Subsidence analysis of hydroelectric dam using the Kalman filter – a case study in Hoa Binh hydropower plant, Vietnam
Abstract
Hydroelectric dams have a great influence on the safety of the downstream area. Therefore, deformation monitoring for assessing the safety of dam should be carried out regularly. In order to improve efficiency of the dam management, it is necessary to analyse the displacement values in space, over time to assess overally the displacement of dam. In this purpose, an attempt was conducted to analyse the subsidence of hydroelectric dams located in Hoa Binh, Vietnam using one of the most useful method – Kalman filter. Kalman filter is the unique method that can determine influence of external factors (particularly, elevation of water level in the reservoir) on dams, simultaneously forecast the displacement values of dam in the future. Moreover, Kalman filter allows to predict subsidence accurately in about 6 months that is longer prediction time than other static models. These are clearly presented and discussed in the article. The obtained results demonstrate the high applicability of Kalman filter method in analysing and forecasting the subsidence of the Hoa Binh hydroelectric dam.
Keyword : subsidence monitoring, prediction, subsidence analysis, Kalman filter, hydroelectric dams, external factors
How to Cite
Nguyen, T. K. T. (2024). Subsidence analysis of hydroelectric dam using the Kalman filter – a case study in Hoa Binh hydropower plant, Vietnam. Geodesy and Cartography, 50(2), 84–96. https://doi.org/10.3846/gac.2024.18892
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bak, A. (2016). The prediction of the displacements of the body of the solina dam as a tool to improve safety of the facility. World Scientific News.
Dai, W., Liu, N., Santerre, R., & Pan, J. (2016). Dam deformation monitoring data analysis using space-time Kalman filter. International Journal of Geo-Information, 5(12), Article 236. https://doi.org/10.3390/ijgi5120236
Electricity of Vietnam, Hoa Binh Hydropower Company. (2020). Report on results of monitoring and assessment of safety status of Hoa Binh hydroelectric dam.
Gamse, S. & Oberguggenberger, M. (2016). Assessment of long-term coordinate time series using hydrostatic-season-time model for rock-fill embankment dam. Structural Control and Health Monitoring, 24, Article e1859. https://doi.org/10.1002/stc.1859
Gibbs, B. P. (2011). Advanced Kalman filtering, least-squares and modeling. John Wiley & Sons. https://doi.org/10.1002/9780470890042
He, P., & Li, Y. (2022). A data driven dam deformation forecasting and interpretation method using the measured prototypical temperature data. Water. https://doi.org/10.3390/w14162538
Irughe, R. E., Ehiorobo, J., & Ehigiator, M. (2014). Prediction of dam deformation using Kalman filter technique. In FIG Congress 2014, Engaging the Challenges-Enhancing the relevance. Kuala Lumpur, Malaysia.
Jang, R. B., Pan, J. C., Yang, M. D., & Xu, L. (2010, May 21–24). Application of the improved BP Neural network model to deformaiton analysis of an earth-stone dam. In The 2nd International conference on future computer and communication, Wuhan, China.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(Series D), 35–45. https://doi.org/10.1115/1.3662552
Li, F. Q., Wang, Z. Y., & Liu, G. H. (2013). Towards an erra correction model for dam monitoring data analysis based on cointegration theory. Structural Safety, 43, 12–20. https://doi.org/10.1016/j.strusafe.2013.02.005
Liu, B., Zhang, B., Xu, J. & Gu, G. K. (2010). Forecast for dam deformation based on multiple linear regression model. Yangtze River, 41(20), 53–55.
Lu, F. (2002). Application of Kalman filter method in dam deformation analysis. Site Investigation Science and Technology, 1, 43–45.
Lu, F. (2003). Application of Kalman filter method considering multi-factors to dam deformation analysis. Dam Observation and Geotechnical Tests, (3), 71–73.
Lu, F. M. & Li, J. (2013). Application of Kalman filter method based on water level factors in the dam deformation forecast. Advanced Materials Research, 648, 376–380. https://doi.org/10.4028/www.scientific.net/AMR.648.376
Luo, X., Wang, L., Meng, X., Gan, W., & Chen, Y. (2020). A prediction model of structural settlement based on EMD-SVR-WNN. Advances in Civil Engineering, 2020(4), Article 8831965. https://doi.org/10.1155/2020/8831965
Ma, L. X., Wang, F. Y., & Chen, J. P. (2009). Analysis and prediction of dam deformation based on ANN – an example of deformation at monitoring point 27 of Xijin dam. Journal of Jilin university (Earth Science Edition), 39(3).
Moradi, G., & Ebrahimnezhed, E. (2017). Foundation and embankment settlement monitoring in Alavian dam. Electronic Journal of Geotechnical Engineering, 22(09), 3819–3830.
