In the concurrent extraction of coal and gas, the quantitative assessment of evolving characteristics in mining-induced fracture networks and mining-enhanced permeability within coal seams serves as the cornerstone for effective gas extraction. However, representing mining-induced fracture networks from a three-dimensional (3D) sight and developing a comprehensive model to evaluate the anisotropic mining-enhanced permeability characteristics still pose significant challenges. In this investigation, a field experiment was undertaken to systematically monitor the evolution of borehole fractures in the coal mass ahead of the mining face at the Pingdingshan Coal Mining Group in China. Using the testing data of borehole fracture, the mining-induced fracture network at varying distances from the mining face was reconstructed through a statistical reconstruction method. Additionally, utilizing fractal theory, a model for the permeability enhancement rate (PER) induced by mining was established. This model was employed to quantitatively depict the anisotropic evolution patterns of PER as the mining face advanced. The research conclusions are as follows: (1) The progression of the mining-induced fracture network can be classified into the stage of rapid growth, the stage of stable growth, and the stage of weak impact; (2) The PER of mining-induced fracture network exhibited a typical progression that can be characterized with slow growth, rapid growth and significant decline; (3) The anisotropic mining-enhanced permeability of the reconstructed mining-induced fracture networks were significant. The peak PER in the vertical direction of the coal seam is 6.86 times and 4446.38 times greater than the direction perpendicular to the vertical thickness and the direction parallel to the advancement of the mining face, respectively. This investigatione provides a viable approach and methodology for quantitatively assessing the anisotropic PER of fracture networks induced during mining, in the concurrent exploitation of coal and gas. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

Open-pit mining seriously damages the original vegetation community and soil layer and disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental awareness and the influence of related policies, the ecological restoration of open-pit mines has been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and small scale of single mines. However, the carbon sequestration capability of mines after ecological restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current status were determined through field investigation, the methods for sampling and data collection for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting of three aspects and nine evaluation indexes was established by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following: the establishment of the judgment matrix, calculation of the index weight, determination of the membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation results of the restoration mines were determined with the 'excellent, good, normal and poor' grade classification according to the evaluation standards for each index proposed considering the data of the field investigation and laboratory tests. The results indicated that (1) the evaluation results of the mines' carbon sequestration capacity were of excellent and good grade at a proportion of 62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547, indicating the stability of the system is the main factor in the carbon sequestration capacity of the mines and the sustainability of the vegetation community and the stability of soil fixation on the slope. The proposed evaluation system effectively evaluates the short-term carbon sequestration capability of the restoration mining system according to the visual effects and the laboratory testing results, objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative analysis. The evaluation method is relatively applicable and reliable for restoration mines and can provide a reference for similar ecological restoration engineering.

The buried prestressed concrete cylinder pipe (PCCP) in mountainous areas easily suffers from geological disasters such as falling rocks, which result in leakage, pipe explosion and other structural safety issues. According to engineering practice, a rock-soil-pipe finite element model of buried PCCPs impacted by spherical falling rocks is established in this paper. The PCCP structural response to the impacts of falling rocks under different parameter settings is calculated. Taking the maximum plastic damage value of PCCP concrete cores as the evaluation index, an intelligent evaluation model of PCCP structural safety under falling rock impacts is built based on the Bayesian Optimization-Long Short-Term Memory (BO-LSTM) model. The constructed model is used to quickly predict the damage caused by falling rocks to the PCCP structure after the disaster. Meanwhile, the model provides support for the implementation of subsequent engineering measures. The study results show that the BO-LSTM-based intelligent evaluation model can accurately predict the PCCP structural damage caused by falling rock impacts, and it has practical application value.