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Introduction: Osteosynthesis of the equine femur is still a challenge for veterinary medicine. Even though intramedullary fracture fixation is possible nowadays, the varying geometry of the medullary cavity along the bone axis is a critical factor. Limited contact area between implant and bone can cause insufficient primary stability. In this study, it was investigated whether the osteosynthesis stability can be improved with a form-adaptive reinforcement for the diaphyseal part of the proximal fragment. Material and Methods: Eight equine femora were fitted with intramedullary nail osteosynthesis and analyzed by 4-point bending. Virtual position planning of the ex-vivo implantation using CT-data increased comparability. For five femora the proximal fragment was reinforced with a flexible polymer mixture. Longterm stability was tested via cyclic loading. Bending stiffness and its development due to cyclic loading was evaluated before and after reinforcement procedure. Finally, load-to-failure was tested in the same setup. Results and Discussion: The application of the polymer reinforcement increased the maximum torque in the load-tofailure measurement by 26%. Bending stiffness was not affected in the measured loading range by the reinforcement. Cyclic loading increased bending stiffness for a conditioned state but showed to be reversible for the most part. Conclusion: The fracture adjacent reinforcement showed to be beneficial to the osteosynthesis stability, but further investigation is necessary for surgical application.

This paper develops a model-assisted approach for determining predictive probability of detection curves. The approach is "model-assisted," as the damage-sensitive features are evaluated in combination with a numerical model of the examined structure. It is "predictive" in the sense that probability of detection (POD) curves can be constructed based on measurement records from the undamaged structure, avoiding any destructive tests. The approach can be applied to a wide range of damage-sensitive features in structural health monitoring and non-destructive testing, provided the statistical distribution of the features can be approximated by a normal distribution. In particular, it is suitable for global vibration-based features, such as modal parameters, and evaluates changes in local structural components, for example, changes in material properties, cross-sectional values, prestressing forces, and support conditions. The approach explicitly considers the statistical uncertainties of the features due to measurement noise, unknown excitation, or other noise sources. Moreover, through confidence intervals, it considers model-based uncertainties due to uncertain structural parameters and a possible mismatch between the modeled and the real structure. Experimental studies based on a laboratory beam structure demonstrate that the approach can predict the POD before damage occurs. Ultimately, several ways to utilize predictive POD curves are discussed, for example, for the evaluation of the most suitable measurement equipment, for quality control, for feature selection, or sensor placement optimization. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) techniques can be used for the investigation of local damage in materials. Compared to other observation techniques one advantage is the recording of the damage process during the entire load history without any disturbance to the specimen. This is somehow unique and permits for high-resolution studies of the time-dependent failure of materials including precise determinations of the beginning of fractures and their consecutive progression. There are only a few other experimental techniques in fracture mechanics allowing for similar detailed observations of materials' time and spatial behaviour. Moreover, these techniques allow a calibration of experiment and simulation comparing for example the output of finite element modelling with AE data analysis. This paper deals with some experimental results obtained during fracture mechanical experiments at concrete specimens demonstrating the capabilities of quantitative AE techniques. (C) 2006 Elsevier Ltd. All rights reserved.