Title	Computational Intelligence in Structural Analysis and Design
Speaker	Prof. Michael Beer
Chair	Vladik Kreinovich

Abstract
Engineering analyses are associated with two key challenges; they must be realistic and numerically efficient. A realistic analysis requires a proper description of the physics of the underlying problem in the numerical model. In the case of complex problems or physics this can easily lead to a quite high computational cost in order to arrive at reasonably realistic results. If the available information about the physics and the problem is vague and limited, a numerical model cannot be formulated with sufficient confidence. In engineering design additional requirements need to be considered to ensure products to serve their purpose. This includes robust design to compensate deviations from normal conditions and even unforeseen events. Also, decision margins are often desired to provide flexibility in variant development and more freedom in use. Challenges are then to translate the requirements into numerical descriptions, to identify the most suitable design solutions that meet the various requirements, to find variants there of and to compare them with one another. In these contexts, engineers have sought help from computational intelligence in various forms and for various purposes.

This keynote lecture provides insight in civil and mechanical engineering approaches to develop solutions to the described challenges with the aid of computational intelligence. Selected developments are discussed with focus on the added value for engineering analyses and are demonstrated on industrial examples. These developments include processing of vague information as fuzzy sets with evolutionary concepts [1,2] and their use in design [3], efficient stochastic analysis with meta models [4,5] and process simulation [6,7] based on neural networks, robust design [3] and identification of critical mechanical behavior [8] with the aid of cluster analysis methods. The examples include dynamical analyses of civil engineering structures and of an aerospace structure, as well as nonlinear dynamical problems in crashworthiness analysis.

Keywords: Crash analysis, Structural analysis, Process simulation, Neural network, Cluster analysis, Robust design, Meta model.

References
[1] B. Möller and M. Beer, Fuzzy Randomness – Uncertainty in Civil Engineering and Computational Mechanics. Berlin, Heidelberg, New York: Springer, 2004.
[2] B. Möller, W. Graf and M. Beer, “Fuzzy structural analysis using alpha-level optimization”, Computational Mechanics, vol. 26, pp. 547–565, 2000.
[3] M. Beer and M. Liebscher, “Designing robust structures – a nonlinear simulation based approach”, Computers & Structures, vol. 86 (10), pp. 1102–1122, 2008.
[4] B. Goller, M. Broggi, A. Calvi and G. I. Schuëller, “A stochastic model updating technique for complex aerospace structures”, Finite Elements in Analysis and Design, vol. 47(7), pp. 739–752, 2011.
[5] W. Beyer, M. Liebscher, M. Beer and W. Graf, “Neural Network Based Response Surface Methods – a Comparative Study”, Proceedings of the 5th German LS-DYNA Forum 2006, Ulm: DYNAmore GmbH, pp. K-II-29 – K-II-37.
[6] M. Beer and P. D. Spanos, “A Neural Network Approach for Simulating Stationary Stochastic Processes”, Structural Analysis and Mechanics Structural Engineering and Mechanics, vol. 32 (1), pp. 71–94, 2009.
[7] L. A. Comerford, I. A. Kougioumtzoglou, and M. Beer, “An artificial neural network based approach for power spectrum estimation and simulation of stochastic processes subject to missing data”, Proceedings of the 2013 IEEE Symposium Series on Computational Intelligence, Singapore, 2013 (in press).
[8] M. Beer and M. Liebscher, “Detection of Branching Points in Noisy Processes”, Computational Mechanics, vol. 45(4), pp.363–374, 2010.

Biography
Michael Beer is Professor of Uncertainty in Engineering and Director of the Institute for Risk & Uncertainty in the University of Liverpool. He graduated with a doctoral degree in Civil Engineering from the Technische Universität Dresden, Germany. As a Feodor-Lynen Fellow of the Alexander von Humboldt-Foundation Dr. Beer pursued research at Rice University together with Professor Pol D. Spanos. From 2007 to 2011 he worked as an Assistant Professor in the Department of Civil & Environmental Engineering, National University of Singapore. His research is focused on non-traditional uncertainty models in engineering with emphasis on reliability analysis and on robust design. Dr. Beer is a member of ASME, Charter Member of the ASCE Engineering Mechanics Institute, Member of the European Association for Structural Dynamics, Member of the European Safety and Reliability Association, Member of IACM, Associate Editor of the International Journal of Reliability and Safety, as well as Member of the Editorial Boards of Probabilistic Engineering Mechanics, Computers & Structures, Structural Safety, International Journal of Computational Methods, and International Journal of Engineering under Uncertainty: Hazards, Assessment, and Mitigation.

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