The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Seepage monitoring and diagnosis of distresses in an earth embankment dam using probability methods
Author:	Mousavian, Seyed Mohammad Reza
View Online:	njit-etd2017-092 (xv, 141 pages ~ 9.1 MB pdf)
Department:	Department of Civil and Environmental Engineering
Degree:	Doctor of Philosophy
Program:	Civil Engineering
Document Type:	Dissertation
Advisory Committee:	Karaa, Fadi A. (Committee chair) Marhaba, Taha F. (Committee member) Dresnack, Robert (Committee member) Konon, Walter (Committee member) Adams, Matthew P. (Committee member) Golon, Frank L. (Committee member)
Date:	2017-08
Keywords:	Dam safety Risk management Geophysical monitoring methods Statistical analysis Infrastructure safety monitoring Dam breach analysis
Availability:	Unrestricted
Abstract:	Failure of embankment dams may result in catastrophic consequences. Considering seepage and internal erosion are accounted as one of the major causes of failure in earth embankment dams, it is essential to detect any concentrated seepage and sources of distress at early stages. A number of investigation and monitoring methods exist for the detection of seepage, with varying degrees of technological and implementation complexity. This research, focuses on the Electrical Resistivity Monitoring Method (ERM), and develops a condition assessment process that allows 1) the identification of potential seepage areas and progress through visual observation and flow measurement, and 2) the determination of the most likely paths where piping may have occurred. In particular, two separate statistical studies are carried out to identify the existence of and quantify the probability of potential seepage sources in earth embankment dams. The testing and evaluation of the accuracy and reliability of the ERM method in seepage detection in earthen hydraulic structures is also undertaken as a result of the correlation of the field measurements of flow rates and ERM outputs. An earth dam suffering from seepage is studied and monitored visually and with the ERM to discover and locate the potential sources and paths of seepages, detected and observed at the downstream toe over time. A Bayesian network model is developed to evaluate the potential sources and related paths associated with the detected flows downstream. The model is completed by developing an approach to estimate the rate of erosion and predict the potential failure time of the dam with empirical and theoretical methods.