Multivariate statistical analysis of monitoring data for concrete dams

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. Advances in automated data monitoring system makes it possible to regularly collect data on a large number of instruments for a dam. 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.Statistical models such as multiple linear regression, and back propagation neural networks have been used to estimate the response of individual instruments. Multiple linear regression models are of two kinds, (1) Hydro-Seasonal-Time (HST) models and (2) models that consider concrete temperatures as predictors.Univerariate, bivariate, and multivariate methods are proposed for the identification of anomalies in the instrumentation data. The source of these anomalies can be either bad readings, faulty instruments, or changes in dam behavior.The proposed methodologies are applied to three different dams, Idukki, Daniel Johnson and Chute-a-Caron, which are respectively an arch, multiple arch and a gravity dam. Displacements, strains, flow rates, and crack openings of these three dams are analyzed.This research also proposes various multivariate statistical analyses and artificial neural networks techniques to analyze dam monitoring data. One of these methods, Principal Component Analysis (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. Other multivariate analysis methods such as canonical correlation analysis, partial least squares and nonlinear principal component analysis are discussed. The advantages of methodologies for noise reduction, the reduction of number of variables that have to be monitored, the prediction of response parameters, and the identification of faulty readings are discussed. Results indicated that dam responses are generally correlated and that only a few principal components can summarize the behavior of a dam