Die detailierte Untersuchung von 25 tiefgreifenden Hangdeformationen im Ostalpenkorper zeigt, dass Gesteine mit deutlicher Festigkeitsanisotrophie besonder betroffen sind. Das Auftreten ist vornehmlich an metamorphe Gesteinsserien geknupft (77 %), wobei Phyllite mit 36 %, Gneise mit 23 % und Glimmerschiefer mit 18 % beteiligt sind.

Da die tiefgreifenden und grosflachigen Hangbewegungen nicht unerhebliche Beeintrachtigungen und sogar Zerstorungen von Infrastruktur und Siedlungen bedeuten (Kap. 8), hat sich im Laufe der letzten 50 Jahre ein umfangreicher Masnahmenkatalog etabliert.

Die Autoren stellen umfangreiche Forschungsergebnisse tiefgreifender und großflächiger Hangdeformationen vor. Sie bewerten diese hinsichtlich ihrer Auswirkungen auf die Umwelt, ihrer Erscheinungsforme

Durch die Aufnahmen der letzten Jahrzehnte schalen sich folgende Untersuchungsschwerpunkte heraus, die ein zielfuhrendes Management solcher tiefgreifender und grosflachiger Hangbewegungen ermoglichen (Abb. 64).

Obwohl seit den 60iger Jahren eine Vielzahl von Untersuchungen vorgelegt wurde, ist besonders bei sehr tiefgreifenden und grosflachigen Hangdeformationen dieses Typs der Kenntnisstand gering.

Die Analyse der Kinematik einer instabilen Talflanke uber einen langeren Zeitraum wird in der Zusammenschau mit weiteren Parametern, wie z.B. den hydrographischen Charakteristiken, Aussagen uber folgende Aspekte erlauben.

Die Auswirkungen von grosflachigen und tiefgreifenden Hangbewegungen auf Umwelt, Infrastruktur und bautechnische Anlagen sind im alpinen Raum nicht zu ubersehen. Solche Hangbewegungen, die nicht nur Lockeruberlagerungen, sondern auch Festgesteine (vor allem metamorphe Gesteine wie Glimmerschiefer, Phyllite usw.) betreffen, fuhren nicht nur zu Beeintrachtigungen, sondern sehr oft zu weitreichenden Zerstorungen.

This paper deals with the inverse problem of using time-displacement monitoring data to determine the material parameters of a numerical model of a large-scale mass movement. A finite element model for simulating the mechanical behavior is presented for the Gradenbach landslide in Carinthia, Austria. Particular attention is paid to the calibration of the constitutive relationships, which represent a prerequisite for a realistic quantitative analysis. After a short introduction to the concept of model-parameter identification, this paper demonstrates how to apply the proposed model identification strategy to determine model parameters for the Gradenbach example. The impact of the amount of reference data available for the inverse model-parameter analysis is evaluated by means of artificial reference data. Subsequently, the numerical model is calibrated using field measurement data. The results obtained are presented, and the benefits and drawbacks of the proposed concept are evaluated.

Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head.

Objective Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation. Methods The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification. Results On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p -value of 0.019 emphasizes the statistical difference of both approaches. Conclusions The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.

When working with numerical models, it is essential to determine model parameters which are as realistic as possible. Optimization techniques are used more and more frequently to solve this task. However, using these methods may lead to very high time costs – in particular, if rather complicated forward calculations are involved. In this paper, we present a class of methods wich allows estimating the solution of this kind of optimization problems, based on relatively few sampling points. We put very weak constraints on the sampling point distribution; hence, they may be taken from previous forward calculations as well as from alternative sources. Starting from an introduction into the theoretical approach, a strategy for speeding up inverse optimization problems is introduced which is illustrated by an example from geomechanics.