Neural-network applications in predicting moment-curvature parameters from experimental data

The objective of this study is to demonstrate a concept and a methodology, rather than to build a full-scale knowledge-based system model by incorporating most of the fundamental aspects of a neural network to solve the complex non-linear mapping for a beam-column joint. This paper presents the concept of parallel distributed processing base learning in artificial neural networks, in assisting with experimental evidence to predict moment-curvature parameters that are usually accomplished solely by experimental work. Generally, it may be possible to identify certain parameters, and allow the neural network to develop the model, thus accounting for the observed behaviour without relying on a particular algorithm, but depending entirely on the manipulation of numerical data. Copyright (C) 1996 Elsevier Science Ltd