Assessment of long-term coordinate time series using hydrostatic-season-time model for rock-fill embankment dam

Abstract

The safety control of dams is based on monitoring activities and modelling of registered observations. The statistical hydrostatic-season-time model was originally developed and proposed for analyzing of monitoring data on concrete dams. In some later works, the model was implemented for earth-fill embankment dams. The model admits a simultaneous estimation of hydrostatic load, temperature influences and irreversible deformations. In our study, we analyze long-term coordinate time series of a geodetic point on the crest of a rock-fill embankment dam. Coordinate time series are result of an adjustment of the observations in a permanent geodetic network for different epochs. An optimal model is defined using a multiple linear regression by combined process of exclusion and inclusion of individual parameters. In the process, different statistical parameters are observed. The analyses confirmed that not all parameters are significant. The most interesting and important conclusion of computations can be stated as follows: after inclusion of significant coefficients of the hydrostatic load and long-term trend, the residual time series still expose underlying periodicities. They can be removed by inclusion of at least one parameter of the seasonal term, where the temperature influences (i.e. air, water and soil) are modelled. The influences of the temperature on the dam are not significant in any direction, but the inclusion of the parameters improves the statistical performance of the used model.

Dynamic modelling of displacements on an embankment dam using the Kalman filter

“…Namely, the reversible and irreversible deformations are of the utmost significance in this direction. The coordinate system is defined as Gamse et al (2016):…”

“…In Gamse et al (2016), the inverse modeling of geodetically measured displacements on a rock-fill embankment dam is presented using multiple linear regression of the hydrostaticseason-time model. The estimated empirical model, which models the influence of the water level in an impounding reservoir and the influence of water and air temperature on the dam, can be used for future predictions and as a real-time alarm system, under the presumption that the future external influences are in the same max-min range as for the modeling period.…”

Detection Model for Seepage Behavior of Earth Dams Based on Data Mining

“…Thus, this algorithm was optimized, and the accuracy of the model was improved. Gamse and Oberguggenberger [5] used a "coordinate time series" method to analyze the seepage monitoring data of earth dams, thereby avoiding the overfitting phenomenon in the model. The expressions for water level, temperature, and aging factor were reasonable, and the monitoring model detected abnormal seepage behavior.…”

Initial service life data towards structural health monitoring of a concrete arch dam

“…With the availability of reliable low‐cost sensors to an advancement in their miniaturization, data storage, low‐power consumption, and communication technologies, there has been a marked adoption of data‐driven prediction and automation methods for structural health monitoring (SHM) of dams. () In these approaches, data collected from in situ sensors are utilized to develop prediction models, data analytics to prognose failures, and to schedule dam safety programs. Dams are increasingly being regarded as critical structures, which call for mandatory application of SHM to improve their performance under natural hazards and to track their structural integrity over their lifetime.…”