Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis

Chen, Siyu; Gu, Chongshi; Lin, Chaoning; Zhao, Erfeng; Song, Jianshe

doi:10.1155/2018/1712653

Cited by 32 publications

(20 citation statements)

References 22 publications

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“…Unfortunately, the time‐dependent deformation is unable to be measured directly and can only be extracted from the measured data. Although sometimes this deformation can be negligible for operation periods, 11 it is an important predictor for initial impoundment periods 15–17 . All published mathematical expressions of the time‐dependent deformation are monotonically increasing functions with time 3,6,11,12 .…”

Section: Introductionmentioning

confidence: 99%

“…Although sometimes this deformation can be negligible for operation periods, 11 it is an important predictor for initial impoundment periods. [15][16][17] All published mathematical expressions of the time-dependent deformation are monotonically increasing functions with time. 3,6,11,12 However, in accordance with the detailed studies, this deformation does not always conform to the assumption of the monotonically increasing functions.…”

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confidence: 99%

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Statistical modelling for high arch dam deformation during the initial impoundment period

2020

Struct Control Health Monit

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Although statistical models are efficient in most cases to analyze concrete dam displacements, these models are built on several hypotheses, leading to uncertainties especially for special periods. The special statistical models, improving estimations of the non-stationary thermal and the non-monotonic timedependent effects, are proposed for the displacements of high arch dams during their initial impoundment periods in this paper. The hierarchical clustering on principal component analysis is developed to divide thermometers into groups and to choose representative thermometers or identify major principal components on measured temperature data to represent the non-stationary thermal effect on dam's displacements. The non-monotonic formula for the time-dependent deformation, emphasizing the creep and its restoration of dam concrete and its surrounding rock, is derived and further simplified when the reservoir water level exhibits an evident periodicity. Then, two improved statistical models accounting for these non-stationary thermal and non-monotonic time-dependent effects are proposed. The proposed statistical models with clear physical meanings are applied to investigate the measured displacements of the Xiluodu arch dam. Model performance comparisons indicate that the proposed models have significant improvement in fitting precision and prediction ability over the traditional and more recent models. Model results confirm the influence of reservoir thermal stratification and concrete temperature rise on the thermal displacement, and the non-monotonic effect on the timedependent displacement. The proposed models yield to a better identification of the deformation mechanism for high arch dams during their initial impoundment periods. K E Y W O R D S clustering, dam, initial impoundment period, principal component analysis, structural health monitoring, time-dependent effect 1 | INTRODUCTION A number of super high arch dams of 200-300 m high have been built in China. Many of these dams have entered or will enter their initial impoundment periods. 1 Super high dams have strong interactions with air and water temperature, reservoir water level, uplift, rock deformability, and so on. During the initial impoundment period, a super high

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Statistical modelling for high arch dam deformation during the initial impoundment period

2020

Struct Control Health Monit

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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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“…Generally, the prediction models are mainly divided into point prediction and interval prediction according to different prediction forms. These point prediction methods have been applied in the field of temperature prediction, including back-propagation neural network [19,20], artificial neural network [21], gray model [22], genetic algorithm [23,24], and support vector machine [25,26]. However, point prediction only provides one prediction result at each target point, which lacks uncertainty estimation [27,28].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

et al. 2020

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Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

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Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis

Cited by 32 publications

References 22 publications

Statistical modelling for high arch dam deformation during the initial impoundment period

Statistical modelling for high arch dam deformation during the initial impoundment period

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

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

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