Noorzad, A., Behnia, D., Moeinossadat, S. R., & Ahangari, K. (2014). Prediction of crest settlement of concrete-faced rockfill dams using a new approach [Conference presentation]. International Symposium on “Dams in a Global Environmental Challenges”, Bali, Indonesia.
Ogundare, J. O. (2019). Understanding least squares estimation and geomatics data analysis. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119501459
Pelecanos, L., Skarlatos, D., & Pantazis, G. (2018). Finite element analysis of the monitored long-term settlement behaviour of Kouris earth dam in Cyprus. In The 9th European conference on numerical methods in geotechnical engineering (IX NUMGE), At Porto, Portugal. https://doi.org/10.1201/9781351003629-155
Sigtryggsdottin, F. J., Snæbjörnsson J., Sigbjornsson, R., & Grande, L. (2013). Rockfill dam settlement data processing and statistical analysis [Conference presentation]. The Third International Symposium on Rockfilldams, Kunming, China.
Sigtryggsdottir, F. G., Grande, L., & Sigbjornsson, R. (2015). Statistical analysis of dam settlement data. In Geotechnical engineering for infrastructure and development: XVI European conference on soil mechanics and geotechnical engineering (pp. 3759–3764).
Sigtryggsdottin, F. G., Snæbjörnsson, J., & Grande, L. (2018). Statistical model for dam-settlement prediction and structure health assessment. Journal of Geotechnical and Geoenvironmental Engineering, 144(9). https://doi.org/10.1061/(ASCE)GT.1943-5606.0001916
Tran, D. N. (2011). Evaluation of the dependence of the construction displacement on some external factors by single linear correlation analysis method. Journal of Construction Science and Technology, 2, 58–64.
Tran, K., & Nguyen, P. Q. (2010). Structural deformation monitoring. Publisher of Transportation.
Tran, H. Q., Nguyen, L. T., & Tong, H. T. (2017). Research and build a suitable subsidence prediction model in analysing and predicting subsidence of soft ground from monitoring results. Journal of Mining-Geology Science and Technology, 4(58), 93–100.
US. Army Corps of Enginners. (2018). Structural Deformation Surveying.
Wang, H. Q., Zhu, Y. W., Chen, J. S., & Zhao, Y. E. (2012). The monitoring analysis on subsidence of the connection earth dam in Chaozhou water supply pivot project. In Applied mechanics and materials (Vols. 170–173, pp. 1897–1920). Trans Tech Publications. https://doi.org/10.4028/www.scientific.net/AMM.170-173.1897
Welch, G., & Bishop, M. (2006). An introduction to the Kalman Filter. Department of Computer Science, University of North Carolina at Chapel Hill. www.cs.unc.edu/welch/media/pdf/kalman_introduction/pdf
Zou, J., Bui, K.-T. T., Xiao, Y., & Doan, C. V. (2018). Dam deformation analysis based on BPNN merging models. Geo-spatial Information Science, 21(2), 149–157. https://doi.org/10.1080/10095020.2017.1386848
Zhou, W., Hua, J., Chang, X., & Zhou, C. (2011). Settlement analysis of the Shuibuya concrete-face rockfill dam. Computers and Geotechnics, 38(2), 269–280. https://doi.org/10.1016/j.compgeo.2010.10.004
Dai, W., Liu, N., Santerre, R., & Pan, J. (2016). Dam deformation monitoring data analysis using space-time Kalman filter. International Journal of Geo-Information, 5(12), Article 236. https://doi.org/10.3390/ijgi5120236
Electricity of Vietnam, Hoa Binh Hydropower Company. (2020). Report on results of monitoring and assessment of safety status of Hoa Binh hydroelectric dam.
Gamse, S. & Oberguggenberger, M. (2016). Assessment of long-term coordinate time series using hydrostatic-season-time model for rock-fill embankment dam. Structural Control and Health Monitoring, 24, Article e1859. https://doi.org/10.1002/stc.1859
Gibbs, B. P. (2011). Advanced Kalman filtering, least-squares and modeling. John Wiley & Sons. https://doi.org/10.1002/9780470890042
He, P., & Li, Y. (2022). A data driven dam deformation forecasting and interpretation method using the measured prototypical temperature data. Water. https://doi.org/10.3390/w14162538
Irughe, R. E., Ehiorobo, J., & Ehigiator, M. (2014). Prediction of dam deformation using Kalman filter technique. In FIG Congress 2014, Engaging the Challenges-Enhancing the relevance. Kuala Lumpur, Malaysia.
Jang, R. B., Pan, J. C., Yang, M. D., & Xu, L. (2010, May 21–24). Application of the improved BP Neural network model to deformaiton analysis of an earth-stone dam. In The 2nd International conference on future computer and communication, Wuhan, China.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(Series D), 35–45. https://doi.org/10.1115/1.3662552
Li, F. Q., Wang, Z. Y., & Liu, G. H. (2013). Towards an erra correction model for dam monitoring data analysis based on cointegration theory. Structural Safety, 43, 12–20. https://doi.org/10.1016/j.strusafe.2013.02.005
Liu, B., Zhang, B., Xu, J. & Gu, G. K. (2010). Forecast for dam deformation based on multiple linear regression model. Yangtze River, 41(20), 53–55.
