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Aug 6, 2013
Interpreting the Dynamics of Embankment Dams through a Time-Series Analysis of Piezometer Data Using a Non-Parametric Spectral Estimation Method
Authors: In-Soo Jung [email protected], Mario Berges [email protected], James H. Garrett, Jr. [email protected], and Christopher J. Kelly [email protected]Author Affiliations
Publication: Computing in Civil Engineering (2013)
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.
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© 2013 American Society of Civil Engineers.
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Published online: Aug 6, 2013
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Carnegie Mellon University, Department of Civil and Environmental Engineering; 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
Carnegie Mellon University, Department of Civil and Environmental Engineering; 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
Carnegie Mellon University, Department of Civil and Environmental Engineering; 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
US Army Corps of Engineers, Risk Management Center; 1000 Liberty Avenue, Suite 1107, Pittsburgh, PA 15222. E-mail: [email protected]
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