MODELING INITIAL DESIGN PROCESS USING ARTIFICIAL NEURAL NETWORKS

The preliminary design model is of vital importance in the synthesis of a finally acceptable solution in a design problem. The initial design process is extremely difficult to computerize because it requires human intuition. It has often been impossible to form declarative rules to express human intuition and past experience. The suitability of an artificial neural network for modeling an initial design process has been investigated in this paper. Development of a network for initial design of reinforced-concrete rectangular single-span beams has been reported. The network predicts a good initial design (i.e., tensile reinforcement required, depth of beam, width, cost per meter, and the moment capacity) for a given set of input parameters (i.e., span, dead load, live load, concrete grade, and steel type). Various stages of development and performance evaluation with respect to rate of learning, fault tolerance, and generalization have been presented.