Comparison of hierarchical clustering based deformation prediction models for high arch dams during the initial operation period

Purpose The purpose of this study is to develop and verify a methodology for a zoned deformation prediction model for super high arch dams, which is indeed a panel data-based regression model with the hierarchical clustering on principal components. Design/methodology/approach The hierarchical clustering method is used to highlight the main features of the time series. This method is used to select the typical points of the measured ambient and concrete temperatures as predictors and divide the deformation observation points into groups. Based on this, the panel data of each zone can be established, and its type can be judged using F and Hausman tests successively. Then hydrostatic–temperature–time–season models for zones can be constructed. Through the comparative analyses of the distributions and the fitted coefficients of these zones, the spatial deformation mechanism of a dam can be identified. A super high arch dam is taken as a case study. Findings According to the measured radial displacements during the initial operation period, the investigated pendulums are divided into four zones. After tests, fixed-effect regression models are established. The comparative analyses show that the dam deformation conforms to the natural condition. The factors such as the unstable temperature field and the nonlinear time-dependent effect have obvious effects on the dam deformation. The results show the efficiency of the proposed methodology in zoning and prediction modeling for deformation of super high arch dams and the potential to mining dam deformation mechanism. Originality/value A zoned deformation prediction model for super high arch dams is proposed where hierarchical clustering on principal component method and panel data model are combined.

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Safety Monitoring of High Arch Dams in Initial Operation Period Using Vector Error Correction Model

Rock Mechanics and Rock Engineering ◽

10.1007/s00603-017-1287-y ◽

2017 ◽

Vol 51 (8) ◽

pp. 2469-2481 ◽

Cited By ~ 5

Author(s):

Guohe Liang ◽

Yu Hu ◽

Qingbin Li

Keyword(s):

Error Correction ◽

Safety Monitoring ◽

Vector Error Correction Model ◽

Initial Operation ◽

Correction Model ◽

Vector Error Correction ◽

Arch Dams ◽

Vector Error ◽

Operation Period ◽

High Arch Dams

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A Deformation Prediction Model of High Arch Dams in the Initial Operation Period Based on PSR-SVM-IGWO

Mathematical Problems in Engineering ◽

10.1155/2021/8487997 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Mingjun Li ◽

Jiangyang Pan ◽

Yaolai Liu ◽

Hao Liu ◽

Junxing Wang ◽

...

Keyword(s):

Time Series ◽

Phase Space ◽

Entropy Method ◽

Arch Dam ◽

Support Vector ◽

Initial Operation ◽

High Arch Dam ◽

Deformation Prediction ◽

Arch Dams ◽

Operation Period

The deformation prediction of the dam in the initial stage of operation is very important for the safety of high dams. A hybrid model integrating chaos theory, support vector machine (SVM), and an improved Grey Wolf Optimization (IGWO) algorithm is developed for deformation prediction of dam in the initial operation period. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method, and the Kolmogorov entropy method. Secondly, the SVM-IGWO model based on phase space reconstruction (PSR) is established for deformation forecasting of the dam in the initial operation period. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM and the GWO algorithm is improved to realize the optimization of SVM parameters. Finally, take the actual monitoring displacement of Xiluodu super-high arch dam as an example. The engineering application example shows that, compared with the existing models, the prediction accuracy of the PSR-SVM-IGWO model established in this paper is improved.

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