Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled structures can be influenced by the presence of seepage. Thus, continuous temperature measurements can allow detection of seepage flows. With the recent advances in optical fiber temperature sensor technology, accurate and fast temperature measurements, with relatively high spatial resolution, have been made possible using optical fiber distributed temperature sensors (DTSs). As with any sensor system, to obtain a precise temperature, the DTS measurements need to be calibrated. DTS systems automatically calibrate the measurements using an internal thermometer and reference section. Additionally, manual calibration techniques have been developed which are discussed in this paper. The temperature data do not provide any direct information about the seepage, and this requires further processing and analysis. Several methods have been developed to interpret the temperature data for the localization of the seepage and in some cases to estimate the seepage quantity. An efficient DTS application in seepage monitoring strongly depends on the following factors: installation approach, calibration technique, along with temperature data interpretation and post-processing. This paper reviews the different techniques for calibration of DTS measurements as well as the methods of interpretation of the temperature data.

Seepage is the key factor in the safety of dikes and earth-fill dams. It is crucial to identify and localize the seepage excesses at the early stages before it initiates the internal erosion process in the structure. A proper seepage monitoring system should ensure a continuous and wide area seepage measurement. Here, continuous monitoring of seepage at the laboratory-scale is achieved by a passive optical fiber Distributed Temperature Sensing (DTS) system. An experimental model was designed which consists of initially unsaturated sand model, water supply, seepage outflow, optical fiber DTS system, and water and air temperature measurement. Initially, the sand temperature was higher than the temperature of the seepage water. An optical fiber DTS system was employed with a high-temperature resolution, short sampling intervals and short time intervals for temperature monitoring in the sand model. In the system, the small variation in the temperature due to groundwater flow was detected. The numerical analysis was conducted for both the seepage process and the heat transfer progression in the sand model. The results of the heat flow simulation were evaluated and compared with the measured temperature by the optical fiber DTS. Obvious temperature reduction was obtained due to seepage propagation in the sand. The rate of temperature reduction was observed to be dependent on the seepage flow velocity.

Temperature measurements are widely used in structural health monitoring. Optical fiber distributed temperature sensors (DTS) are developed, based on Raman spectroscopy, to measure temperature with relatively high accuracy and short temporal and spatial resolutions. DTS systems provide an extensive number of temperature measurements along the entire length of an optical fiber that can be extended to tens of kilometers. The efficiency of the temperature measurement strongly depends on the calibration of the DTS data. Although DTS systems internally calibrate the data, manual calibration techniques were developed to achieve more accurate results. Manual calibration employs reference sections or points with known temperatures and the DTS scattering data to estimate the calibration parameters and calculate temperature along the optical fiber. In some applications, manual calibration is subjected to some shortages, based on the proposed fiber installation configuration and continuity of calibration. In this article, the manual calibration approach was developed using the model-independent Parameters Estimation (PEST), together with the external temperature sensors as references for the DTS system. The proposed method improved manual calibration in terms of installation configuration, continuity of dynamic calibration, and estimation of the calibration parameters.