TY  - JOUR
A1  - Aguinaga, José Guillermo De
T1  - Error in prediction due to data type availability in a coupled hydro-mechanical model
JF  - Electronic Journal of Geotechnical Engineering
N2  - Different types of data provide different type of information. The present research analyzes the error on prediction obtained under different data type availability for calibration. The contribution of different measurement types to model calibration and prognosis are evaluated. A coupled 2D hydro-mechanical model of a water retaining dam is taken as an example. Here, the mean effective stress in the porous skeleton is reduced due to an increase in pore water pressure under drawdown conditions. Relevant model parameters are identified by scaled sensitivities. Then, Particle Swarm Optimization is applied to determine the optimal parameter values and finally, the error in prognosis is determined. We compare the predictions of the optimized models with results from a forward run of the reference model to obtain the actual prediction errors. The analyses presented here were performed calibrating the hydro-mechanical model to 31 data sets of 100 observations of varying data types. The prognosis results improve when using diversified information for calibration. However, when using several types of information, the number of observations has to be increased to be able to cover a representative part of the model domain. For an analysis with constant number of observations, a compromise between data type availability and domain coverage proves to be the best solution. Which type of calibration information contributes to the best prognoses could not be determined in advance. The error in model prognosis does not depend on the error in calibration, but on the parameter error, which unfortunately cannot be determined in inverse problems since we do not know its real value. The best prognoses were obtained independent of calibration fit. However, excellent calibration fits led to an increase in prognosis error variation. In the case of excellent fits; parameters' values came near the limits of reasonable physical values more often. To improve the prognoses reliability, the expected value of the parameters should be considered as prior information on the optimization algorithm.
KW  - Sensitivitätsanalyse
KW  - Damm
KW  - Embankment, sensitivity analysis, parameter identification, Particle Swarm Optimization
KW  - Fehlerabschätzung
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170413-31170
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868020397&partnerID=40&md5=72c87bb112839303c1ef9a4afa8c6421
SP  - 2459
EP  - 2471
ER  - 
TY  - JOUR
A1  - Scheuermann, Alexander
A1  - Huebner, Christof
A1  - Schlaeger, Stefan
A1  - Wagner, Norman
A1  - Becker, Rolf
A1  - Bieberstein, Andreas
T1  - Spatial time domain reflectometry and its application for the measurement of water content distributions along flat ribbon cables in a full-scale levee model
JF  - Water Resources Research
N2  - Spatial time domain reflectometry (spatial TDR) is a new measurement method for determining water content profiles along elongated probes (transmission lines). The method is based on the inverse modeling of TDR reflectograms using an optimization algorithm. By means of using flat ribbon cables it is possible to take two independent TDRmeasurements from both ends of the probe, which are used to improve the spatial information content of the optimization results and to consider effects caused by electrical conductivity. The method has been used for monitoring water content distributions on a full-scale levee model made of well-graded clean sand. Flood simulation tests, irrigation tests, and long-term observations were carried out on the model. The results show that spatial TDR is able to determine water content distributions with an accuracy of the spatial resolution of about ±3 cm compared to pore pressure measurements and an average deviation of ±2 vol % compared to measurements made using another independent TDR measurement system.
KW  - Damm
KW  - Infiltration
KW  - Bodenfeuchte
KW  - Transient and time domain; Dams; Infiltration; Soil moisture; calibration; levee model; soil moisture measurement; spatial time domain reflectometry
Y1  - 2009
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170425-31601
ER  -