Neural network-based design of edge-supported reinforced concrete slabs - Arslan, A; Ince, R (Pergamon-Elsevier Science Ltd, 1996)
Modeling of material behavior generally involves the development of mathematical models derived from observations and experimental data. An alternative way discussed in this paper, is neural network-based modeling that is a subfield of artificial intelligence. The main benefit in using a neural network approach is that the network is built directly from experimental or theoretical data using the self-organizing capabilities of the neural network. In this article, a back-propagation neural network package (NETICE) was presented which has been developed for use in general purpose work. NETICE (Neural nETworks In Civil Engineering) has been used in the design of edge-supported reinforced concrete slabs and the results are presented in this study. It has been observed that the results given by the network has a good approximation when compared with the conventional solution. Copyright (C) 1996 Civil-Comp Ltd and Elsevier Science Ltd.
2nd International Conference on Computational Structures Technology -- AUG 30-SEP 01, 1994 -- ATHENS, GREECE