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The pattern-mapping, pattern-classiJication, and optimization capabilities of neural networks have been used to solve a number of structural analysis and design problems. Most applications exploit the pattern-mapping capabilio and are based on the back-propagation paradigm .for neural networks. There are a number of factors that influence the performance of these networks. This paper initially discusses these factors and the domain-dependent and -independent techniques presently available for improving performance. The paper then considers the effect of representation, selected for the inputloutput pattern pairs, on the performance of these networks and demonstrates that representations based on dimensionless terms, derived from dimensional analysis, lead to improved performance. It is shown that dimensional analysis provides a representational framework, with reduced dimensionality and embedded domain knowledge, within which effective learning can take place and that this representational change can be used to enhance the domain-independent and -dependent techniques presently available for improving performance of these networks. 0 1994 Microcompurers in Civil Engineering. Published by Blackwell Publishers,

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This paper focuses on that form of learning that relates to exploration, rather than generalization. It uses the notion of exploration as the modification of state spaces within which search and decision making occur. It demonstrates that the genetic algorithm formalism provides a computational construct to carry out this learning. The process is exemplified using a shape grammar for a beam section. A new shape grammar is learned that produces a new state space for the problem. This new state space has improved characteristics.