Reinforced concrete slabs exhibit complexities in their structural behavior due to the composite nature of the material and the multitude and variety of factors that affect such behavior. As such, current methods for the design and analysis of reinforced concrete slabs are limited in scope and are approximate at best as they must rely on the results of experimental tests, which are both costly and time-consuming to perform. The research embodied by this document investigates the use of a branch of artificial intelligence known as Neural Networks (NN) as a quick and reliable alternative to such experimental testing.; Four neural network models are developed to predict the following aspects of the overall behavior of a concrete slab: (1) load-deflection behavior; (2) crack pattern at failure; (3) concrete strain distribution; and (4) reinforcing steel strain distribution. Results from experimental tests on thirty-four full scale slabs are utilized to develop these four models, incorporating all of the parameters that govern their behavior. (Abstract shortened by UMI.)
展开▼
