The resilient modulus (M_R) is a key mechanical parameter in geotechnical engineering, but conventional laboratory measurement is time-consuming and labor-intensive. Deep learning models provide an alternative for predicting M_R using easily obtainable soil properties, yet their performance is often limited by the small size of available datasets. To address this limitation, this study develops an interpretable data-enhanced deep learning framework for M_R prediction. In the proposed framework, a multilayer perceptron (MLP) is adopted as the base prediction model, a generative adversarial network (GAN) is used to generate synthetic samples from the limited training data, Optuna is employed for hyperparameter optimization, and Shapley Additive Explanations (SHAP) are adopted to interpret the trained model. Random Forest (RF) and extreme gradient boosting (XGBoost) models optimized by Optuna are also introduced for performance comparison. The results show that the GAN-generated samples are generally consistent with the original data in terms of distribution characteristics and correlation patterns. Compared with the Optuna-MLP model without GAN-based data augmentation, the proposed GAN-Optuna-MLP model improves the testing R² from 0.87 to 0.93, reduces the mean absolute error (MAE) from 3.62 to 2.72 MPa, and decreases the mean absolute percentage error (MAPE) from 7.61% to 5.70%. The proposed model also achieves comparable performance to the optimized RF and XGBoost models, with slightly lower error-based indicators. In addition, SHAP analysis identifies cone tip resistance as the most influential variable for M_R prediction. These findings suggest that the integration of data augmentation, automated optimization, and interpretability analysis offers a practical solution for small-sample soil property prediction.

In order to understand the disturbance and deformation characteristics of clay during the penetration of soil sampler, the novel transparent clay made by Aristoflex AVC and the particle image velocimetry (PIV) technique were used to carry out the penetration tests of soil sampler with different sizes through the visual model test device. The test results show that the deformation distribution characteristics of soil in different sections are nearly the same, and the deformation decreases as it distances away from the tube wall. The soil at the lower part of the sampler has good integrity, and the shear strain of the soil within about 0.19 times the core diameter from the central axis of sampler is small, which is suitable for doing the laboratory experiment. Sampler penetration disturbance is mainly caused by the frictional resistance of the sidewall and the squeezing effect of edged foot, so increasing the inner diameter of soil sampler can effectively reduce the compressive deformation of soil in the sampler.