Soil laboratory data interpretation using generalized regression neural network

Artificial intelligence techniques which incorporate empirical knowledge and/or pattern matching techniques are ideally suited to assist engineers to interpret information from site and laboratory investigations because of the "imprecise" nature of soil. This paper explores the pattern matching and prediction capabilities of neural networks to interpret laboratory test data. The neural network paradigm used in this paper is the generalized regression neural network (GRNN) algorithm. Detailed examples are given of the use of this approach to assist engineers to interpret laboratory test data from consolidation tests and to characterize soil types from laboratory particle size distribution information. The main advantage of the GRNN technique in comparison to the widely used backpropagation neural network algorithm is the speed at which the optimal neural network configuration is determined, since this process only involves adjusting one variable.