Neural network design for engineering applications

Computers are an integral part of day to day activities in engineering design and engineers have utilised various applications to assist them improve their design. Although computers are used to model a variety of engineering activities, currently the main focus of computer applications are areas with well defined rules. Activities related to the conceptual stage of the design process are generally untouched by computers. Artificial neural networks (ANN) have recently been widely used to model some of the human activities in many areas of science and engineering. Early applications of NN in civil engineering occurred the late eighties. One of the distinct characteristics of the ANN is its ability to learn from experience and examples and then to adapt with changing situations. Engineers often deal with incomplete and noisy data, which is one area where NN are most applicable. This is particularly the case at the conceptual stage of the design process. This paper presents practical guidelines for designing ANN for engineering applications. A brief introduction to NN is given; major aspects of three types of NN, multi-layer perceptron (MLP), radial basis network (RBF) and normalised RBF (NRBF) are discussed; new methods for selection and normalisation of training data are introduced and a practical example of a reinforced concrete slab design is presented.