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Simulation Data Mining for Supporting Bridge Design

Burrows, S., Stein, B., Frochte, J., Wiesner, D. and Mueller, K.

    We introduce simulation data mining as an approach to extract knowledge and decision rules from simulation results. The acquired knowledge can be utilized to provide preliminary answers and immediate feed- back if a precise analysis is not at hand, or if waiting for the actual simulation results will considerably im- pair the interaction between a human designer and the computer. This paper reports on a bridge design project in civil engineering where the motivation to apply simulation data mining is twofold: (1) when dealing with real-world bridge models the simulation efficiency is inadequate to gain true interactivity during the design process, and (2) the designers are confronted with a parameter space (the design space) of enormous size, from which they can analyze only a small fraction. To address both issues, we propose that a database of models (the design variants) should be pre-computed so that the behavior of similar models can be used to guide decision making. In particular, simulation results based on displacement, strain, and stress analyses are clustered to identify models with similar behavior, which may not be obvious in the design space. By means of machine learning, the clustering results obtained in the simulation space can be transferred back into the design space in the form of a highly non-linear similarity measure that compares two design alternatives based on relevant physical connections. If the assessments of the measure are reliable, it will perfectly address the mentioned issues above. With this approach we break new ground, and our paper details the technology and its application for a real-world design setting.
Cite as: Burrows, S., Stein, B., Frochte, J., Wiesner, D. and Mueller, K. (2011). Simulation Data Mining for Supporting Bridge Design. In Proc. Australasian Data Mining Conference (AusDM 11) Ballarat, Australia. CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 163-170
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