Major dams in the world are often instrumented in order to validate numerical models, to gaininsight into the behavior of the dam, to detect anomalies, and to enable a timely response either in theform of repairs, reservoir management, or evacuation. It is possible to regularly collect data on a largenumber of instruments for a dam due to advances in automated data monitoring system. Managing thisdata is a major concern since traditional means of monitoring each instrument are time consuming andpersonnel 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 statisticallysignificant changes in the response of a dam.This article proposes Principal Component Analysis (PCA), a multivariate statistical method, to analyzedam monitoring data. PCA is concerned with explaining the variance-covariance structure of a data setthrough a few linear combinations of the original variables. The general objectives are (1) data reductionand (2) data interpretation. The proposed methodology is applied to monitoring data for a concrete gravitydam.The simultaneous analysis of instrumentation data was performed using principal componentanalysis on instrumentation data for a concrete gravity dam. Displacements, flow rates, and crackmovements are simultaneously analyzed. The advantages of the methodology for noise reduction and thereduction of number of variables that have to be monitored are discussed.
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