Journal of Applied and Computational Mechanics
Analysis of Possibilities to Use Predictive Mathematical Models for Studying the Dam Deformation State
Document Type : Research Paper
Authors
1 Siberian State University of Geosystems and Technologies, 10, Plakhotny str., Novosibirsk, 630108, Russia
2 Siberian Federal University (SFU), Free ave., 79, Krasnoyarsk, 660041, Russia
Abstract
Long-term monitoring of the safety and reliability of large dams operation has been attracting increasing attention of researchers. Moreover, special consideration is given to the study of dam displacements that characterize its global behavior. The article discusses specifics of constructing predictive mathematical models for studying the deformation process associated with displacements of the high-head dam crest. The authors present the most successfully designed predictive mathematical models for various combinations of input effective factors, including the results of field observations and the calculated values of the component displacements. These models allow forecasting the control points of the dam body for various time stages of its operation. The advantages of using a mathematical model with separate introduction of the main effective factors into the model are shown, thereby eliminating the effect of their multicollinearity. Using the example of the Sayano-Shushenskaya hydro power plant for certain time stages of the dam operation and structures with different temperature conditions (average, warm and cold in respect to annual temperatures), the authors present the results of forecasting the dam displacements.
Keywords
Main Subjects
Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
[1] 2009 Sayano-Shushenskaya Power Station Accident, Wikipedia, 2020
[2] Li, B., Yang, J., Hu, D., Dam Monitoring Data Analysis Methods: A Literature Review, Structural Control and Health Monitoring, 27(3), 2020, e2501.
[3] Léger, P., Leclerc, M., Hydrostatic, Temperature, Time-Displacement Model for Concrete Dams, Journal of Engineering Mechanics, 133(3), 2007, 267–277.
[4] Wu, Z.R., Shen, C.S., Ruan, H.X., Factor Selection of Statistical Model for Concrete Dam Deformation, Journal of Hohai University, 6(16), 1988, 1–9.
[5] Vul’fovich, N.A., Gordon, L.A., Stefanenko, N.I., Arched-gravity dam of Sayano-Shushenskaya HPP (Assessment of the technical state from the data of field observations), OAO “VNIIG,” St. Petersburg, Russia, 2012
[6] Ardito, R., Maier, G., Massalongo, G., Diagnostic Analysis of Concrete Dams Based on Seasonal Hydrostatic Loading, Engineering Structures, 30(11), 2008, 3176–3185.
[7] Tatin, M., Briffaut, M., Dufour, F., Simon, A., Fabre, J.-P., Statistical Modelling of Thermal Displacements for Concrete Dams: Influence of Water Temperature Profile and Dam Thickness Profile, Engineering Structures, 165, 2018, 63–75.
[8] Mata, J., Tavares de Castro, A., Sá da Costa, J., Constructing Statistical Models for Arch Dam Deformation, Structural Control and Health Monitoring, 21(3), 2014, 423–437.
[9] Zhao, E., Wu, C., Wang, S., Hu, J., Wang, W., Seepage Dissolution Effect Prediction on Aging Deformation of Concrete Dams by Coupled Chemo-Mechanical Model, Construction and Building Materials, 237, 2020, 117603.
[10] Salazar, F., Morán, R., Toledo, M.Á., Oñate, E., Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations, Archives of Computational Methods in Engineering, 24(1), 2017, 1–21.
[11] Stephan, P., Salin, J., Ageing Management of Concrete Structure: Assessment of EDF Methodology in Comparison with SHM and AIEA Guides, Construction and Building Materials, 37, 2012, 924–933.
[12] Jafari, M., Deformation Modelling of Structures Enriched by Inter-Element Continuity Condition Based on Multi-Sensor Data Fusion, Applied Mathematical Modelling, 40(21–22), 2016, 9316–9326.
[13] Gaziev, é.G., Inclines of Horizontal Sections of Sayano-Shushenskaya Arch-Gravity Dam, Power Technology and Engineering, 46(3), 2012, 181–184.
[14] Savich, A.I., Il’in, M.M., Elkin, V.P., Rechitskii, V.I., Basova, A.B., Geologic-Engineering and Geomechanical Models of the Rock Mass in the Bed of the Dam at the Sayano-Shushenskaya HPP, Power Technology and Engineering, 47(2), 2013, 89–101.
[15] Kostylev, V.S., Use of Mathematical “Structure – Bed” Model to Analyze Changes in Kinematic Indicators of the Concrete Arch-Gravity Dam at the Sayano-Shushenskaya HPP from 2004 through 2012, Power Technology and Engineering, 47(3), 2013, 191–199.
[16] Sani Aftabi, A., Lotfi, V., Dynamic Analysis of Concrete Arch Dams by Ideal-Coupled Modal Approach, Engineering Structures, 32(5), 2010, 1377–1383.
[17] Yu, Y., Wang, E., Zhong, J., Liu, X., Li, P., Shi, M., Zhang, Z., Stability Analysis of Abutment Slopes Based on Long-Term Monitoring and Numerical Simulation, Engineering Geology, 183, 2014, 159–169.
[18] Fan, L.F., Yi, X. W., Ma, G.W., Numerical Manifold Method (NMM) Simulation of Stress Wave Propagation through Fractured Rock Mass, International Journal of Applied Mechanics, 05(02), 2013, 1350022.
[19] Pinchuk, V.A., Kuzmin, A.V., Method of Approximating Experimental Data on Fuel Combustion, International Journal of Energy for a Clean Environment, 20(3), 2019, 261–272.
