Displacement monitoring model of concrete dams using the shape feature clustering‐based temperature principal component factor

Xu, Cong; Gu, Chongshi; Su, Huaizhi; Hu, Kun; Xiang, Qun

doi:10.1002/stc.2603

Cited by 28 publications

(21 citation statements)

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“…2. According to equation (17), the integrated SSI between the model-fitted displacement time series of the analyzed monitoring point and the measured displacement time series of all the other monitoring points in the same cluster can be obtained. To achieve the optimization objectives of the fitting MSE and SSI at the same time, the reciprocal value, RS = 1=SSI, is coupled with the fitting MSE to form the overall optimization objective function.…”

Section: Spatial Association-coupled Double Objective Optimization Fu...mentioning

confidence: 99%

“…The massive temperature observation data can also be extracted by principal component analysis (PCA), and only a small number of useful principal components are then used to establish the HT PCA T model. To further improve the accuracy of the thermal deformation effect, Wang et al 17 suggested conducting a shape feature-based spatial clustering for the measured dam temperature field, and principal components can then be extracted from each cluster.…”

Section: Introductionmentioning

confidence: 99%

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A spatial association-coupled double objective support vector machine prediction model for diagnosing the deformation behaviour of high arch dams

Liu

2021

Structural Health Monitoring

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Displacement is the most intuitive reflection of the structural behaviour of concrete dams, and it is of great significance to predict future displacement for dam health diagnosis. The mathematical models used to predict displacement are mostly established with the only regression objective of minimizing the mean square error between the measured and fitted displacements, whereas the spatial associations between the displacements of multiple monitoring points of an arch dam are ignored. To increase the prediction accuracy of machine learning technique-based mathematical models, a spatial association-coupled support vector machine model is proposed in this article to predict the displacement of high arch dams. This approach is conducted by performing an incremental distance-based spatial clustering for dam displacement field in the first step. The displacement spatial association is quantified by the integrated shape similarity index between the measured time series of multiple monitoring points and is then coupled with the fitting mean square error to optimize the parameters of the support vector machine model. A case study of an engineering example indicates that the prediction accuracy and generalization ability of the proposed double objective support vector machine model have been greatly improved compared to the traditional single objective support vector machine model. For the total 34 plumb line monitoring points on the dam body of the Jinping-I arch dam, when using the hydraulic, seasonal and time- and hydraulic, hysteretic, seasonal and time-based double objective support vector machine models, the prediction accuracy of 25 and 21 monitoring points increases with an average rate of 50.8% and 47.4%, and the degrees of overfitting are evenly reduced by 44.3% and 70.9%, respectively.

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Section: Spatial Association-coupled Double Objective Optimization Fu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A spatial association-coupled double objective support vector machine prediction model for diagnosing the deformation behaviour of high arch dams

Liu

2021

Structural Health Monitoring

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“…Hu et al [1] proposed the special statistical models for the displacements of high arch dams during their initial impoundment periods by improving estimations of the non-stationary thermal and the non-monotonic timedependent effects. Wang et al [19] presented a shape feature-based spatial clustering method for the dam temperature field, and established a displacement monitoring model of concrete dams using the shape feature clustering-based temperature principal component factor.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

Yang¹,

Han²,

Kuang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J 23 (on dam section 24 # ), J 33 (on dam section 4 # ), J 35 (on dam section 8 # ), J 37 (on dam section 12 # ), and J 39 (on dam section 15 # ) located on the crest of Wuqiangxi concrete gravity dam, the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models. A deep learning model is developed based on long short-term memory (LSTM) recurrent neural network. In the LSTM model, two LSTM layers are used, the rectified linear unit function is adopted as the activation function, the input sequence length is 20, and the random search is adopted. The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set, and the monitoring data from 2018 to 2020 are taken as the test set. From the results of case study, we can find that (1) the good fitting results can be obtained with the two statistical models; (2) the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors; (3) the prediction accuracy of the LSTM model increases with increasing the amount of training data. In the deformation prediction of concrete gravity dam, the LSTM model is suggested when there are sufficient training data, while the partial least squares regression method is suggested when the training data are insufficient.

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“…To improve the accuracy of the dam thermal deformation effect, some researchers have proposed replacing the periodic harmonic factor in the HST model with the measured dam temperatures, and the hydraulic, thermal, and time (HTT), HT PCA T (improved HTT model with temperature factors extracted by principal component analysis), HT S-PCA T (further improved HT PCA T model with the shape feature clustering-based temperature principal component factors), HST-grad, and HST-layer models have been proposed. [15][16][17][18] In addition, to interpret the observed nontypical deformation behavior of some concrete dams, new influencing factors have been introduced into the HST or HTT models, as in the case of the hydraulic, thermal, crack, and time (HTCT) model for the Chencun arch dam with horizontal cracks, 19 the hydraulic, hysteretic, seasonal, and time (HHST) model for interpreting the hysteretic hydraulic deformation of the Jinping-I arch dam, 20 and the hydraulic, seasonal, thermal, and time (HSTT) model for considering the influence of European heat waves in 2003. 21 These models were mainly established by multiple linear regression (MLR) and stepwise regression methods and had explicit mathematical expressions.…”

Section: Introductionmentioning

confidence: 99%

Mixed‐coefficient panel model for evaluating the overall deformation behavior of high arch dams using the spatial clustering

Liu

2021

Struct Control Health Monit

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The mathematical monitoring models that are currently used for the displacement of arch dams are mostly established for a single monitoring point, without considering the spatial association of the displacements at multiple monitoring points. The main purpose of this paper is to develop and verify a mixed-coefficient panel model for dam displacements of multiple monitoring points. To maintain the largest similarities of the deformation mechanism of different monitoring points represented by one panel model, the displacement monitoring points on dam body are first divided into several groups according to the similarity index of the incremental distance between their measured displacement time series. The mixed-coefficient panel models are then established for each clustered zone in which the adjustment coefficient of the FEM-calculated hydraulic component is determined to be identical for all the monitoring points in the same zone, whereas the regression coefficients of the temperature and time effect component are considered to be variable, in order to reflect the similarities and differences of deformation mechanism in different dam regions. Based on the hydraulic, hysteretic, seasonal, and time (HHST) causal factors, the proposed panel model is verified by the measured radial displacement field of the Jinping-I arch dam. The modeling results demonstrate that the proposed mixed-coefficient panel model can effectively represent the overall deformation behavior and structural integrity of a high arch dam, and it has good performance in the interpretation and prediction of the dam displacement and the inversion analysis of viscoelastic parameters of the dam body concrete. K E Y W O R D S displacement, high arch dams, mixed-coefficient panel model, overall deformation behavior, spatial clustering, zoned inversion analysis

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Displacement monitoring model of concrete dams using the shape feature clustering‐based temperature principal component factor

Cited by 28 publications

References 28 publications

A spatial association-coupled double objective support vector machine prediction model for diagnosing the deformation behaviour of high arch dams

A spatial association-coupled double objective support vector machine prediction model for diagnosing the deformation behaviour of high arch dams

Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

Mixed‐coefficient panel model for evaluating the overall deformation behavior of high arch dams using the spatial clustering

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