## 3 Highlights and Key Contributions of Published Articles
### Computer Technologies  
The emergence of advanced computer technologies has facilitated the study of these complex rock mechanics issues. This topic introduces various numerical methods and artificial intelligence techniques.  
[PERSON] et al. (contribution 16) addressed the complex mechanical characteristics of the Xiyu conglomerate, which impact the resistance and deformation of surrounding rock in water diversion tunnel construction. An improved SegFormer framework was developed for detecting mesoscale geomaterial structures, using CT scan images of the Xiyu conglomerate to create a high-precision numerical model. The proposed algorithm outperformed UNet, HRNet, and the original SegFormer in segmentation tasks. The segmentation results were used to calculate porosity, and biaxial compression simulations were conducted using particle flow code (PFC) to observe failure processes and determine shear strength. The study also explored the anisotropy of the Xiyu conglomerate from a microstructural perspective, providing a micro-observation basis for developing an anisotropic mechanical model.  
[PERSON] et al. (contribution 17) introduced an intelligent technique combining a genetic algorithm (GA) and long short-term memory (LSTM) to predict airflow temperature and relative humidity (RH) in underground mines, enhancing air conditioning and ventilation control. After collecting 21 features and over 200 data points, principal component analysis (PCA) was used to reduce dimensionality, and GA optimized the LSTM and PCA-LSTM models. The PCA-LSTM-GA model outperformed single LSTM and hybrid LSTM-GA models in predicting end-point air temperature (Tfout) and humidity (RHout). Tfout is mainly influenced by the surrounding rock temperature and inlet air temperature, while RHout is affected by rock temperature in the water trickling section, inlet air temperature, and water temperature/flow rate. This model aids in co-mining coal and geothermal resources and controlling thermal hazards.  
[PERSON] et al. (contribution 18) focused on the mechanical behavior of block-in-matrix materials, which is significantly influenced by block content. Traditionally, the areal block proportion (ABP) is determined through visual analysis of digital imagery. However, with advancements in computer vision, models can now extract detailed information from these images. The study by [PERSON] et al. compares classical feature-detection algorithms with cutting-edge models to automatically calculate the ABP from images of surface and underground outcrops. It introduces a framework based on the Segment Anything Model (SAM), which achieves human-level performance in ABP calculation, as validated by 32 experts. This approach reduces human bias in estimating the mechanical properties of block-in-matrix materials and addresses technical issues arising from the mischaracterization of rock block quantities and dimensions. The methodology integrates ABP estimation with rock matrix property assessments and other characterization techniques to mechanically characterize these materials. This comprehensive approach was applied to model Roman gold-mining strategies at an archeological site in NW Spain, using a 2D finite element numerical model to explain the mining techniques.  
[PERSON] et al. (contribution 26) used custom Python scripts and finite element (FEM) numerical models to determine the mechanical parameter values of the rock layers in an advancing mining front. They also introduced a modification to assess the errors in the estimated parameter values. Utilizing mining, geological, and measurement data, a numerical analysis was conducted for the Piast-Ziemowit mining area in Poland. Our results indicate that the accurate calibration of mechanical parameters is crucial for reliable predictions of surface deformation. The proposed method enhances the precision of numerical models, providing a more robust framework for assessing the impact of mining activities on rock layers.