Application of multivariate statistical methods to monitoring data, case study of a gravity dam. Ahmadi Nedushan, B. & Chouinard, L. In volume 2003, pages 1006 - 1015, Moncton, NB, Canada, 2003. Crack movements;Data interpretation;Multivariate statistical methods;Reservoir management;
abstract   bibtex   
Major dams in the world are often instrumented in order to validate numerical models, to gain insight into the behavior of the dam, to detect anomalies, and to enable a timely response either in the form of repairs, reservoir management, or evacuation. It is possible to regularly collect data on a large number of instruments for a dam due to advances in automated data monitoring system. Managing this data is a major concern since traditional means of monitoring each instrument are time consuming and personnel intensive. Among tasks that need to be performed are: identification of faulty instruments, removal of outliers, data interpretation, model fitting and management of alarms for detecting statistically significant changes in the response of a dam. This article proposes Principal Component Analysis (PCA), a multivariate statistical method, to analyze dam monitoring data. PCA is concerned with explaining the variance-covariance structure of a data set through a few linear combinations of the original variables. The general objectives are (1) data reduction and (2) data interpretation. The proposed methodology is applied to monitoring data for a concrete gravity dam.The simultaneous analysis of instrumentation data was performed using principal component analysis on instrumentation data for a concrete gravity dam. Displacements, flow rates, and crack movements are simultaneously analyzed. The advantages of the methodology for noise reduction and the reduction of number of variables that have to be monitored are discussed.
@inproceedings{2006169828704 ,
language = {English},
copyright = {Compilation and indexing terms, Copyright 2023 Elsevier Inc.},
copyright = {Compendex},
title = {Application of multivariate statistical methods to monitoring data, case study of a gravity dam},
journal = {Proceedings, Annual Conference - Canadian Society for Civil Engineering},
author = {Ahmadi Nedushan, B. and Chouinard, L.E.},
volume = {2003},
year = {2003},
pages = {1006 - 1015},
address = {Moncton, NB, Canada},
abstract = {Major dams in the world are often instrumented in order to validate numerical models, to gain insight into the behavior of the dam, to detect anomalies, and to enable a timely response either in the form of repairs, reservoir management, or evacuation. It is possible to regularly collect data on a large number of instruments for a dam due to advances in automated data monitoring system. Managing this data is a major concern since traditional means of monitoring each instrument are time consuming and personnel intensive. Among tasks that need to be performed are: identification of faulty instruments, removal of outliers, data interpretation, model fitting and management of alarms for detecting statistically significant changes in the response of a dam. This article proposes Principal Component Analysis (PCA), a multivariate statistical method, to analyze dam monitoring data. PCA is concerned with explaining the variance-covariance structure of a data set through a few linear combinations of the original variables. The general objectives are (1) data reduction and (2) data interpretation. The proposed methodology is applied to monitoring data for a concrete gravity dam.The simultaneous analysis of instrumentation data was performed using principal component analysis on instrumentation data for a concrete gravity dam. Displacements, flow rates, and crack movements are simultaneously analyzed. The advantages of the methodology for noise reduction and the reduction of number of variables that have to be monitored are discussed.},
key = {Gravity dams},
keywords = {Data processing;Data reduction;Information management;Noise abatement;Personnel;Principal component analysis;Reservoirs (water);Statistical methods;Watersheds;},
note = {Crack movements;Data interpretation;Multivariate statistical methods;Reservoir management;},
}

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