[20] Rico, J., Barateiro, J., Mata, J., Antunes, A., Cardoso, E., Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams. In Machine Learning Paradigms (G. A. Tsihrintzis, M. Virvou, E. Sakkopoulos, and L. C. Jain, eds.), Springer International Publishing, Cham, 2019.
[21] Shen, W.W., Ren, J.M., Multiple Stepwise Regression Analysis Crack Open Degree Data in Gravity Dam, Applied Mechanics and Materials, 477–478, 2013, 888–891.
[22] Yang, J., Hu, D.-X., Wu, Z.-R., Multiple Co-Linearity and Uncertainty of Factors in Dam Safety Monitoring Model, Shuili Xuebao/Journal of Hydraulic Engineering, 12, 2004, 99.
[23] Lan, S., Fang, C., Application of least square method based logistic regression model to settlement prediction of earth-rock fill dam, Water Resources and Hydropower Engineering, 43, 2012, 16–18.
[24] Shao, C., Gu, C., Yang, M., Xu, Y., Su, H., A Novel Model of Dam Displacement Based on Panel Data, Structural Control and Health Monitoring, 25(1), 2018, e2037.
[25] Xi, G., Yue, J., Zhou, B., Tang, P., Application of an Artificial Immune Algorithm on a Statistical Model of Dam Displacement, Computers & Mathematics with Applications, 62(10), 2011, 3980–3986.
[26] Chen, J., The nonlinear parameter estimation of aging deformation of concrete dam, Dam Observation and Geotechnical Testing, 2, 1980, 3–13.
[27] Lombardi, G., Amberg, F., Darbre, G.R., Algorithm for the Prediction of Functional Delays in the Behaviour of Concrete Dams, International Journal on Hydropower and Dams, 15(3), 2008, 111–116.
[28] Wang, S., Gu, C., Bao, T., Observed Displacement Data-Based Identification Method of Deformation Time-Varying Effect of High Concrete Dams, Science China Technological Sciences, 61(6), 2018, 906–915.
[29] Kang, F., Liu, J., Li, J., Li, S., Concrete Dam Deformation Prediction Model for Health Monitoring Based on Extreme Learning Machine, Structural Control and Health Monitoring, 24(10), 2017, e1997.
[30] Cheng, L., Zheng, D., Two Online Dam Safety Monitoring Models Based on the Process of Extracting Environmental Effect, Advances in Engineering Software, 57, 2013, 48–56.
[31] Dai, B., Gu, C., Zhao, E., Qin, X., Statistical Model Optimized Random Forest Regression Model for Concrete Dam Deformation Monitoring, Structural Control and Health Monitoring, 25(6), 2018, e2170.
[32] Wei, B., Yuan, D., Xu, Z., Li, L., Modified Hybrid Forecast Model Considering Chaotic Residual Errors for Dam Deformation, Structural Control and Health Monitoring, 25(8), 2018, e2188.
[33] Liu, W., Pan, J., Ren, Y., Wu, Z., Wang, J., Coupling Prediction Model for Long‐term Displacements of Arch Dams Based on Long Short‐term Memory Network, Structural Control and Health Monitoring, 27(7), 2020, e2548.
[34] Ranković, V., Novaković, A., Grujović, N., Divac, D., Milivojević, N., Predicting Piezometric Water Level in Dams via Artificial Neural Networks, Neural Computing and Applications, 24(5), 2014, 1115–1121.
[35] de Granrut, M., Simon, A., Dias, D., Artificial Neural Networks for the Interpretation of Piezometric Levels at the Rock-Concrete Interface of Arch Dams, Engineering Structures, 178, 2019, 616–634.
[36] Li, L.H., Peng, S.J., Jiang, Z.X., Wei, B.W., Prediction Model of Concrete Dam Deformation Based on Adaptive Unscented Kalman Filter and BP Neural Network, Applied Mechanics and Materials, 513–517, 2014, 4076–4079.
[37] Karimi, I., Khaji, N., Ahmadi, M.T., Mirzayee, M., System Identification of Concrete Gravity Dams Using Artificial Neural Networks Based on a Hybrid Finite Element–Boundary Element Approach, Engineering Structures, 32(11), 2010, 3583–3591.
[38] Li, F., Wang, Z., Liu, G., Fu, C., Wang, J., Hydrostatic Seasonal State Model for Monitoring Data Analysis of Concrete Dams, Structure and Infrastructure Engineering, 11(12), 2015, 1616–1631.
[39] Su, H., Wu, Z., Wen, Z., Identification Model for Dam Behavior Based on Wavelet Network, Computer-Aided Civil and Infrastructure Engineering, 22(6), 2007, 438–448.
[40] Khoroshilov, V.S., Mathematical Modelling of Sayano-Shushenskaya Dam Displacement Process after 2009 Accident, International Journal of Engineering Research in Africa, 39, 2018, 47–59.
[41] Sayano-Shushenskaya Dam. Wikipedia, 2020.
[42] Vul’fovich, N.A., Potekhin, L.P., Influence of the Thermal State of the Sayano-Shushenskaya Dam on the Reservoir Filling Conditions, Power Technology and Engineering, 50(6), 2017, 572–579.
[43] Khoroshilov, V.S., Mazurov, B.T., Antonovich, K.M., Kalentsky, A.I., Kolmogorov, V.G., Prediction of the Movement Process of the High-Head Dam of Sayano-Shushenskaya Hydroelectric Power Plant during Operation after the Accident in 2009, International Journal of Advanced Biotechnology and Research, 8(4), 2017, 1096–1106.
[44] Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., Time Series Analysis: Forecasting and Control, John Wiley & Sons, Inc, Hoboken, New Jersey, 2016.
)
