Abstract. This paper investigates the correlation between shear wave velocity and some of the index parameters of soils, including Standard Penetration Test blow counts (SPT), FineContent (FC), soil moisture (W ), Liquid Limit (LL), and Depth (D). The study attempts to show the application of arti cial neural networks and multiple regression analysis to the prediction of the shear wave velocity (V S ) value of soils. New prediction equations are suggested to correlate VS with the mentioned parameters based on a dataset collected from Mashhad city in the north east of Iran. The results suggest that, in the case of ANN method use, highly accurate correlations in the estimation of VS are acquired. The predicted values using ANN method are checked against the real values of V S to evaluate the performance of this method. The minimum correlation coe cient obtained in ANN method is higher than the maximum correlation coe cient obtained from the MLR. In addition, the value of estimation error in the ANN method is much less than that in the MLR method, indicating the role of higher con dence coe cient of the ANN in estimating VS of soil.

This study investigates the correlation between shear wave velocity (V S) and standard penetration test blow counts (SPT-N value) in all soil types, gravelly soil, sandy soil and clayey soil for Mashhad city in the north eastern region of Iran. The V S data used were measured through downhole method in 84 construction projects (in 88 boreholes). From among collected data, 424 pairs of V S-SPT recorded in one depth were used for regression analysis. The obtained results showed that the N-value had a critical role in estimating V S and soil type was less effective in this regard. These findings are in line with the findings of the previous studies. Regression equations obtained in this study were compared with correlations from previous studies. There was a relative similarity between the previously published and the new regression equations for Iranian soils. Although almost all correlations follow a similar trend, there are significant differences between new equations and correlations reported for other countries. The regression coefficients of the new equations show the acceptable capability of the suggested correlations in estimating V S through SPT-N value. Therefore, these equations can be used to estimate V S for the soils of the current study area and for other similar areas. Moreover, V S30 was used for site classification of the study area as per National Earthquake Hazard Reduction Program (NEHRP) guidelines and it was shown that a major portion of the city comes under site class C and other locations are categorised as site class D.