Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

AbstractStandard Penetration Test (SPT) and Cone Penetration Test (CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design. Numerous soil parameters are related to the SPT N-value. In contrast, CPT is becoming more popular for site investigation and geotechnical design. Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values. A back-propagation artificial neural network (ANN) model was developed to predict the N60-value from CPT data. Data used in this study consisted of 109 CPT-SPT pairs for sand, sandy silt, and silty sand soils. The ANN model input variables are: CPT tip resistance (qc), effective vertical stress (σv′), and CPT sleeve friction (fs). A different set of SPT-CPT data was used to check the reliability of the developed ANN model. It was shown that ANN model either under-predicted the N60-value by 7–16% or over-predicted it by 7–20%. It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy