Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Hu, Yunxia; Shao, Chenfei; Gu, Chongshi; Meng, Zhenzhu

doi:10.3390/w11040714

Cited by 28 publications

(20 citation statements)

References 17 publications

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“…Both are caused by seepage water and have certain influences on dam stability, deformation, and stress; (2) Dam leakage causes the water to take the fine particles out of the dam body and form a seepage passage, which endangers the stability of the dam; (3) Upstream impoundment can not only seep through the dam body and foundation, but also seep downward around the bank slopes at both ends of the dam. Considering the temporal continuity and spatial correlation [21] of the monitoring points, each indicator is jointly monitored by several monitoring points for a long time series. With reference to the monitoring data and the dam seepage monitoring research results [10,22,23], the comprehensive indicator system of concrete dam seepage state is shown in Figure 1.…”

Section: The Comprehensive Indicator System Of Dam Seepage Statementioning

confidence: 99%

Seepage Comprehensive Evaluation of Concrete Dam Based on Grey Cluster Analysis

Chen

et al. 2019

Water

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Most concrete dams have seepage problems to some degree, so it is a common strategy to maintain ongoing monitoring and take timely repair measures. In order to grasp the real operation state of dam seepage, it is vital to analyze the measured data of each monitoring indicator and establish an appropriate prediction equation. However, dam seepage states under the load and environmental influences are very complicated, involving various monitoring indicators and multiple monitoring points of each indicator. For the purpose of maintaining the temporal continuity and spatial correlation of monitoring objects, this paper used a multi-indicator grey clustering analysis model to explore the grey correlation among various indicators, and realized a comprehensive evaluation of a dam seepage state by computation of the clustering coefficient. The case study shows that the proposed method can be successfully applied to the health monitoring of concrete dam seepage.

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Section: The Comprehensive Indicator System Of Dam Seepage Statementioning

confidence: 99%

Seepage Comprehensive Evaluation of Concrete Dam Based on Grey Cluster Analysis

Chen

et al. 2019

Water

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“…Accidental loads, such as earthquakes and massive landslides, are not within the scope of this research. Several prediction approaches have been developed in recent years 10–20 . The hydraulic‐seasonal‐time (HST) model is commonly used.…”

Section: Introductionmentioning

confidence: 99%

Hybrid hydraulic‐seasonal‐time model for predicting the deformation behaviour of high concrete dams during the operational period

Yang

Chen

et al. 2021

Struct Control Health Monit

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Summary Considering the limitations of the traditional hydraulic‐seasonal‐time (HST) model, this study proposes a hybrid modeling method for the deformation prediction of high concrete dams during the operational period. First, the elastic finite element (FE) method is applied to simulate the interactive effects of structural properties, topography, geology, and high hydrostatic load on the deformation behaviour of high concrete dams in operating conditions. The hybrid model of hydrostatic pressure deformation is established. The hybrid hydraulic‐seasonal‐time (HHST) model is proposed. Second, the self‐adaptive stochastic inertia weight, dynamic learning factors, and velocity and position parameters are introduced to improve the particle swarm optimization (PSO) algorithm. The hybrid prediction approach is developed through the comprehensive application of the HHST model and the improved PSO algorithm. The proposed methodology is adopted for the Jinping I project, which is the highest concrete arch dam in the world. The analysis results indicate that the model accuracy is good and that the model performance is promoted.

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“…In order to enhance the model stability, recent studies have integrated the spatial correlations of the monitoring points into the statistical models, by classifying the monitoring data at different monitoring points into several groups [15], [16]. In the statistical models with monitoring data being classified, the spatial correlations were quantified by groups, whereas the correlations between monitoring points in one group were lacking.…”

Section: Introductionmentioning

confidence: 99%

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

Meng

et al. 2020

IEEE Access

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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.

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Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Cited by 28 publications

References 17 publications

Seepage Comprehensive Evaluation of Concrete Dam Based on Grey Cluster Analysis

Seepage Comprehensive Evaluation of Concrete Dam Based on Grey Cluster Analysis

Hybrid hydraulic‐seasonal‐time model for predicting the deformation behaviour of high concrete dams during the operational period

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

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