Modeling dam deformation using independent component regression method
References (16)
- et al.
Diagnostic analysis of concrete dams based on seasonal hydrostatic loading [J]
Engineering Structures
(2008) - et al.
Use of deformation monitoring results in solving geomechanical problems-case studies [J]
Engineering Geology
(2005) - et al.
Application of an artificial immune algorithm on a statistical model of dam displacement [J]
Computers and Mathematics with Applications
(2011) - et al.
Dam displacement model based on principal component regression [J]
Hydropower Automation and Dam Monitoring
(2008) - et al.
Complex process monitoring using modified partial least squares method of independent component regression [J]
Chemometrics and Intelligent Laboratory Systems
(2009) - et al.
Independent component analysis: Algorithms and applications [J]
Neural Networks
(2000) - et al.
A new regression method based on independent component analysis [J]
Talanta
(2006) - et al.
Analysis and application of automatic deformation monitoring data for buildings and structures of mining area [J]
Transactions of Nonferrous Metals Society of China
(2011)
Cited by (15)
A critical review of statistical model of dam monitoring data
2023, Journal of Building EngineeringA Bayesian approach to model selection and averaging of hydrostatic-season-temperature-time model
2021, StructuresCitation Excerpt :Using advanced data analytics and various machine learning method, such as neural networks [3], support vector regression [14–16], Gaussian process regression [17], and deep learning method [18] algorithms were developed that can be implemented in real-time. Among other studies [19] using principal component analysis, [20,21] using artificial neural networks, [22] using blind source separation, [23] using artificial immune algorithm, [24] using independent component regression, [25] using deformation separation method, [26] using short term memory model, and [27] using Bayesian dynamic forecasting is worth mentioning here. Most of the previous studies assumed that the what variables to be included in model are known apriori.
Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation
2019, Engineering StructuresCitation Excerpt :Statistical models based on multiple linear regression and its advanced forms have proven to be more or less successful in dam behavior modeling [5]. The regression models adopted include multiple linear regression (MLR) [3], stepwise regression (SR) [9,10], regression based on principal component analysis [11,12], independent component regression [13] and partial least-squares regression [14], etc. The advantages of MLR models are simplicity of formulation and speed of execution.
Extracting seasonal deformations of the Nepal Himalaya region from vertical GPS position time series using Independent Component Analysis
2017, Advances in Space ResearchCitation Excerpt :Independent Component Analysis (ICA) is the most widely used BSS method and separates statistically independent signals based on the analysis of the higher order statistical moments of the sources (Comon, 1994). It has been effectively used in ground deformation analysis (Bottiglieri et al., 2007) and dam deformation monitoring (Dai et al., 2013, 2014). Liu et al. (2015) proposed a spatio-temporal filtering method that uses ICA to extract some ‘meaningful’ independent series from GPS networks.
A multi-variable grey model with a self-memory component and its application on engineering prediction
2015, Engineering Applications of Artificial IntelligenceCitation Excerpt :Moreover, accurate estimation and effective control of engineering settlement deformation are key to the success of engineering construction. Existing methods for predicting settlement deformation usually adopt the time series analysis model by utilizing monitoring data (Zhou et al., 2010; Xu et al., 2012; Dai et al., 2013), which have effectively promoted the development of the settlement deformation prediction theory and its practice. However, since engineering construction is a complicated nonlinear dynamical system with multiple influencing factors, the issue of settlement deformation involves not only time influence but also spatial effect.
Foundation item: Project (41074004) supported by the National Natural Science Foundation of China; Project (2013CB733303) supported by the National Basic Research Program of China