant No. GJJ190970).

  • Received Date: 2021-04-22
  • Accepted Date: 2021-11-28
  • Rev Recd Date: 2021-11-28
    • Available Online: 2022-06-21
    Abstract
  • Traditional methods for early warning of dam displacements usually assume that residual displacements follow a normal distribution. This assumption deviates from the reality, thereby affecting the reliability of early warning results and leading to misjudgments of dam displacement behavior. To solve this problem, this study proposed an early warning method using a non-normal distribution function. A new early warning index was developed using cumulative distribution function (CDF) values. The method of kernel density estimation was used to calculate the CDF values of residual displacements at a single point. The copula function was used to compute the CDF values of residual displacements at multiple points. Numerical results showed that, with residual displacements in a non-normal distribution, the early warning method proposed in this study accurately reflected the dam displacement behavior and effectively reduced the frequency of false alarms. This method is expected to aid in the safe operation of dams.

     

    • FullText(HTML)
  • loading
    • References(27)
  • Ahn, J.Y., Fuchs, S., Oh, R., 2021. A copula transformation in multivariate mixed discrete-continuous models. Fuzzy Set. Syst. 415, 54-75. https://doi.org/10.1016/j.fss.2020.11.008.
    Alcay, S., Yigit, C.O., Inal, C., Ceylan, A., 2018. Analysis of displacement response of the Ermenek Dam monitored by an integrated geodetic and pendulum system. Int. J. Civ. Eng. 16(10B), 1279-1291. https://doi.org/10.1007/s40999-017-0211-x.
    Chen, B., Hu, T.Y., Huang, Z.S., Fang, C.H., 2019. A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data. Struct. Health Monit. 18(5-6), 1355-1371. https://doi.org/10.1177/1475921718797949.
    Chen, B., Huang, Z., Bao, T., Zhu, Z., 2021. Deformation early-warning index for heightened gravity dam during impoundment period. Water Sci. Eng. 14(1), 54-64. https://doi.org/10.1016/j.wse.2021.03.001.
    Dai, B., Gu, C.S., Zhao, E.F., Qin, X.N., 2018. Statistical model optimized random forest regression model for concrete dam deformation monitoring. Struct.Control Health Monit. 25(6), e2170. https://doi.org/10.1002/stc.2170.
    de Granrut, M., Simon, A., Dias, D., 2019. Artificial neural networks for the interpretation of piezometric levels at the rockeconcrete interface of arch dams.
    Eng. Struct. 178, 616-634. https://doi.org/10.1016/j.engstruct.2018.10.033.
    Gamse, S., Zhou, W.H., Tan, F., Yuen, K.V., 2018. Hydrostaticeseasonetime model updating using Bayesian model class selection. Reliab. Eng. Syst.Saf. 169, 40-50. https://doi.org/10.1016/j.ress.2017.07.018.
    Gamse, S., Henriques, M.J., Oberguggenberger, M., Mata, J.T., 2020. Analysis of periodicities in long-term displacement time series in concrete dams. Struct.Control Health Monit. 27(3), e2477. https://doi.org/10.1002/stc.2477.
    Gu, H., Yang, M., Gu, C., Huang, X., 2021. A factor mining model with optimized random forest for concrete dam deformation monitoring. Water Sci. Eng. 14(4), 330-336. https://doi.org/10.1016/j.wse.2021.10.004.
    Hellgren, R., Malm, R., Ansell, A., 2020. Performance of data-based models for early detection of damage in concrete dams. Struct. Infrastruct. Eng. 17(2), 275-289. https://doi.org/10.1080/15732479.2020.1737146.
