A neural network approach for predicting the structural behavior of concrete slabs

Abstract

In this paper the use of a nontraditional technique, neural networks, has been investigated as a means to develop efficient predictive models of the structural behavior of concrete slabs. The applicability of neural networks within the realm of structural analysis is first reviewed, along with their state-of-the-art applications and research efforts. Four neural networks have then been developed to model the load-deflection behavior of concrete slabs, the final crack-pattern formation, and both the reinforcing-steel and concrete strain distributions at failure. The four neural networks were trained and tested using the experimental results of 38 full-scale slabs. Details regarding data modeling, neural network training, and performance evaluation are described. Using the developed networks, a spreadsheet tool for the structural analysis of concrete slabs was developed with a simple interface, automated predictions, and what-if capabilities. The developed tool is useful for teaching purposes and for reasonable prediction of the behavior of concrete slabs without additional experimental testing.Key words: neural networks, structural analysis, reinforced concrete, load-deflection, crack pattern, artificial intelligence, concrete slabs.