Representing Design Knowledge with Neural Networks

Neural networks have been used in a number of civil engineering applications because of their ability to implicitly learn an input–output relationship. Typically, the applications involve deriving an input–output relationship for problems that may be too complex to model mathematically, computationally expensive, or difficult to solve using the traditional procedural computing approach. Heuristic design knowledge used by structural engineers when performing structural design often falls in the latter category of being difficult to represent procedurally. Neural networks have been investigated for the representation of heuristic design knowledge, and the results of this investigation and the lessons learned regarding neural network training are presented.