## 3 Triaxial Compression Calibration Test and Verification
### Verification Tests for PartsNet  
After the confirmation of PartsNet for its accuracy, more intricate cases will be calibrated to demonstrate its general applicability in calibrating the EEPA constitutive relationship for elastoplastic wet sand. In practice, these force-displacement sequential data can be obtained from the above experimental instruments, while here, for simplicity, this paper uses the DEM simulated data generated by the DEM as ground truth. Nine sets of parameter combinations \(\lambda_{p}\), \(f_{s-s}\), \(f_{s-a}\), were sampled based on the Gaussian distribution, deliberately including design points beyond the scope of the original training dataset of PartsNet. Numerical experiments were conducted using EDEM 2022. Experimental data were then predicted using the pre-trained PartsNet to calibrate the corresponding parameters. The relative errors between the calibrated parameters and the true parameters are displayed in Table 3.  
Excellent accuracy is achieved by PartsNet in the prediction of parameters \(f_{s-s}\) and \(\lambda_{p}\), with the majority of relative errors for \(f_{s-s}\), which is the most weighted, being within 1%. Parameter \(f_{s-a}\), with a smaller weight and sharing some out-of-training-set design points with \(\lambda_{p}\), has relative errors exceeding 10% in three cases. Figure 12: Comparison between parameter calibration curve and triaxial test curve. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case** & \(\lambda_{p}\) & \(f_{s-s}\) & \(f_{s-a}\) & **Calibrated \(\lambda_{p}\)** & **Calibrated \(f_{s-s}\)** & **Calibrated \(f_{s-a}\)** & **Error 1** & **Error 2** & **Error 3** \\ \hline
1 & 0.660 & 0.471 & 0.501 & 0.672 & 0.495 & 0.427 & 1.87\% & 5.11\% & 14.86\% \\
2 & 0.730 & 0.544 & 0.385 & 0.719 & 0.549 & 0.366 & 1.50\% & 0.93\% & 4.97\% \\
3 & 0.658 & 0.515 & 0.405 & 0.627 & 0.513 & 0.427 & 4.70\% & 0.38\% & 5.39\% \\
4 & 0.614 & 0.463 & 0.378 & 0.6423 & 0.462 & 0.330 & 4.67\% & 0.15\% & 12.82\% \\
5 & 0.647 & 0.542 & 0.416 & 0.595 & 0.534 & 0.438 & 8.06\% & 1.39\% & 5.35\% \\
6 & 0.592 & 0.523 & 0.396 & 0.599 & 0.525 & 0.378 & 1.35\% & 0.46\% & 4.49\% \\
7 & 0.585 & 0.483 & 0.397 & 0.623 & 0.487 & 0.381 & 6.52\% & 0.84\% & 4.15\% \\
8 & 0.675 & 0.491 & 0.325 & 0.633 & 0.492 & 0.384 & 6.22\% & 0.17\% & 18.08\% \\
9 & 0.680 & 0.509 & 0.457 & 0.692 & 0.502 & 0.448 & 1.79\% & 1.44\% & 2.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The actual parameters and calibration parameters of 9 verification cases. Figure 13 illustrates the calibration results of PartsNet for the nine validation experimental datasets and compares them with the experimental force-displacement curves. The parameters calibrated by PartsNet accurately capture the EPEA constitutive relationship during the loading and unloading processes of the elastoplastic wet sand. The dynamic response obtained from EDEM 2022 exhibits good consistency with the experimental curves, reflecting the dynamic trends, peak load values, and post-unloading displacement values. The peak load values from the nine validation experiments are discretely distributed between 405.138 N and 678.339 N. In Figure 14, 5% of the experimental peak load is used as error bars to assess the calibration of peak load values. It was found that the results from PartsNet accurately match the experimental data, all falling within the confidence interval. This indicates that PartsNet maintains accuracy even under extreme conditions. The most precise peak fit and the least amount of error are found in the ninth dataset. These parameters, \(\lambda_{p,f_{s-s},f_{s-a}}\), determine the micromechanical behavior and macroscopic response of the sand. The simulation stress-strain curve is faithfully reproduced when the contact plasticity ratio \(\lambda_{p}\) is chosen accurately, capturing the subtleties of the material's elastic-plastic transition during contact.