Prediction of Shear Wave Velocity on Sand Using Standard Penetration Test Results : Application of Artificial Neural Network Model

Abstract

Although shear wave velocity (V s) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand Vs based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value (N 60), water content, fine content, specific gravity for input data and V s for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the Vs. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on V s is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

Prediction Models Based on Regression and Artificial Neural Network for Moduli of Layers Constituted by Open-Graded Aggregates

“…It was attempted to evaluate the moduli of open-graded aggregates based on either ANN or Linear Regression [ 45 , 46 , 47 , 48 ], in order to see whether the moduli can be predicted well from aggregate and compaction information. MATLAB [ 49 ] and MS Excel [ 50 ] were used to implement ANN and LR, respectively.…”

“…Of these, an ANN operates in a manner similar to a neural network structure in which multiple neural cells connected to each other share signals and there is no direct connection between the input layer and the output layer. An ANN is divided into an input layer, which receives data as inputs, hidden layers that represent a complex relationship between the input and output, and the output layer, which produces the final result ( Figure 10) [47,48,51]. The zone of influence of PLT is known as 1.5 to 2 times the diameter of the plate in the literatures based on experimental and numerical studies [41,42].…”