Wavelet-fractal diagnosis model and its criterion for concrete dam crack status

Su, Huaizhi; Chen, Jian; Wen, Zhiping; Wang, Feng

doi:10.1177/0142331217693076

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…To solve this problem, machine learning algorithms, such as support vector machine (SVM) (Su et al, 2015), extreme learning machine (ELM) (Kang et al, 2017), neural network (NN) (Mata, 2011), long short-term memory (LSTM) network (Liu et al, 2020), random forests (RF) (Dai et al, 2019), boosted regression tree (Salazar et al, 2016), and relevance vector machine (RVM) (Chen et al, 2020), have been shown to possess strong data mining abilities aimed at nonlinear implicit relations and have been employed to establish monitoring models. Meanwhile, based on the multi-scale characteristics, the long term measured displacement time series can be separated in the frequency domain into several components by the wavelet decomposition and empirical mode decomposition, and so forth, and according to the same frequency of effect component and its influencing factor, these components can be distinguished, by which the tendency component is usually defined as the time effect displacement (Correˆa et al, 2016;Fu et al, 2019;Su et al, 2018;Wang et al, 2018). The time effect displacement separated by these methods only contains its irreversible component.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

et al. 2021

Transactions of the Institute of Measurement and Control

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Displacement is the most intuitive reflection of the comprehensive behavior of concrete dams, especially the time effect displacement, which is a key index for the evaluation of the structural behavior and health status of a dam in long-term service. The main purpose of this paper is to establish a state space model for separating causal components from the measured dam displacement. This approach is conducted by initially proposing two equations, which are the state and observation equations, and model parameters are then optimized by the Kalman filter algorithm. The state equation is derived according to the creep deformation of dam concrete and foundation rock and is used to preliminarily predict the dam time effect displacement. Considering the generally recognized three components of dam displacement, the hydraulic-seasonal-time (HST) model is used to establish the observation equation, which is used to update the time effect displacement. The efficiency and rationality of the established state space model is verified by an engineering example. The results show that the hydraulic component separated by the state space model only contains the instantaneous elastic hydraulic deformation, while the hysteretic elastic hydraulic deformation is divided into the time effect component. The inverted elastic modulus of dam body concrete is an instantaneous value for the state space model but a comprehensive reflection of the instantaneous and hysteretic elastic deformation ability for the HST model, where the hysteretic elastic deformation is a part of the hydraulic component. For the Xiaowan arch dam, the inverted values are 42.9 and 36.7 GPa for the state space model and HST model, respectively. The proposed state space model is useful to improve the interpretation ability of the separated displacement components of concrete dams.

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Section: Introductionmentioning

confidence: 99%

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

et al. 2021

Transactions of the Institute of Measurement and Control

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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 changing status of joint cracks is one of the main concerns in the health evaluation of concrete structures, especially for concrete dams. The abnormality of joint cracks will weaken the strength and rigidity, destroy the integrity and impermeability, accelerate concrete corrosion and carbonization, and endanger safe operating condition of the dam 1–4 . According to an investigative report issued by the International Committee on Large Dams, a total of 243 dams have collapsed as a result of cracking problems 5,6 .…”

Section: Introductionmentioning

confidence: 99%

Spatio‐temporal data mining method for joint cracks in concrete dam based on association rules

Huang

Liu

et al. 2021

Structural Contr & Hlth

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Structural health monitoring (SHM) has been widely employed to reveal the hidden safety information and to diagnose the safety status in dam engineering fields. As one of the most important parameters of SHM, crack opening displacement (COD) is often used to evaluate the cracks or joints of concrete dams. In this paper, a new dam health analytic perspective is introduced by integrating the data mining method into SHM field, focusing on revealing the association rules in COD monitoring data. The association rules are investigated systematically, considering the cause-effect relations between external loads and structural response, the temporal characteristics of time series for a single sensor, the spatial characteristics of monitoring data for multisensors, and the abnormal characteristics for different items of structural responses. The association relation is quantified by proposing the quantitative indexes, including support degree, confidence degree, and promotion degree. The methods are used in the COD monitoring data of the Baishan concrete gravity-arch dam, which is located in a severely cold area in northeastern China. Results show that 4 out of 24 cause-effect association rules are extracted by calculating the association degree of monitored COD values, and 21 out of 24 crack sensors present a temporal association relationship, among which the confidence degree of two sensors reaches 100%. The variation trend of COD values is relevant with the locations of the crack sensors. These results are consistent with the dam safety monitoring theories and models, which would be very useful for extracting the SHM information between different sensors, predicting the trend of COD value and repairing the monitoring data series of COD sensors, or even for discovering an abnormal signal for the operation safety of dams.

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Wavelet-fractal diagnosis model and its criterion for concrete dam crack status

Cited by 10 publications

References 21 publications

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

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

Spatio‐temporal data mining method for joint cracks in concrete dam based on association rules

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