Zoned safety monitoring model for uplift pressures of concrete dams

Hu, Jiang; Ma, Feifei

doi:10.1177/0142331219842281

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…Generally speaking, separation of effects models have been applied in displacements analysis, with relatively simple formulation, validated by experience. Nevertheless, based on the successful application of these models, throughout the years, their variations have become more complex [15][16][17][18][19][20][21][22][23] and involving other physical quantities [17,21,[24][25][26][27][28], as this work is an example.…”

Section: Hst Modelsmentioning

confidence: 99%

Cahora Bassa vibrations monitoring between 2010-2022. Analysis of frequencies using HST statistical models for effects separation

Oliveira,

Mendes,

Rodrigues

et al. 2024

J. Phys.: Conf. Ser.

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Cahora Bassa dam is a 170m-high arch dam located in western Mozambique. In operation for almost 50 years, since 1974, the dam is affected by a swelling process of small/medium amplitude. Aiming to control its structural health over time, in 2010 the dam was instrumented with a continuous vibrations monitoring system, designed for Seismic and Structural Health Monitoring (SSHM), which includes 10 uniaxial accelerometers in the crest gallery and 3 triaxial accelerometers in the dam-foundation interface. The latest results on the dynamic behaviour monitoring of the dam are presented, namely the evolution of the main natural frequencies over time and the corresponding mode shapes. Then, HST statistical models for separation of effects, considering the hydrostatic pressure (H), seasonal variations (S), and time (T) dependent factors, are used to analyse the evolution of the experimental natural frequencies over the last 12 years. The achieved results show that the dynamic behaviour of the dam is mostly influenced by the water level variations. However, a very small decreasing tendency of the natural frequency values was detected, which may indicate a slight decrease of the global stiffness of the dam, possibly due to the evolution of the concrete swelling pathological process.

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Section: Hst Modelsmentioning

confidence: 99%

Cahora Bassa vibrations monitoring between 2010-2022. Analysis of frequencies using HST statistical models for effects separation

Oliveira,

Mendes,

Rodrigues

et al. 2024

J. Phys.: Conf. Ser.

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“…Among them, 34 plumb meter deformation monitoring points distributed in 8 dam sections are selected as the study samples. Figure 8 shows the layout of the plumb meter monitoring points in the 1#, 5#, 9#, 11#, 13#, 16…”

Section: B Case Study 2 1) General Background Of a Concrete Arch Dam And Deformation Datamentioning

confidence: 99%

“…In the study of spatio-temporal clustering algorithms for dam monitoring data, Hu and Ma took advantage of segment representation of time-sequence data, similarity measurement and K-means algorithm to cluster the osmometer monitoring data in the dam foundation [16]. Chen et al applied the fuzzy C-means (FCM) algorithm to zone the time periods and then the spatial monitoring points [14].…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

Wang

Chen

et al. 2021

IEEE Access

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Deformation is the most intuitive indicator of the actual working status of a concrete dam. Zoning the variation regulation of dam deformation is one of the key parts of dam safety evaluation and risk assessment. However, the sample points reflecting deformation and variation characteristic information are non-uniformly distributed, thus it is difficult to cluster the data samples by traditional clustering methods. To solve this problem, a spatio-temporal zoning method of dam deformation considering non-uniform distribution of monitoring information is proposed. Firstly, the preprocessed deformation data are utilized to establish the similarity-distance zoning indicators using the absolute deformation, the deformation increase and the relative deformation increase respectively; then the deformation data are transferred into the Cartesian coordinate system, known as sample points. Secondly, utilize the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the points. The clustering parameters M and δ are determined by an optimization algorithm with an evaluation index as the objective function, then the sample points representing time sections or spatial monitoring points are clustered through dynamically updating the neighborhood radius value ε. Moreover, several artificial data sets are selected to demonstrate that the improved DBSCAN algorithm is with more obvious superiority in nonuniform clustering compared to traditional algorithms. Deformation data of an existing concrete dam are presented and discussed to validate the established zoning method.

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“…The dam health monitoring system for recording dam behaviors, including displacement, temperature, stress-strain, crack, leakage, uplift pressure, and so forth, is essential for safety evaluation and operational instruction of dams (Hu and Ma, 2019;Salazar et al, 2017). Among all recorded quantities, dam displacement is the most intuitive reflection of the integrity of concrete dams, and thus, the interpretations and predictions of dam displacements are necessary (Kao and Loh, 2013).…”

Section: Introductionmentioning

confidence: 99%

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

et al. 2021

Transactions of the Institute of Measurement and Control

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Displacement is the most intuitive reflection of the comprehensive behavior of concrete dams, especially the time effect displacement, which is a key index for the evaluation of the structural behavior and health status of a dam in long-term service. The main purpose of this paper is to establish a state space model for separating causal components from the measured dam displacement. This approach is conducted by initially proposing two equations, which are the state and observation equations, and model parameters are then optimized by the Kalman filter algorithm. The state equation is derived according to the creep deformation of dam concrete and foundation rock and is used to preliminarily predict the dam time effect displacement. Considering the generally recognized three components of dam displacement, the hydraulic-seasonal-time (HST) model is used to establish the observation equation, which is used to update the time effect displacement. The efficiency and rationality of the established state space model is verified by an engineering example. The results show that the hydraulic component separated by the state space model only contains the instantaneous elastic hydraulic deformation, while the hysteretic elastic hydraulic deformation is divided into the time effect component. The inverted elastic modulus of dam body concrete is an instantaneous value for the state space model but a comprehensive reflection of the instantaneous and hysteretic elastic deformation ability for the HST model, where the hysteretic elastic deformation is a part of the hydraulic component. For the Xiaowan arch dam, the inverted values are 42.9 and 36.7 GPa for the state space model and HST model, respectively. The proposed state space model is useful to improve the interpretation ability of the separated displacement components of concrete dams.

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Zoned safety monitoring model for uplift pressures of concrete dams

Cited by 10 publications

References 28 publications

Cahora Bassa vibrations monitoring between 2010-2022. Analysis of frequencies using HST statistical models for effects separation

Cahora Bassa vibrations monitoring between 2010-2022. Analysis of frequencies using HST statistical models for effects separation

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

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