# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 6 ANN Models
### ANN Models for Predicting Dry Density  
Soil dry density is used to determine the degree of compaction. The maximum dry density is very important to increase the soil ability to support the structure and to avoid excessive settlements. ANN models are developed to predict dry density using lab and field data independently and in combination. Based on the previous models for velocity this analysis is developed without validation. Models are developed with and without velocity as input to evaluate if velocity helps in predicting dry density. The statistics for the six different models are presented in Table 6.  
The best model to predict dry density from LL, plasticity index and water content using field data is Network 3_(4\(4\)2000)_1. The predicted versus actual graph for the best network shown in Figure 3a indicates that the accuracy of predicting dry density is good. The best ANN model developed to predict dry density with the same number of classified data and ranges but including seismic wave velocity into the input parameters is network 4_(3\(5\)100)_1. The predicted versus actual graph is shown in Figure 3b and the statistics indicate better accuracy in predicting dry density So, the results indicate that including seismic wave velocity improves the prediction of dry density. The best network using lab data to predict dry density from LL, plasticity index, and dry density is network 3_(4\(4\)500)_1. The predicted versus actual graphs are shown in Figure 3c and statistics indicate that the accuracy of predicting dry density is lower than the model derived from similar field data. Adding the velocity as additional input to traditional lab data results in network 4_(4\(4\)100)_1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Data** & **Model** & **Model Structure** & **ASE** & **MARE** & **R\({}^{2}\)** \\ \hline Field & without & 3\_(4\_4\_2000)\_1 & 0.0068 & 2.90 & 0.80 \\  & velocity & 4\_(3\_5\_100)\_1 & 0.0032 & 1.81 & 0.91 \\  & without & 3\_(4\_4\_500)\_1 & 0.0051 & 2.42 & 0.86 \\ Lab & velocity & 4\_(4\_4\_100)\_1 & 0.0056 & 2.52 & 0.85 \\  & without & 3\_(3\_11\_19800)\_1 & 0.0087 & 2.94 & 0.75 \\ Field plus Lab & velocity & 4\_(9\_9\_400)\_1 & 0.0077 & 3.00 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing ANN models (predicting dry density). The predicted versus actual graph for this network is shown in Figure 3d and the statistics shown in Table 6 indicate the addition of velocity in the lab data did not help to improve the results. The models for predicting dry density are better using field data than laboratory data. This is in contrast to the results for water content shown previously. Combining lab and field data to develop ANN models to predict dry density reduces the performance of the models. Network 3_(3_11_19800) \(1\) is for without the velocity. The predicted versus actual graph is shown in Figure 3e. The statistics indicate that the errors are higher and  
Figure 3: Graphical prediction accuracy of model for predicting dry density, (**a**) field, without velocity, (**b**) field, with velocity, (**c**) lab, without velocity, (**d**) lab, with velocity, (**e**) field plus lab, without velocity, (**f**) field plus lab, with velocity. the R\({}^{2}\) values are lower for this network in comparison to the networks built using lab data or field data. The predicted versus actual graph for the best network 4_(9\(9\)400)_1 including seismic wave velocity into the input parameters is shown in Figure 3f. The addition of velocity as input causes the MARE to increase but the R\({}^{2}\) is higher, and ASE is lower. This suggests a slight to insignificant improvement of the model after adding velocity as an input. In summary, models for predicting dry density are more accurate when using field data with velocity as an input. The model based on lab data is comparable to field data-based models without the additional of velocity. Combining the lab and field data results in much poorer models.