Advertisement

Parametric Modeling of Static and Dynamic Processes in Engineering Geodesy

  • A. EichhornEmail author
Conference paper
Part of the International Association of Geodesy Symposia book series (IAG SYMPOSIA, volume 140)

Abstract

In this paper, the main focus is set on the utilization of parametric methods for the quantification of causative relationships in static and dynamic deformation processes. Parametric methods are still ‘exotic’ in engineering geodesy but state of the art e.g. in civil and mechanical engineering. Within this context, an essential part is the physical (parametric) modeling of the functional relationships based on partial or ordinary differential equations using the corresponding numerical solutions represented by finite element (FE) or finite difference (FD) models.

The identification of a physical model is realised by combination with monitoring data. One important part of the identification includes establishing the deterministic model structure and estimating a priori unknown model parameters and initial respectively boundary conditions by filtering (e.g. adaptive Kalman-filtering). Major challenges are establishing the parametric model structure, quantifying disturbances and the identifiability of the model parameters which are possibly non-stationary. These challenges are discussed with the help of a practical example from engineering geology.

Keywords

Descriptive and causative view GB-SAR Mass movement Parametric modeling Static and dynamic deformation processes 

Notes

Acknowledgment

The author thanks the FWF (Austrian Science Fund) for the financial support of the project ‘KASIP’, project number P20137.

References

  1. Eichhorn A (2005) Ein Beitrag zur Identifikation von dynamischen Strukturmodellen mit Methoden der adaptiven Kalman-Filterung. PhD thesis, IAGB, Uni StuttgartGoogle Scholar
  2. Gallagher RH (1976) Finite-element-analysis. Springer, BerlinCrossRefGoogle Scholar
  3. Gelb A, Kasper JF, Nash RA, Price CF, Sutherland AA (1974) Applied optimal estimation. The M.I.T Press, CambridgeGoogle Scholar
  4. Gülal E (1997) Geodätische Überwachung einer Talsperre; eine Anwendung der KALMAN-Filtertechnik. In: Wiss. Arbeiten der Fachrichtung Verm.wesen der Uni Hannover, Nr. 224, HannoverGoogle Scholar
  5. Hesse C (2002) Deformation analysis of a shell structure under varying loads with Kalman-filter techniques. In: Proceedings of 2nd symposium for geotechnology and structural engineering, BerlinGoogle Scholar
  6. Heunecke O (1995) Zur Identifikation und Verifikation von Deformationsprozessen mittels adaptiver KALMAN-Filterung (Hannoversches Filter). In: Wiss. Arbeiten der Fachrichtung Verm.wesen der Uni Hannover, Nr. 208, HannoverGoogle Scholar
  7. Heunecke O (1996) Einige Gedanken zur fachübergreifenden Untersuchung von Deformationsvorgängen, dargestellt am Beispiel der Filterung der Biegelinie eines Pylons. In: Wiss. Arbeiten der Fachrichtung Verm.wesen der Uni Hannover, Hannover, pp 75–92Google Scholar
  8. Isermann R (1988) Identifikation dynamischer Systeme. Springer, BerlinCrossRefGoogle Scholar
  9. Itasca (2006) Fast Lagrangian analysis of continua in three dimensions. Version 3.1. manualGoogle Scholar
  10. Lienhart W (2007) Analysis of Inhomogenous Structural Monitoring Data. PhD thesis, Engineering Geodesy, TU GrazGoogle Scholar
  11. Mair am Tinkhof K, Preh A, Tentschert E, Eichhorn A, Schmalz T, Zangerl C (2010) FLAC3D and adaptive Kalman-filtering – a new way to install effective alarm systems for landslides? In: Eurock rock mechanics symposium 2010, LausanneGoogle Scholar
  12. Rödelsperger S (2011) Real-time processing of ground based synthetic aperture radar (GB-SAR) measurements. PhD thesis, TU Darmstadt, Schriftenreihe der Fachrichtung Geodäsie, Heft 33Google Scholar
  13. Rödelsperger S, Läufer G, Gerstenecker C, Becker M (2010) Terrestrische Mikrowelleninterferometrie – Prinzip und Anwendungen. In: AVN, 10/2010, pp 324–333Google Scholar
  14. Roth W (1999) Entwicklung von Sicherheitsfaktoren mittels des kontinuumsmechanischen FD-Codes FLAC. Master thesis, Engineering Geology, TU ViennaGoogle Scholar
  15. Schmalz T, Buhl V, Eichhorn A (2010) An adaptive Kalman-filtering approach for the calibration of FD-models of mass movements. J Appl Geod 3:127–135Google Scholar
  16. Schmidt H-H (2006) Grundlagen der Geotechnik. Teubner Verlag, WiesbadenGoogle Scholar
  17. Smith GD (1985) Numerical solution of partial differential equations – FD methods. Oxford University Press, New YorkGoogle Scholar
  18. Welsch W, Heunecke O, Kuhlmann H (2000) Auswertung geodätischer Überwachungsmesungen. Handbuch der Ingenieurgeodäsie, WichmannGoogle Scholar
  19. Zangerl C, Eberhardt E, Schönlaub H, Anegg J (2007) Deformation behaviour of deep-seated rockslides in crystalline rock. In: Rock mechanicsGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.TU Darmstadt Institute of GeodesyDarmstadtGermany

Personalised recommendations