Lu, F. (2002). Application of Kalman filter method in dam deformation analysis. Site Investigation Science and Technology, 1, 43–45.
Lu, F. (2003). Application of Kalman filter method considering multi-factors to dam deformation analysis. Dam Observation and Geotechnical Tests, (3), 71–73.
Lu, F. M. & Li, J. (2013). Application of Kalman filter method based on water level factors in the dam deformation forecast. Advanced Materials Research, 648, 376–380. https://doi.org/10.4028/www.scientific.net/AMR.648.376
Luo, X., Wang, L., Meng, X., Gan, W., & Chen, Y. (2020). A prediction model of structural settlement based on EMD-SVR-WNN. Advances in Civil Engineering, 2020(4), Article 8831965. https://doi.org/10.1155/2020/8831965
Ma, L. X., Wang, F. Y., & Chen, J. P. (2009). Analysis and prediction of dam deformation based on ANN – an example of deformation at monitoring point 27 of Xijin dam. Journal of Jilin university (Earth Science Edition), 39(3).
Moradi, G., & Ebrahimnezhed, E. (2017). Foundation and embankment settlement monitoring in Alavian dam. Electronic Journal of Geotechnical Engineering, 22(09), 3819–3830.
Noorzad, A., Behnia, D., Moeinossadat, S. R., & Ahangari, K. (2014). Prediction of crest settlement of concrete-faced rockfill dams using a new approach [Conference presentation]. International Symposium on “Dams in a Global Environmental Challenges”, Bali, Indonesia.
Ogundare, J. O. (2019). Understanding least squares estimation and geomatics data analysis. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119501459
Pelecanos, L., Skarlatos, D., & Pantazis, G. (2018). Finite element analysis of the monitored long-term settlement behaviour of Kouris earth dam in Cyprus. In The 9th European conference on numerical methods in geotechnical engineering (IX NUMGE), At Porto, Portugal. https://doi.org/10.1201/9781351003629-155
Sigtryggsdottin, F. J., Snæbjörnsson J., Sigbjornsson, R., & Grande, L. (2013). Rockfill dam settlement data processing and statistical analysis [Conference presentation]. The Third International Symposium on Rockfilldams, Kunming, China.
Sigtryggsdottir, F. G., Grande, L., & Sigbjornsson, R. (2015). Statistical analysis of dam settlement data. In Geotechnical engineering for infrastructure and development: XVI European conference on soil mechanics and geotechnical engineering (pp. 3759–3764).
Sigtryggsdottin, F. G., Snæbjörnsson, J., & Grande, L. (2018). Statistical model for dam-settlement prediction and structure health assessment. Journal of Geotechnical and Geoenvironmental Engineering, 144(9). https://doi.org/10.1061/(ASCE)GT.1943-5606.0001916
Tran, D. N. (2011). Evaluation of the dependence of the construction displacement on some external factors by single linear correlation analysis method. Journal of Construction Science and Technology, 2, 58–64.
Tran, K., & Nguyen, P. Q. (2010). Structural deformation monitoring. Publisher of Transportation.
Tran, H. Q., Nguyen, L. T., & Tong, H. T. (2017). Research and build a suitable subsidence prediction model in analysing and predicting subsidence of soft ground from monitoring results. Journal of Mining-Geology Science and Technology, 4(58), 93–100.
US. Army Corps of Enginners. (2018). Structural Deformation Surveying.
Wang, H. Q., Zhu, Y. W., Chen, J. S., & Zhao, Y. E. (2012). The monitoring analysis on subsidence of the connection earth dam in Chaozhou water supply pivot project. In Applied mechanics and materials (Vols. 170–173, pp. 1897–1920). Trans Tech Publications. https://doi.org/10.4028/www.scientific.net/AMM.170-173.1897
Welch, G., & Bishop, M. (2006). An introduction to the Kalman Filter. Department of Computer Science, University of North Carolina at Chapel Hill. www.cs.unc.edu/welch/media/pdf/kalman_introduction/pdf
Zou, J., Bui, K.-T. T., Xiao, Y., & Doan, C. V. (2018). Dam deformation analysis based on BPNN merging models. Geo-spatial Information Science, 21(2), 149–157. https://doi.org/10.1080/10095020.2017.1386848
Zhou, W., Hua, J., Chang, X., & Zhou, C. (2011). Settlement analysis of the Shuibuya concrete-face rockfill dam. Computers and Geotechnics, 38(2), 269–280. https://doi.org/10.1016/j.compgeo.2010.10.004