scholarly journals A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185177-185186
Author(s):  
Dashan Yang ◽  
Chongshi Gu ◽  
Yantao Zhu ◽  
Bo Dai ◽  
Kang Zhang ◽  
...  
Keyword(s):  
Prediction Method ◽  
Attention Mechanism ◽  
Deformation Prediction ◽  
Concrete Dam ◽  
Dam Deformation

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Pei ◽  
Jiankang Chen ◽  
Jingren Zhou ◽  
Huibao Huang ◽  
Zhengjun Zhou ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Prediction Method ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dimension Theory ◽  
Deformation Prediction ◽  
The Core ◽  
Dam Deformation ◽  
And Control

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.

scholarly journals A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xudong Qu ◽  
Jie Yang ◽  
Meng Chang
Keyword(s):  
Prediction Model ◽  
Evaluation System ◽  
Prediction Models ◽  
Evaluation Index ◽  
Accuracy Evaluation ◽  
Engineering Project ◽  
Deformation Prediction ◽  
Concrete Dam ◽  
Evaluation Indexes ◽  
Dam Deformation

Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies. This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models. Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied. Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes. An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect. The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models. The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.

scholarly journals Ensemble of bagged regression trees for concrete dam deformation predicting

2019 ◽  
Vol 376 ◽  
pp. 012040 ◽  
Author(s):  
Jiemin Xie ◽  
Jun Zhang ◽  
Xuan Xie ◽  
Zhiwei Bi ◽  
Zhuoheng Li
Keyword(s):  
Regression Trees ◽  
Concrete Dam ◽  
Dam Deformation

scholarly journals An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yijun Chen ◽  
Chongshi Gu ◽  
Chenfei Shao ◽  
Hao Gu ◽  
Dongjian Zheng ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Deformation Prediction ◽  
Ant Lion Optimizer ◽  
Proposed Model ◽  
Vector Machines ◽  
Ant Lion ◽  
Dam Deformation

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

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