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Advances of engineering geodesy and artificial intelligence in monitoring of movements and deformations of natural and man-made structures

 
: Retscher, G.; Mentes, G.; Reiterer, A.

:

Rizos, C. ; International Union of Geodesy and Geophysics -IUGG-:
Earth on the edge. Science for a sustainable planet : Proceedings of the IAG general assembly, Melbourne, Australia, June 28 - July 2, 2011
Berlin: Springer, 2014 (International Association of Geodesy Symposia 139)
ISBN: 978-3-642-37221-6 (Print)
ISBN: 978-3-642-37222-3 (Online)
ISBN: 3-642-37221-X
pp.481-485
International Union of Geodesy and Geophysics (IUGG General Assembly) <25, 2011, Melbourne>
English
Conference Paper
Fraunhofer IPM ()

Abstract
Rapid developments in engineering, microelectronics and computer sciences have greatly changed both the instrumentation and the methodology in engineering geodesy. Advanced technology is needed to meet the challenges of today. In dynamic monitoring, for instance, there is an urgent need for continuous geodetic measurements to determine complex movements. The development of an early warning system is possible only when exact knowledge of the process of the objects movement (e.g. of a landslide area) and all the other physical parameters are available. Special emphasis is laid on the following research areas: detection of potential movements on a large scale, an efficient and continuous observation of critical areas and knowledge-based derivation of real time information about actual risks in order to support an alert system. The necessary tools range from conventional terrestrial measurements and alignment technology - GNSS, InSAR, geotechnical instrumentation - to software systems such as GIS, Spatial Decision Support Systems (SDSS), and so on. For the development of alert systems, the application of Artificial Intelligence (AI) techniques in engineering geodesy is studied. AI, in general, means studying and designing intelligent agents. An intelligent agent is a system which perceives its environment and takes actions to maximize its chances of success. Methods used for uncertain reasoning are probabilistic in nature, such as Bayesian networks, which represent a general tool that can be used for a large number of problems. The achievements of the work in these fields in the past 4 years are presented and discussed in this paper.

: http://publica.fraunhofer.de/documents/N-351328.html