Singular value diagnosis in dam safety monitoring effect values

Gu, Chongshi; Zhao, Erfeng; Jin, Yi; Su, Huaizhi

doi:10.1007/s11431-011-4339-7

Cited by 24 publications

(14 citation statements)

References 12 publications

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“…Dam displacement is taken as an example. For the dam displacement caused by the action of water load, temperature load, and other loads, it can be divided into water level component δ H , temperature component δ T and aging component δ θ , namely

δ = δ_{H} + δ_{T} + δ_{θ} .

…”

Section: Svm‐based Static Model Monitoring Dam Safetymentioning

confidence: 99%

Performance improvement method of support vector machine-based model monitoring dam safety

Chen²,

Wen

2015

Struct. Control Health Monit.

Self Cite

130

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Summary Under the comprehensive influence of material and loads, dam structural behavior presents the time‐varying nonlinear characteristics. To forecast the dam structural behavior (displacement, stress, seepage, etc.), the models monitoring dam safety are often built according to the prototype observations on dam safety. However, the modeling process is usually fulfilled with the offline and static pattern. As time goes on, the fitting and forecasting ability of built static model will decline gradually. The article is focused on the support vector machine (SVM)‐based model monitoring dam safety. The methods are studied to advance the adaptability of SVM model and reduce the modeling time. By implementing the impact analysis for SVM parameters and input vector, the optimization method of SVM parameters and input vector is presented to enhance the efficiency of building the SVM‐based static model monitoring dam safety. To describe dynamically the time‐varying mapping relationship between dam structural behavior (effect‐quantity) and its cause (influence‐quantity), the way is developed to update in real time above model by making the most use of new observations. The displacement of one actual dam is taken as an example to verify the modeling efficiency and forecasting ability. Copyright © 2015 John Wiley & Sons, Ltd.

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δ = δ_{H} + δ_{T} + δ_{θ} .

…”

Section: Svm‐based Static Model Monitoring Dam Safetymentioning

confidence: 99%

Performance improvement method of support vector machine-based model monitoring dam safety

Chen²,

Wen

2015

Struct. Control Health Monit.

Self Cite

130

View full text Add to dashboard Cite

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“…Principal component analysis has been applied in multivariate analysis, structural health analysis, modal analysis, parameter identification, and structural damage detection . In this work, it will be shown that this technique can be very useful in the selection of the thermometers that best represent the thermal effect in the statistical model for prediction of horizontal displacements in concrete dams.…”

Section: Principal Component Analysis: An Overviewmentioning

confidence: 99%

Constructing statistical models for arch dam deformation

Mata

Castro

Costa

2013

Struct. Control Health Monit.

186

139

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In its lifetime, a dam can be exposed to significant water level variations and seasonal environmental temperature changes. The structural safety control of a concrete dam is supported by monitoring activities and is based on models.In practice, the interpretation of recorded concrete dam displacements is usually based on HST (hydrostatic, seasonal, time) statistical models. These models are widely used and consider that the thermal effect can be represented by a seasonal function. The main purpose of this paper is to present an HTT (hydrostatic, thermal, time) statistical model to interpret recorded concrete dam displacements. The idea is to replace the seasonal function with the use of recorded temperatures that better represent the thermal effect on dam behavior. Two new methodologies are presented for constructing HTT statistical models, both based on principal component analysis applied to recorded temperatures in the concrete dam body. In the first method, principal component analysis is used to choose the thermometers for the construction of the HTT model. In the second method, the thermal effect is represented by the principal components of temperature of selected thermometers.The advantage of these methods is that the thermal effect is represented by real temperature measured in the concrete dam body. The HTT statistical models proposed are applied to the 110 m high Alto Lindoso arch dam, and the results are compared with the HST displacement model.

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“…Some research work has demonstrated that the prototype monitoring data series on dam behavior has chaotic characteristics. 9,10 The unordered chaotic time series presents a certain regularity, which is sensitive to initial value. It is difficult to forecast the long-term behavior according to chaotic time series.…”

Section: Introductionmentioning

confidence: 99%

Dam safety prediction model considering chaotic characteristics in prototype monitoring data series

Wen

Chen

et al. 2016

Structural Health Monitoring

Self Cite

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Support vector machine, chaos theory, and particle swarm optimization are combined to build the prediction model of dam safety. The approaches are proposed to optimize the input and parameter of prediction model. First, the phase space reconstruction of prototype monitoring data series on dam behavior is implemented. The method identifying chaotic characteristics in monitoring data series is presented. Second, support vector machine is adopted to build the prediction model of dam safety. The characteristic vector of historical monitoring data, which is taken as support vector machine input, is extracted by phase space reconstruction. The chaotic particle swarm optimization algorithm is introduced to determine support vector machine parameters. A chaotic support vector machine-based prediction model of dam safety is built. Finally, the displacement behavior of one actual dam is taken as an example. The prediction capability on the built prediction model of dam displacement is evaluated. It is indicated that the proposed chaotic support vector machine-based model can provide more accurate forecasted results and is more suitable to be used to identify efficiently the dam behavior.

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Singular value diagnosis in dam safety monitoring effect values

Cited by 24 publications

References 12 publications

Performance improvement method of support vector machine-based model monitoring dam safety

Performance improvement method of support vector machine-based model monitoring dam safety

Constructing statistical models for arch dam deformation

Dam safety prediction model considering chaotic characteristics in prototype monitoring data series

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