An Artificial Intelligence Approach Towards Sensorial Materials
Florian Pantke, Stefan Bosse, Michael Lawo, Matthias Busse
In Proceedings of the Third International Conference on Future Computational Technologies and Applications, (Future Computing), 25.9.-30.9.2011, Rome, o. A., Sep/2011. ISBN: 978-1-61208-011-6.
Sensorization aims at equipping technical structures with an analog of a nervous system by providing a network of sensors and communication facilities that link them. The objective is that, instead of having been designed to loads and tested to conditions, a structure can experience and report design constraint violations by means of realtime self-monitoring. Specialized electronic components and computational algorithms are needed to derive meaning from the combined signals. For this task, artificial intelligence trend of ever increasing sensor network size and density suggests that sensor and structure may soon become one, forming a sensorial material. Current simulation techniques capture many aspects of sensor networks and structures. For decision making and communication, the Intelligent Agent paradigm is an accepted approach, as is finite element analysis for structural behavior. To gain knowledge how sensorial structures can most effectively be built, an artificial intelligence based process for the construction of such structures was
developed that uses machine learning methods for fast load inference. It is presented in this paper, along with evaluation results obtained in experiments using a finite element model of a strain gauge equipped plate which demonstrate the general practicability.
sensorial material; finite element method; sensor network; machine learning; multi-agent system