Editorial for special issue on intelligent green mining

References

[1] Wu AX, Wang ZQ, Ruan ZE, Wu PJ. Advances in tailings thickening: A rheology-oriented review of theory and practice. Int. J. Miner. Metall. Mater., 2026, 3372098

[2] He W, Liu L, Li TT, et al. . Investigation of backfill creep characteristics under different stress levels and initial bearing ages and their effects on strength and permeability. Int. J. Miner. Metall. Mater., 2026, 3372114

[3] Lu HJ, Mou C, Liu L, et al. . Experimental investigation on mechanical properties of backfill-rock composite structure under in-situ mining and filling stress loading. Int. J. Miner. Metall. Mater., 2026, 3372129

[4] Quan WZ, Fall M. Effect of blast furnace slag and fly ash on autogenous self-healing in tailings-based cementitious composites. Int. J. Miner. Metall. Mater., 2026, 3372143

[5] Feng WC, Kang D, Li M, et al. . Optimization in the design of gangue and coal gasification slag-based backfill materials based on the response surface methodology. Int. J. Miner. Metall. Mater., 2026, 3372163

[6] Lyu HY, Cao XX, Shimada H, Sasaoka T, Hamanaka A. Coupled effects of sodium lignosulfonate and water-to-binder mass ratio on rheology, strength and microstructure of high-volume fly ash cemented paste backfill. Int. J. Miner. Metall. Mater., 2026, 3372178

[7] Chen X, Zhang JH, Wang YM, Miao SJ, Ji FC, Li GS. Study on the solidification strength of tailings backfill with different particle sizes induced by microbial mineralization under conventional mixing method. Int. J. Miner. Metall. Mater., 2026, 3372194

[8] Zhang C, Guo JP, Li TT, Song WD, Wang Y, Taheri A. Thermomechanical and microstructural characteristics of cemented backfill incorporating low-dosage polyvinyl chloride powder. Int. J. Miner. Metall. Mater., 2026, 3372206

[9] Wang ZR, Zhang JX, Jiang HQ, Cui L, Fu Y, Yilmaz E. Blast furnace slag-based binder as an eco-friendly and cost-effective material in cementitious mine backfill: Mechanisms, applications and future perspectives. Int. J. Miner. Metall. Mater., 2026, 3372220

[10] Ngo I, Ma LQ, Yang RZ, et al. . Carbon-negative backfill design: Dual functionality of aluminum nanoparticles in strength enhancement and CO₂ fixation. Int. J. Miner. Metall. Mater., 2026, 3372242

[11] Su B, Yue XT, Shao N, et al. . CO₂-driven structural transformation of steel slag into low-carbon cementitious materials for carbon-reducing mine backfill. Int. J. Miner. Metall. Mater., 2026, 3372255

[12] Tao H, Wang SM, Cui JP, Jia QF, Guo W, Zheng HH. CO₂ sequestration performance and microstructural response of a novel modified magnesium-coal-based all-solid waste backfill material. Int. J. Miner. Metall. Mater., 2026, 3372271

[13] Aslam A, Rasyid M, Rafiei A, Hassani F, Sasmito A. Investigation of microwave treatment in kimberlite beneficiation for enhanced separation efficiency and sustainable fine waste reduction. Int. J. Miner. Metall. Mater., 2026, 3372284

[14] Wang GF, Luo Y, Yang LK, Wang DD, Cheng J. Large artificial intelligence models for coal mining: Development, applications and challenges. Int. J. Miner. Metall. Mater., 2026, 3372300

[15] Patel HA, Verma S. Generative artificial intelligence in extractive metallurgy: Industrial applications, limitations and implementation pathways. Int. J. Miner. Metall. Mater., 2026, 3372313

[16] Yu TY, Zhu YM, Liu J, Han YX, Li YJ. Deep learning-driven autonomous spodumene flotation: Integrating theoretical modeling and YOLOv11-M vision systems with semi-industrial trial validation. Int. J. Miner. Metall. Mater., 2026, 3372338

[17] Li SB, Gu QH, Ruan SL, Jiang S. Event-level abnormal driving behavior detection in mining trucks based on temporal-aware vision-language models. Int. J. Miner. Metall. Mater., 2026, 3372356

[18] Li WX, Ma D, Gao XF, Liu QH, Zhang CJ, Wang BL. Geology-informed intelligent prediction of aquifer groutability for water-inrush control in mining with an interpretable hybrid deep learning framework. Int. J. Miner. Metall. Mater., 2026, 3372372

[19] Pan JL, Zhang QL, Ma CD, Han J, Feng Y. Image-based dominant fracture recognition for cloud-model blastability classification in underground stopes: Development and field validation. Int. J. Miner. Metall. Mater., 2026, 3372388

[20] Xu HC, Li K, Lai XP, et al. . Intelligent prediction method of rockburst driven by microseismic temporal features in deep near-vertical coal seams. Int. J. Miner. Metall. Mater., 2026, 3372403

[21] Chen JZ, Chen ZW, Liu ZX, Li XB. New insight into identification of rock Kaiser effect: A chaotic deep learning model and its application in deep mining. Int. J. Miner. Metall. Mater., 2026, 3372419

[22] Zhang BY, Wang XC, Ding F, et al. . Prediction model for indoor rock compression failure time based on ensemble learning and optimization algorithms. Int. J. Miner. Metall. Mater., 2026, 3372437

[23] Wang JX, Jiang HR, Wu SC, et al. . Intelligent prediction of deep rock strength based on modified three-dimensional Hoek-Brown criterion. Int. J. Miner. Metall. Mater., 2026, 3372454

RIGHTS & PERMISSIONS

University of Science and Technology Beijing