# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
## 4 Results and Analysis
### Limitations  
This study developed a novel ML model for predicting joint shear strength, achieving promising prediction results. It has fully demonstrated the applicability of ML techniques in joint shear strength prediction. Despite the positive findings, it is essential to recognize the limitations that should be addressed in future work. 1. ML techniques extract knowledge from input data in datasets and construct corresponding nonlinear functions for prediction [75]. Due to differing underlying mechanisms, the output results of the trained models may vary across different datasets. The hyperparameters of the models trained on different datasets are inherently distinct. Therefore, to ensure optimal predictive accuracy in practical scenarios, it is advisable to retrain the model individually for each specific dataset, taking into account the unique geological, mechanical, and statistical characteristics inherent in that dataset. Such tailored training allows the model to better capture domain-specific patterns and minimizes potential biases arising from data heterogeneity. 2. High-quality datasets are fundamental to ensuring the predictive accuracy of ML models [76]. Input features have an important influence on the prediction accuracy of ML models. The datasets collected in Section 3 can be enriched by introducing new data to improve the model's universality and prediction accuracy. It is worth mentioning that when combining data from different sources, it is necessary to pay attention to possible differences between measurements that may lead to bias or inconsistencies in the data. Additional features may need to be added to the combined dataset to ensure data consistency and integrity. In ML models, reasonable input feature selection and processing can significantly improve the performance of the model [77]. In addition, the size and diversity of the dataset are also important factors affecting the generalization ability of the model. Therefore, in the future, we plan to expand the dataset and improve the applicability of ML models by diversifying data sources and further optimizing input characteristics. 3. In subsequent studies, the application of this model to predict other properties of rock joints, such as shear stiffness and peak dilatancy angle, will be further explored. At the same time, the research scope will gradually expand to the engineering scale and will be combined with numerical simulation methods such as the simplified finite element model (FEM) [78] to evaluate the applicability and robustness of this method in a wider range of application scenarios. Therefore, it is necessary to combine more advanced machine learning algorithms to optimize model hyperparameters to improve the accuracy and generalization ability of model predictions. (4) The uncertainty inherent in ML models is another aspect that warrants attention. The main sources of uncertainty in this study include (i) data-level uncertainty caused by measurement errors, sampling bias, and the heterogeneity of geological conditions; (ii) algorithmic stochasticity stemming from random initialization and evolutionary processes during model optimization; (iii) sensitivity to hyperparameters and structural configurations of the hybrid ML model; and (iv) limited generalization capacity when applied to unseen or out-of-distribution datasets. To mitigate these uncertainties, future research should consider incorporating uncertainty quantification strategies--such as ensemble methods, bootstrap resampling, or Bayesian frameworks--to explicitly estimate and reduce the variance in model predictions. This will enhance the reliability and interpretability of the developed models when applied in practical engineering settings. In recent years, although the research on soft intelligence technology has been deepening, and the related theories and methods are becoming more and more mature, there are still many challenges in the application of these technologies in solving practical engineering problems [79].