Reprint

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

Edited by
January 2023
432 pages
  • ISBN978-3-0365-5761-8 (Hardback)
  • ISBN978-3-0365-5762-5 (PDF)

This book is a reprint of the Special Issue Analytical, Numerical and Big-Data-Based Methods in Deep Rock Mechanics that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
Summary

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.

Format
  • Hardback
License
© by the authors
Keywords
complex variable method; conformal mapping; triangle interpolation; stress analytical solution; DEM numerical simulation; laterally loaded pile; p–y curve; soil-pile interface; Drucker–Prager model; mine slope; stability classification of rock mass; extremum entropy; variable weight theory; matter–element extension; grade variable; polymer; diffusion model; crack; expansion ratio; grouting amount; rockburst classification; data-driven approach; random forest; beetle antennae search algorithm; high concrete face rockfill dam; geosynthetic-reinforced soil structures; generalized probability density evolution method; seismic performance; reliability analysis; numerical methods; deep rock mechanics; rock damage judgment criteria; SPH; blind shaft cutting blasting; coal; deterioration characteristics; chemical-stress coupling factor; damage constitutive model; rock burst; t-SNE; unsupervised learning; supervised learning; XGBoost; true triaxial compression test; acoustic emission; b value; rock drillability; DPM parameters; regression analysis; RF; GA-SVM; UCS prediction model; fractured rock mass; uranium-containing solution; multifield coupling; reactive transport; rough-walled fracture; lithology; concrete part; mechanical and damage behaviors; damage constitutive model; rockburst prediction; deep forest; bayesian optimization; ensemble model; zonal disintegration; jointed rock mass; stress redistribution; strength reduction; numerical simulation; genetic algorithm; BP neural network; smooth wall blasting; parameter optimization; deep mining; mining disturbance; stress evolution; brittle-ductile transition; backfilled stopes; fractured aquifer; Bingham slurry; grout diffusion model; slurry diffusion distance; grouting effect; submarine slides; submarine pipelines; copula function; reliability; slide–pipeline interaction; strain rate; temperature effect; mechanical properties; energy dissipation features; failure modes; fault; water inrush; mechanical behavior; mining advancing direction; mind evolutionary algorithm; BP neural network; MEA-BP model; rock mechanical parameters; orthogonal test method; heat-concentrated source; optimization of heat source location; temperature gradient; closed-form solution; temperature difference; n/a