Correlation and Neural Network Modeling of Shear Wave Velocity of Macau Soils Using SPT and CPT Data

Shear wave velocity of soil is an important parameter for earthquake analysis and civil engineering designs. Correlations between shear wave velocity and SPT-N values based on the simple power-law regression model were provided by previous studies. However, due to the inherent uncertainties of the SPT, estimation of shear wave velocity may be improved by incorporating other site investigation data in addition to SPT-N values. In this study, artificial neural networks were used for the correlation analysis between the shear wave velocity with SPT and CPT data, based on recent field test data obtained from the site investigation program of a major transportation project at Macau. The results of this study indicated that incorporating the additional soil parameters in the correlation model would improve the prediction performance. When combined with SPT-N and CPT-q_c to form a neural network model, a better prediction would be obtained than that using SPT-N or CPT-q_c alone.