Estimation of SPT Results Using Probabilistic Method and Artificial Neural Network in clay layers (Case study: Tabriz Clayey layers)

In this study, the estimation of the results of the standard penetration test with probabilistic methods and artificial neural network using the physical and plasticity properties of clay layers with case study of clayey soils of Tabriz city have been performed. In this research, two machine boreholes were drilled at depths of up to 8 meters and standard penetration tests and other tests were carried out to determine all of the plasticity and physical properties on the prepared specimens. By using the results of experiments as well as available data, a database of 112 series of clay layer properties were prepared. The probabilistic ranges for each of the research variables including activity, moisture content, consistency index, passage percentage sieve 200 and plasticity index were proposed for estimating the corrected standard penetration by using the three sigma probabilistic analyses. The best correlation with the standard penetration based on the consistency index with the correlation slope is 1.015. Also, the artificial neural networks were studied in different states and with the different number of hidden neurons with all physical and plasticity properties including 11 input variables. The best results of the artificial neural network related to the hidden double layer with 10 and 5 hidden neurons that the determination coefficient and the root mean square error were equal to 0.805 and 0.063 at the test stage, respectively. The results of the neural network method have been statistically compared and more appropriate than the probabilistic method.