A deformation monitoring model for concrete dams based on PSO-ASIN

2010 ◽  
Author(s):  
Xiaoqing Gu ◽  
Zhenzhong Shen
Keyword(s):  
Deformation Monitoring ◽  
Concrete Dams ◽  
Monitoring Model

scholarly journals Crack-Considered Elastic Net Monitoring Model of Concrete Dam Displacement

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiingmei Zhang ◽  
Chongshi Gu
Keyword(s):  
Water Pressure ◽  
Elastic Net ◽  
Arch Dam ◽  
Time Dependent ◽  
Concrete Dams ◽  
Displacement Monitoring ◽  
Concrete Dam ◽  
Monitoring Model ◽  
Proposed Model ◽  
Dependent Component

Displacement monitoring data modeling is important for evaluating the performance and health conditions of concrete dams. Conventional displacement monitoring models of concrete dams decompose the total displacement into the water pressure component, temperature component, and time-dependent component. And the crack-induced displacement is generally incorporated into the time-dependent component, thus weakening the interpretability of the model. In the practical engineering modeling, some significant explaining variables are selected while the others are eliminated by applying commonly used regression methods which occasionally show instability. This paper proposes a crack-considered elastic net monitoring model of concrete dam displacement to improve the interpretability and stability. In this model, the mathematical expression of the crack-induced displacement component is derived through the analysis of large surface crack’s effect on the concrete dam displacement to improve the interpretability of the model. Moreover, the elastic net method with better stability is used to solve the crack-considered displacement monitoring model. Sequentially, the proposed model is applied to analyze the radial displacement of a gravity arch dam. The results demonstrate that the proposed model contributes to more reasonable explaining variables’ selection and better coefficients’ estimation and also indicate better interpretability and higher predictive precision.

Dam Deformation Prediction Based on EEMD-ELM Model

2020 ◽  
Author(s):  
Tao Yan ◽  
Bo Chen
Keyword(s):  
Training Sample ◽  
Ensemble Empirical Mode Decomposition ◽  
Deformation Monitoring ◽  
Concrete Gravity Dam ◽  
Deformation Prediction ◽  
Monitoring Model ◽  
Mode Decomposition ◽  
Dam Deformation ◽  
Learning Machine ◽  
Bpnn Model

<p>Establishing a reasonable and reliable dam deformation monitoring model is of great significance for effective analysis of dam deformation monitoring data and accurate assessment of dam working conditions. Firstly, the dam deformation is decomposed by the EEMD algorithm to obtain IMF components representing different characteristic scales, and different influencing factors are selected for different IMF components. Secondly, each IMF component is used as the ELM training sample to analyze, fit and predict the dam deformation component. Finally, the prediction results of each IMF component are accumulated to obtain the dam deformation prediction value. Taking a roller compacted concrete gravity dam as an example, the EEMD-ELM model is used to predict the deformation of the dam. At the same time, it is compared and analyzed with the prediction results of the BPNN model and the ELM model. The mean square error of the EMD-ELM model is 0.566, which is 54% and 14.8% lower than the BPNN model and the ELM model, indicating that the EEMD-ELM model has higher prediction accuracy and has certain application value.</p><p><strong>Key words:</strong> dam deformation;prediction model; ensemble empirical mode decomposition; extreme learning machine</p>

Abnormality monitoring model of cracks in concrete dams

2011 ◽  
Vol 54 (7) ◽  
pp. 1914-1922 ◽  
Author(s):  
TengFei Bao ◽  
Dong Qin ◽  
XiWu Zhou ◽  
GuiFen Wu
Keyword(s):  
Concrete Dams ◽  
Monitoring Model

Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

2019 ◽  
Vol 19 (4) ◽  
pp. 987-1002 ◽  
Author(s):  
Fei Kang ◽  
Xi Liu ◽  
Junjie Li
Keyword(s):  
Temperature Effect ◽  
Air Temperature ◽  
Health Monitoring ◽  
Structural Response ◽  
Concrete Dams ◽  
Extreme Learning Machines ◽  
Concrete Gravity Dam ◽  
Learning Machines ◽  
Time Deformation ◽  
Monitoring Model

Statistical models have been used for dam health monitoring for many years and have achieved some successful applications. In the statistical model, dam structural response is related to external environmental factors such as reservoir water level, temperature, and irreversible time deformation. For concrete dams, the structural response is affected greatly by the ambient temperature. Therefore, in order to establish a more reliable dam health monitoring model, the temperature effect and modeling method should be further studied. This article presents a dam health monitoring model using measured air temperature for temperature effect simulation based on kernel extreme learning machines. The temperature effect is simulated by long-term air temperature data, and the nonlinear relationship is modeled by kernel extreme learning machines, which is an intelligent machine learning technique with high learning speed and good generalization performance. The proposed dam health monitoring model is verified on a real concrete gravity dam with efficient safety monitoring data. Results show that the proposed approach with a variable set recommended for concrete dam behavior prediction is feasible.

scholarly journals Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Siyu Chen ◽  
Chongshi Gu ◽  
Chaoning Lin ◽  
Erfeng Zhao ◽  
Jintao Song
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Temperature Effect ◽  
Principal Component ◽  
Safety Monitoring ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Structural Safety ◽  
Concrete Dams ◽  
Monitoring Model

Effective deformation monitoring is vital for the structural safety of super-high concrete dams. The radial displacement of the dam body is an important index of dam deformation, which is mainly influenced by reservoir water level, temperature effect, and time effect. In general, the safety monitoring models of dams are built on the basis of statistical models. The temperature effect of dam safety monitoring models is interpreted using approximate functions or the temperature values of a few points of measurement. However, this technique confers difficulty in representing the nonlinear features of the temperature effect on super-high concrete dams. In this study, a safety monitoring model of super-high concrete dams is established through the radial basis neural network (RBF-NN) and kernel principal component analysis (KPCA). The RBF-NN with strong nonlinear fitting capacity is utilized as the framework of the model, and KPCA with different kernels is adopted to extract the temperature variables of the dam temperature dataset. The model is applied to a super-high arch dam in China, and results show that the Hybrid-KPCA -RBF-NN model has high fitting and prediction precision and thus has practical application value.

Dam Safety Analysis Based on Stepwise Regression Model

2011 ◽  
Vol 204-210 ◽  
pp. 2158-2161 ◽  
Author(s):  
De Xiu Hu ◽  
Zhi Qi Zhou ◽  
Yong Li ◽  
Xiao Long Wu
Keyword(s):  
Regression Model ◽  
Stepwise Regression ◽  
Least Squares Method ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Analysis Model ◽  
Monitoring Model ◽  
Rockfill Dam ◽  
Measured Deformation ◽  
Dam Deformation

The simulating and predicting analysis model is studied by the deformation monitoring data of Bikou earth-rockfill dam. Based on the least squares method of Statistics principles, the stepwise regression model has been established of earth-rockfill dam deformation displacement, which is used to fit and forecast the measured deformation data sequences of dam. The results shows that the deformation monitoring model of Bikou earth-rockfill dam having higher fitting precision, longer predict cycle, can be better applied to the fitting and prediction of dam deformation monitoring data.

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