Displacement monitoring model of concrete dams using the shape feature clustering‐based temperature principal component factor

2020 ◽  
Vol 27 (10) ◽  
Author(s):  
Shaowei Wang ◽  
Cong Xu ◽  
Chongshi Gu ◽  
Huaizhi Su ◽  
Kun Hu ◽  
...  
Keyword(s):  
Principal Component ◽  
Shape Feature ◽  
Concrete Dams ◽  
Displacement Monitoring ◽  
Feature Clustering ◽  
Monitoring Model ◽  
Component Factor

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.

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.

Neuropsychological asymmetry in Alzheimer's disease: Verbal versus visuoconstructional deficits across stages of dementia

1997 ◽  
Vol 3 (5) ◽  
pp. 420-427 ◽  
Author(s):  
DANIEL STRITE ◽  
PAUL J. MASSMAN ◽  
NORMA COOKE ◽  
RACHELLE S. DOODY
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Factor Analysis ◽  
Principal Component ◽  
Neuropsychological Deficits ◽  
Principal Component Factor Analysis ◽  
Factor Scores ◽  
Lateral Asymmetry ◽  
Mild Dementia ◽  
Component Factor

The incidence of clinically apparent asymmetric profiles of neuropsychological deficits in Alzheimer's disease (AD) patients similar to those reported in the PET literature is currently unclear. This study investigated lateral neuropsychological asymmetry using principal component factor analysis in a sample of 153 patients diagnosed with probable AD. Using factor scores, patients were classified into groups exhibiting asymmetric or symmetric profiles of neuropsychological deficits. In the analysis of lateral asymmetry, 27.5% of patients were classified as asymmetric (10% verbally and 17% visuospatially). Consistent with reports of continued asymmetry beyond the mild dementia stage, asymmetry was exhibited in the mild, moderate, and severely demented groups. These findings of neuropsychological asymmetry across stages of dementia are consistent with the picture of significant neuropsychological heterogeneity in AD that has been emerging in the decade. (JINS, 1997, 3, 420–427.)

scholarly journals Process Monitoring and Fault Diagnosis for Piercing Production of Seamless Tube

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Dong Xiao ◽  
Jinhong Jiang ◽  
Yachun Mao ◽  
Xiaobo Liu
Keyword(s):  
Fault Diagnosis ◽  
Process Monitoring ◽  
Principal Component ◽  
Seamless Tube ◽  
Hollow Shell ◽  
Mechanism Model ◽  
Monitoring Model ◽  
Intelligent Model ◽  
Multiway Principal Component Analysis

With the development of modernization, the application of seamless tube becomes widespread. As the first process of seamless tube, piercing is vital for the quality of the tube. The solid round billet will be transformed into a hollow shell after the piercing process. The defects of hollow shell cannot be cleared in the following process, so a monitoring model for the quality of the hollow shell is important. But the piercing process is very complicated, and a mechanism model is difficult to build between the qualities of the hollow shell and measurement variables. Furthermore, an intelligent model is needed. We established two piercing process monitoring and fault diagnosis models based on the multiway principal component analysis (MPCA) model and the multistage MPCA model, respectively, and furthermore we made a comparison between these two concepts. We took three ways to divide the period based on process,K-means, and GA, respectively. Simulation experiments have shown that the multistate MPCA method has advantage over the MPCA method and the model based on the genetic algorithm (GA) can monitor the process effectively and detect the faults.

The Gilles de la Tourette Syndrome: a principal component factor analytic study of a large pedigree

2007 ◽  
Vol 17 (3) ◽  
pp. 143-152 ◽  
Author(s):  
Mary M. Robertson ◽  
Andrea E. Cavanna
Keyword(s):  
Tourette Syndrome ◽  
Principal Component ◽  
Analytic Study ◽  
Large Pedigree ◽  
Factor Analytic ◽  
Factor Analytic Study ◽  
Component Factor

Ecological Orientation of Tourists: An Empirical Investigation

2007 ◽  
Vol 7 (3-4) ◽  
pp. 161-175 ◽  
Author(s):  
Natan Uriely ◽  
Arie Reichel ◽  
Amir Shani
Keyword(s):  
Factor Analysis ◽  
Empirical Investigation ◽  
Principal Component ◽  
Successful Implementation ◽  
Principal Component Factor Analysis ◽  
Ecological Requirements ◽  
Ecological Features ◽  
Component Factor ◽  
Ecological Values ◽  
Strong Orientation

This study presents a tourist ecological orientation (TEO) construct based on the responses of participants who were asked to state the importance of various ecological features of planned tourist sites. The higher the TEO score, the more sensitive the person's attitudes toward the ecological requirements and responsibilities of tourist sites. A principal component factor analysis reveals two alternative dimensions of the TEO concept: ‘destination oriented’ and ‘visitor oriented’. The higher score that was given to the latter dimension supports the argument that successful implementation of ecological values at tourist sites requires a strong orientation towards consumer needs in addition to an ideological commitment to the environment. In addition, differences in ecological orientation patterns and structure were analysed along age and nationality.

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