À priori uplift pressure model for concrete dam foundations based on piezometric monitoring data

Pereira, Renato; Batista, A. L.; Neves, Luís; Casaca, J. M.

doi:10.1080/15732479.2020.1815805

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…(5) According to the measured uplift pressure and the position of the integration point in the cross-section, the uplift pressure at the integration point is calculated by interpolation. (6) The applying method judges whether all the current element's integration points have finished the uplift pressure calculation. If not, steps (2) to ( 5) are repeated.…”

Section: Fluid-solid Couplingmentioning

confidence: 99%

“…In the process of concrete dam design, uplift pressure statistical analysis is usually carried out according to the existing dams, and the value and distribution of uplift pressure are determined in combination with design experience [6]. However, due to the complex distribution of cracks in the dam foundation and the concealment of seepage control measures, the real value and distribution of the uplift pressure of the concrete dam are affected by many factors, which are relatively different from the design.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Zhao

et al. 2022

Applied Sciences

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As an essential load of the concrete dam, the abnormal change of uplift pressure directly threatens the safety and stability of the concrete dam. Therefore, it is of great significance to accurately and efficiently excavate the hidden information of the uplift pressure monitoring data to clarify the safety state of the concrete dam. Therefore, in this paper, density-based spatial clustering of applications with noise (DBSCAN) method is used to intelligently identify the abnormal occurrence point and abnormal stable stage in the monitoring data. Then, an application method of measured uplift pressure is put forward to accurately reflect the spatial distribution and abnormal position of uplift pressure in the dam foundation. It is easy to calculate and connect with the finite element method through self-written software. Finally, the measured uplift pressure is applied to the finite element model of the concrete dam. By comparing the structural behavior of the concrete dam under the design and measured uplift pressure, the influence of abnormal uplift pressure on the safety state of the concrete dam is clarified, which can guide the project operation. Taking a 98.5 m concrete arch dam in western China as an example, the above analysis ideas and calculation methods have been verified. The abnormal identification method and uplift pressure applying method can provide ideas and tools for the structural diagnosis of a concrete dam.

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Section: Fluid-solid Couplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Zhao

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

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Deduction of ultimate equilibrium limit states for concrete gravity dams keyed into rock mass foundations based on large displacement analysis

et al. 2022

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Research on the Uplift Pressure Prediction of Concrete Dams Based on the CNN-GRU Model

Hua

Wang

Xiao

et al. 2023

Water

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Dam safety is considerably affected by seepage, and uplift pressure is a key indicator of dam seepage. Thus, making accurate predictions of uplift pressure trends can improve dam hazard forecasting. In this study, a convolutional neural network, (CNN)-gated recurrent neural network, (GRU)-based uplift pressure prediction model was developed, which included the CNN model’s feature extractability and the GRU model’s learnability for time series correlation data. Then, the model performance was verified using a dam as an example. The results showed that the mean absolute errors (MAEs) of the CNN-GRU model were 0.1554, 0.0398, 0.2306, and 0.1827, and the root mean square errors (RMSEs) were 0.1903, 0.0548, 0.2916, and 0.2127. The prediction performance was better than that of the particle swarm optimization–back propagation (PSO-BP), artificial bee colony optimization–support vector machines (ABC-SVM), GRU, long short-term memory network (LSTM), and CNN-LSTM models. The method improves the utilization rate of dam safety monitoring results and has engineering utility for safe dam operations.

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À priori uplift pressure model for concrete dam foundations based on piezometric monitoring data

Cited by 3 publications

References 7 publications

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Deduction of ultimate equilibrium limit states for concrete gravity dams keyed into rock mass foundations based on large displacement analysis

Research on the Uplift Pressure Prediction of Concrete Dams Based on the CNN-GRU Model

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