Improve the Model Stability of Dam’s Displacement Prediction Using a Numerical-Statistical Combined Model

In most studies of dam’s displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to improve the model stability. The displacement was modelled within three modules: recoverable displacement (i.e., displacement induced by the external load including the water pressure and temperature), non-recoverable displacement (i.e., displacement due to the inherent variations of the materials such as the creep and fatigue of the concrete), and measurement errors (i.e., instrument error and human error). To reduce the random errors and increase the model stability, we used the numerical simulation to constrain the coefficients of explanatory variables for the recoverable displacement. The non-recoverable displacement was estimated by empirical equations, and the measurement errors were given by Gaussian distributions. The randomness of coefficients in the model among all monitoring points are constrained further by random coefficient model. We adopted the root mean square error (RMSE) at varying time and the change ratio of the coefficients (CRC) to evaluate the model stability. Results indicated that the proposed model not only has better prediction accuracy but also has better model stability compared with the statistical model and coordinates-included statistical model proposed in previous studies.

[1]  Jordi J. Mallorqui,et al.  Monitoring an earthfill dam using differential SAR interferometry: La Pedrera dam, Alicante, Spain , 2013 .

[2]  N. Khaji,et al.  System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element–boundary element approach , 2010 .

[3]  Yong-seong Kim,et al.  Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model , 2008 .

[4]  C. Gu,et al.  Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction , 2020 .

[5]  Yating Hu,et al.  Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows , 2020, Water.

[6]  C. Gu,et al.  A Data-Driven Approach Based on Multivariate Copulas for Quantitative Risk Assessment of Concrete Dam , 2019, Journal of Marine Science and Engineering.

[7]  Eugenio Oñate,et al.  Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations , 2017 .

[8]  P. Léger,et al.  Hydrostatic, Temperature, Time-Displacement Model for Concrete Dams , 2007 .

[9]  Jia Liu,et al.  Concrete dam deformation prediction model for health monitoring based on extreme learning machine , 2017 .

[10]  P. Swamy.  Efficient Inference in a Random Coefficient Regression Model , 1970 .

[11]  H. Su,et al.  An approach for deformation modulus mechanism of super-high arch dams , 2019 .

[12]  Pilate Moyo,et al.  Health monitoring of concrete dams: a literature review , 2014 .

[13]  Mohammad Amin Hariri-Ardebili,et al.  Support vector machine based reliability analysis of concrete dams , 2018 .

[14]  D. J. Vicente,et al.  Treatment of Solar Radiation by Spatial and Temporal Discretization for Modeling the Thermal Response of Arch Dams , 2014 .

[15]  Meng Yang,et al.  A novel model of dam displacement based on panel data , 2018 .

[16]  A. El-Shafie,et al.  Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS) , 2013, Water Resources Management.

[17]  J. Mata,et al.  Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models , 2011 .

[18]  G. C. Nayak,et al.  Nonlinear Analysis of Concrete Face Rockfill Dam , 1990 .

[19]  Robert E. Ployhart,et al.  Growth Modeling Using Random Coefficient Models: Model Building, Testing, and Illustrations , 2002 .

[20]  Chin-Hsiung Loh,et al.  Monitoring of long‐term static deformation data of Fei‐Tsui arch dam using artificial neural network‐based approaches , 2013 .

[21]  Spilios D. Fassois,et al.  Gaussian Mixture Random Coefficient model based framework for SHM in structures with time–dependent dynamics under uncertainty , 2017 .

[22]  C. Gu,et al.  Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model , 2019, Water.