The organisers invite prospective authors to submit an abstract for a lecture
(12 min oral presentation / 3 min discussion) or poster relating to the congress
topics. The abstracts will be evaluated and, if accepted, the authors
will be informed about their assigned type of presentation (oral or poster).
Especially young scientists are welcome to actively contribute to the congress
by submitting an abstract. The submission deadline for the abstracts is
17 February 2014.

Authors are invited to submit selected papers which will be published in a special Issue of EMERGING MATERIALS RESEARCH (EMR) covering our topic. The journal homepage is at http://www.icevirtuallibrary.com/content/serial/emr.

Symposia
===================

A05: Material-integrated Intelligent Systems for Real Time Condition Awareness

Organizers
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Dr. Dirk Lehmhus, University of Bremen, Scientific Centre ISIS, (Germany)
Dr. Axel von Hehl, Stiftung Institut für Werkstofftechnik, Bremen (Germany)
Dr. Stefan Bosse, Dept. of Mathematics & Computer Science, WG Robotics, University of Bremen (Germany)
Prof. Dr. Thomas Hochrainer, Bremen Institute for Mechanical Engineering, University of Bremen (Germany)

Topics
======

Development trends in structural health monitoring and control of mechanically loaded structures profit from continuing miniaturization of sensors and sensor network components such as signal- and data processing or communication hardware, energy harvesting and storage systems etc. This technological background supports realization of material-integrated intelligent systems which locate reliable and fault-tolerant distributed data processing and information evaluation within the monitored material itself.

Best use can be made of such systems if the latter is achieved in real time. The final aim is to go beyond load monitoring, by quantifying and localizing a load acting on the structure, by evaluating its effect on structural state, and by incorporating the outcome of this evaluation in future analyses. This implies either recognition or prediction of internal damage within the structure in terms of size, position, geometry and effect on structural performance under all service conditions ? information which must then be integrated in the internal models the respective materials and structures use in sensor data interpretation.

Addressing this challenge, and providing the required real-time capability, necessitates an interdisciplinary approach which combines in-depth knowledge of materials response, failure mechanisms etc. in combination with advanced data evaluation and system identification techniques. Conventional inverse FEM methods, though fast for a fixed structural state, typically lack the ability of internal material model adaptation in response to an identified state of damage. At the same time, the chain that links an external load, the change it may have caused within the material under the given boundary conditions (environment-, service or service history-related) and the recorded sensor signal is still subject to uncertainty. For example, structural performance will be influenced by material-integrated sensors and electronic components via their own mechanical properties, interaction and compliance with host material characteristics, their interfaces with the host material etc. The ensuing uncertainty is a major obstacle towards implementation e.g. of SHM systems in aerospace, as it precludes pinpointing the exact level of safety of an envisaged intelligent material or structure at any moment in time and thus prevents exploitation of the structure up to its true performance limits.

With this background, the symposium means to bring together experts in sensor integration, materials performance evaluation (structural health monitoring, NDT etc.) and structural and failure mechanics (initiation of failure, „effects of defects“ etc.) with researchers working on sensor network data evaluation using novel methods from artificial intelligence and applied mathematics to support an interdisciplinary discussion of material-integrated intelligence for structural health monitoring, management and control applications.