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Interpretation of in-situ test data using artificial neural networks

Establishing a realistic working profile of soil properties has been, and is still, one of the most challenging problems facing geotechnical engineers. A neural network approach is used to tackle this problem. Source data of a series of standard penetration tests (SPT) performed at the Texas A&M University's National Geotechnical Experimental Site are used for training and testing artificial neural networks. The developed neural network is shown able to predict the SPT N-values of the site studied. Data are then generated for constructing the profiles of the N-values using the trained neural network. The study shows that the potential of neural networks in site characterization is significant.