    Hu, J., Wu, S.H., 2019. Statistical modeling for deformation analysis of concrete arch dams with influential horizontal cracks. Struct. Health Monit. 18(2), 546-562. https://doi.org/10.1177/1475921718760309.
    Huang, X.F., Zheng, D.J., Yang, M., Gu, H., Su, H.Z., Cui, X.B., Cao, W.H., 2018. Displacement aging component-based stability analysis for the concrete dam. Geomech. Eng. 14(3), 241-246. https://doi.org/10.12989/gae.2018.14.3.241.
    Kakizawa, Y., 2021. Recursive asymmetric kernel density estimation for nonnegative data. J. Nonparametric Statistics 33(2), 197-224. https://doi.org/10.1080/10485252.2021.1928120.
    Kang, F., Liu, X., Li, J.J., 2020. Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines. Struct. Health Monit. 19(4), 987-1002. https://doi.org/10.1177/1475921719872939.
    Li, X., Li, Y., Lu, X., Wang, Y.F., Zhang, H., Zhang, P., 2020. An online anomaly recognition and early warning model for dam safety monitoring data. Struct. Health Monit. 19(3), 796-809. https://doi.org/10.1177/1475921719864265.
    Liu, W.J., Pan, J.W., Ren, Y.S., Wu, Z.G., Wang, J.T., 2020. Coupling prediction model for long-term displacements of arch dams based on long short-term memory network. Struct. Control Health Monit. 27(7), e2548.https://doi.org/10.1002/stc.2548.
    Sato, H., Sasaki, T., Kondo, M., Kobori, T., Onodera, A., Yoshikawa, K., Sango, D., Morita, Y., 2017. Basic investigation of displacement monitoring of dams following earthquakes based on SAR satellite data. J. Disaster Res. 12(3), 515-525. https://doi.org/10.20965/jdr.2017.p0515.
    Shao, C.F., Gu, C.S., Yang, M., Xu, Y.X., Su, H.Z., 2018. A novel model of dam displacement based on panel data. Struct. Control Health Monit. 25(1), e2037. https://doi.org/10.1002/stc.2037.
    Sigtryggsdottir, F.G., Snaebjoernsson, J.T., Grande, L., 2018. Statistical model for dam-settlement prediction and structural-health assessment. J. Geotech.Geoenviron. Eng. 144(9), 04018059. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001916.
    Su, H.Z., Yang, M., Wen, Z.P., Cao, J.P., 2016. Deformation-based safety monitoring model for high slope in hydropower project. J. Civil Struct.Health Monit. 6(5), 779-790. https://doi.org/10.1007/s13349-016-0198-z.
    Taaffe, K., Pearce, B., Ritchie, G., 2021. Using kernel density estimation to model surgical procedure duration. Int. Trans. Oper. Res. 28(1), 401-418.https://doi.org/10.1111/itor.12561.
    Tabari, M.M.R., Sanayei, H.R.Z., 2019. Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft Comput. 23(19), 9629-9645. https://doi.org/10.1007/s00500-018-3528-8.
    Tonini, D., 1956. Observed behavior of several Italian arch dams. J. Power Div. 3, 82-86. https://doi.org/10.1061/JPWEAM.0000062.
    Tsionas, M.G., Andrikopoulos, A., 2020. On a high-dimensional model representation method based on Copulas. Eur. J. Oper. Res. 284(3), 967-979.https://doi.org/10.1016/j.ejor.2020.01.026.
    Yang, Y., Sang, X.Z., Yang, S.M., Hou, X.H., Huang, Y.J., 2019. High-precision vision sensor method for dam surface displacement measurement.IEEE Sensor. J. 19(24), 12475-12481. https://doi.org/10.1109/JSEN.2019.2940069.
    Yavasoglu, H.H., Kalkan, Y., Tiryakioglu, I., Yigit, C.O., Ozbey, V., Alkan, M.N., Bilgi, S., Alkan, R.M., 2018. Monitoring the deformation and strain analysis on the Ataturk Dam, Turkey. Geomatics Nat. Hazards Risk 9(1), 94-107. https://doi.org/10.1080/19475705.2017.1411400.
    • 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

    Figures(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1804) PDF downloads(2) 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