Interpreting the Dynamics of Embankment Dams through a Time-Series Analysis of Multiple Piezometer Data Using a Non-Parametric Spectral Estimation Method

Abstract

A common approach used by engineers to monitor and analyze data collected from piezometers installed in embankment dams is to generate time history plots and visually identify any spikes or anomalies in them. However, such practice has several limitations when capturing complicated relationships among a number of factors that affect piezometric readings. This is especially true when periodic or dominant variations that exist in time-series data are of concern, given that environmental and process noise can sometimes mask these variations. In this paper, we propose applying Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA), which have been shown to be successful in other applications, to extract relevant components and detect anomalies in piezometer measurements, which are one of the most important data to be monitored when evaluating the performance of embankment dams. The proposed anomaly detection method provides a more efficient way of understanding and detecting changes in piezometer data.