Analytical, Numerical and Big-Data-Based Methods in Deep Rock Mechanics

With the increasing requirements for energy, resources, and space, numerous rock engineering projects (e.g., mining, tunnelling, underground storage, and geothermal and petroleum engineering) are more often being constructed and operated in large-scale, deep underground, and complex geology environm...

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Other Authors: Wang, Shaofeng (Editor), Cai, Xin (Editor), Zhou, Jian (Editor), Song, Zhengyang (Editor), Li, Xiaofeng (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
SPH
RF
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700 1 |a Wang, Shaofeng  |4 oth 
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245 1 0 |a Analytical, Numerical and Big-Data-Based Methods in Deep Rock Mechanics 
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520 |a With the increasing requirements for energy, resources, and space, numerous rock engineering projects (e.g., mining, tunnelling, underground storage, and geothermal and petroleum engineering) are more often being constructed and operated in large-scale, deep underground, and complex geology environments. Meanwhile, more and more unconventional rock failures and rock instabilities (e.g., rockbursts, large-scale collapses, and mine earthquakes) are occurring and severely threatening the safety of underground operations. It is well-recognized that rocks have multiscale structures from minerals, particles, fractures, fissures, joints, and stratification to faults and involve multiscale fracture processes. In the deep earth, rocks are commonly subjected to complex high-stress and strong-dynamic disturbances simultaneously. In addition, there are many multiphysics coupling processes, such as the coupled thermo-hydromechanical interaction in fractured porous rocks. It is still difficult to understand rock mechanics and to characterize rock behaviors with complex stress conditions, multiphysics processes, and multiscale changes. The primary aim of this Special Issue is to bring together original research discussing innovative efforts on analytical, numerical, and big-data-based methods in rock mechanics. It includes 25 manuscripts that illustrate the richness and challenging nature of deep rock mechanics. 
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653 |a conformal mapping 
653 |a triangle interpolation 
653 |a stress analytical solution 
653 |a DEM numerical simulation 
653 |a laterally loaded pile 
653 |a p-y curve 
653 |a soil-pile interface 
653 |a Drucker-Prager model 
653 |a mine slope 
653 |a stability classification of rock mass 
653 |a extremum entropy 
653 |a variable weight theory 
653 |a matter-element extension 
653 |a grade variable 
653 |a polymer 
653 |a diffusion model 
653 |a crack 
653 |a expansion ratio 
653 |a grouting amount 
653 |a rockburst classification 
653 |a data-driven approach 
653 |a random forest 
653 |a beetle antennae search algorithm 
653 |a high concrete face rockfill dam 
653 |a geosynthetic-reinforced soil structures 
653 |a generalized probability density evolution method 
653 |a seismic performance 
653 |a reliability analysis 
653 |a numerical methods 
653 |a deep rock mechanics 
653 |a rock damage judgment criteria 
653 |a SPH 
653 |a blind shaft cutting blasting 
653 |a coal 
653 |a deterioration characteristics 
653 |a chemical-stress coupling factor 
653 |a damage constitutive model 
653 |a rock burst 
653 |a t-SNE 
653 |a unsupervised learning 
653 |a supervised learning 
653 |a XGBoost 
653 |a true triaxial compression test 
653 |a acoustic emission 
653 |a b value 
653 |a rock drillability 
653 |a DPM parameters 
653 |a regression analysis 
653 |a RF 
653 |a GA-SVM 
653 |a UCS prediction model 
653 |a fractured rock mass 
653 |a uranium-containing solution 
653 |a multifield coupling 
653 |a reactive transport 
653 |a rough-walled fracture 
653 |a lithology 
653 |a concrete part 
653 |a mechanical and damage behaviors 
653 |a rockburst prediction 
653 |a deep forest 
653 |a bayesian optimization 
653 |a ensemble model 
653 |a zonal disintegration 
653 |a jointed rock mass 
653 |a stress redistribution 
653 |a strength reduction 
653 |a numerical simulation 
653 |a genetic algorithm 
653 |a BP neural network 
653 |a smooth wall blasting 
653 |a parameter optimization 
653 |a deep mining 
653 |a mining disturbance 
653 |a stress evolution 
653 |a brittle-ductile transition 
653 |a backfilled stopes 
653 |a fractured aquifer 
653 |a Bingham slurry 
653 |a grout diffusion model 
653 |a slurry diffusion distance 
653 |a grouting effect 
653 |a submarine slides 
653 |a submarine pipelines 
653 |a copula function 
653 |a reliability 
653 |a slide-pipeline interaction 
653 |a strain rate 
653 |a temperature effect 
653 |a mechanical properties 
653 |a energy dissipation features 
653 |a failure modes 
653 |a fault 
653 |a water inrush 
653 |a mechanical behavior 
653 |a mining advancing direction 
653 |a mind evolutionary algorithm 
653 |a MEA-BP model 
653 |a rock mechanical parameters 
653 |a orthogonal test method 
653 |a heat-concentrated source 
653 |a optimization of heat source location 
653 |a temperature gradient 
653 |a closed-form solution 
653 |a temperature difference 
653 |a n/a 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/6566  |7 0  |z DOAB: download the publication 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/96621  |7 0  |z DOAB: description of the publication