Multifractal scaling behavior analysis for existing dams

Su, Huaizhi; Wen, Zhiping; Wang, Feng; Wei, Bowen; Hu, Jiang

doi:10.1016/j.eswa.2013.02.033

Cited by 20 publications

(7 citation statements)

References 25 publications

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“…Chaos is a kind of irregular random behavior with the characteristics of initial value sensitivity and ergodicity. Related researches show that the chaos phenomenon exists both in dam deformation prototype observation data and residual of dam deformation monitoring model. In practical engineering, dam deformation is not only effected by the water pressure, temperature change, but also effected by many other internal and external environments, for example, uplift pressure, sediment pressure, seepage, structural damage, material aging, and monitoring error.…”

Section: Forecast Of Chaotic Characteristics Contained In Residual Sementioning

confidence: 99%

“…Together with the limitations by current monitoring technology and analysis theory, the existing forecast models consider the major influencing factors, for example, water pressure component, temperature component, and aging component, but not the chaotic components contained in residual sequence, which further limits the improvement of fitting precision. The existence of chaotic effect components in dam prototype observation data and fitting residual sequence has been effectively validated . Effectively increasing the information mining scale and considering the chaotic components contained in residual sequence will present great significance to improve the capability of forecast model.…”

Section: Introductionmentioning

confidence: 97%

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Modified hybrid forecast model considering chaotic residual errors for dam deformation

Wei

Yuan

et al. 2018

Struct Control Health Monit

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Summary Dam deformation is double effected by the internal and external environments, showing high nonlinear characteristics. Information mining of dam prototype observation data and effective components contained in the residual sequence is limited in conventional dam deformation forecast models. In order to fully explore the complex nonlinear relationship between dam deformation and environmental factors, modified hybrid forecast model considering chaotic residual errors is proposed on the combination of shuffled frog leapfrog algorithm and chaos theory in this study. Hybrid models are established on the basis of dam deformation prototype observation data, and modified hybrid forecast model is established by determining the optimal weight of each hybrid model with shuffled frog leapfrog algorithm. Considering the chaotic characteristics contained in the residual sequence of modified hybrid forecast model, residual sequence is analyzed and forecasted by chaos theory. By superimposing the residual forecast value with the forecast value of modified hybrid forecast model, modified hybrid forecast model considering chaotic residual errors is established. Example shows that, compared with conventional models, the proposed model is better in fitting precision and convergence speed, and forecast capability is significantly improved by considering the effective components contained in the residual sequence, which presents a new method of the deformation forecast for other hydraulic structures.

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Section: Forecast Of Chaotic Characteristics Contained In Residual Sementioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Wei

Yuan

et al. 2018

Struct Control Health Monit

Self Cite

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“…e fractal theory is an effective method to study nonlinear time series of data with the advantages of wide application range and strong robustness and has been applied to dam [9], slope [10], tunnel [11], mining [12], and other engineering deformation stability analysis. e fractal theory was used to describe the long-term behavior of dam structures through fractal exponents, and the diagnosis model and appropriate criterion for evolution characteristics of concrete dam crack are developed by combining the fractal, phase space reconstruction, and wavelet method [13,14]. Zhang et al [15] put forward a fault analysis method based on the fractal characteristics of acoustic emission signals and assign it as an index to evaluate the overall safety of the dam.…”

Section: Introductionmentioning

confidence: 99%

A Fractal Prediction Method for Safety Monitoring Deformation of Core Rockfill Dams

Peng

Chen

Zhou

et al. 2021

Mathematical Problems in Engineering

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Deformation mechanism in the core rockfill dams with heavy load and high-stress level is difficult to predict and control, which is one of the key problems to be solved in the dam operation safety management and control. Aiming at the large error problems obtained by the parameter-based functional models (regression model, grey theory model, etc.) in the deformation prediction of the core rockfill dams, a fractal prediction method and its technical process by combining the variable dimension fractal dimension and the "metabolism" of prediction data are proposed through analyzing the fractal adaptability and deformation characteristics of original monitoring data based on the resealed-range (R/S) method and fractal dimension theory. It effectively solves the error in the process of constant dimension fractal accumulation and transformation greatly in dam deformation prediction and provides a new way for dam safety monitoring deformation prediction and early warning. The trend analysis of deformation monitoring data of the Pubugou core rockfill dam and the deformation prediction show that the fractal prediction information of dam deformation has a good corresponding relationship with its physical causes, which is in line with the actual deformation trend and operation state of the dam. Compared with the traditional stepwise regression method, the prediction results obtained by the proposed method in this paper are of high accuracy, implying that the improved fractal prediction of dam deformation is effective and the Hurst fractal index is applicable in the evaluation of the dam deformation trend.

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“…The existence of chaos effect in dam prototype observation data has been effectively validated. 17,18 However, research on chaos effect contained in displacement fitting residual sequence remains in the exploratory stage. 19,20 Therefore, the precision of conventional displacement forecast model can be improved by effectively excavating chaotic components of the fitting residual sequence; accordingly, the forecast capability can be enhanced.…”

Section: Introductionmentioning

confidence: 99%

Combination forecast model for concrete dam displacement considering residual correction

Wei

Yuan

et al. 2017

Structural Health Monitoring

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In conventional dam displacement monitoring models, forecast precision is below the standard, the fitting residual sequence contains chaotic components, and information mining of dam prototype observation data is limited. In consideration of the chaotic characteristics of the fitting residual sequence in regression model, the multi-scale wavelet analysis is used to decompose and reconstruct the residual sequence in this study; back propagation neural network and autoregressive integrated moving average model are used to forecast the reconstructed residual sequence by identifying the high-frequency and low-frequency characteristics of signals. By superimposing the residual forecast value with the forecast value of regression model, the combination forecast model for concrete dam displacement considering residual correction is proposed. Examples show that, compared with conventional models, the proposed combination model is better in fitting precision and convergence speed. Forecast capability is significantly improved for dam displacement forecast when effective components contained in residual sequence are considered. A new method of displacement forecast for high slope and other hydraulic structures is presented.

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Multifractal scaling behavior analysis for existing dams

Cited by 20 publications

References 25 publications

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Modified hybrid forecast model considering chaotic residual errors for dam deformation

A Fractal Prediction Method for Safety Monitoring Deformation of Core Rockfill Dams

Combination forecast model for concrete dam displacement considering residual correction

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