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Error in prediction due to data type availability in a coupled hydro-mechanical model

  • 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.

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Document Type:Article
Author: José Guillermo De Aguinaga
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.3117Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20170413-31170Cite-Link
Parent Title (English):Electronic Journal of Geotechnical Engineering
Date of Publication (online):2017/04/13
Year of first Publication:2012
Release Date:2017/04/13
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Graduiertenkolleg 1462
First Page:2459
Last Page:2471
Tag:Embankment, sensitivity analysis, parameter identification, Particle Swarm Optimization
GND Keyword:Sensitivitätsanalyse; Damm; Fehlerabschätzung
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme
600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften
BKL-Classification:54 Informatik / 54.80 Angewandte Informatik
56 Bauwesen / 56.03 Methoden im Bauingenieurwesen
Licence (German):License Logo Zweitveröffentlichung