Book Details


Evolution, Monitoring and Predicting Models of Rockburst

Publication year: 2018

ISBN: 978-981-10-7548-3

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This open access book focuses on investigating predicting precursor information and key points of rockburst in mining engineering through laboratory experiment, theoretical analysis, numerical simulation and case studies. Understanding the evolution patterns for the microstructure instability of rock is a prerequisite for rockburst prediction. The book provides a guide for readers seeking to understand the evolution patterns for the microstrucure of rock failure, the predicting key point of rock failure and the rockburst predicting model. It will be an essential reference to understand mechanism of rockburst and sheds new light on dynamic disasters prediction. Chapters are carefully developed to cover (1) The evolution patterns for the microstructure instability of rock; (2) Rockburst hazard monitoring and predicting criterion and predicting models. The book addresses the issue with a holistic and systematic approach that investigates the occurrence mechanism of rockburst based on the evolution patterns for the microstructure of rock failure and establishes the predicting model of rockburst.

Subject: Open Access, AE experiment of rock under uniaxial cyclic load, unload, Three-dimensional reconstruction of fissured rock, Nonlinear dynamics evolution pattern of rock cracks, Construction of crack growth factor model, Construction of information entropy evolution model, predicting key points, Bayesian model for predicting rockburst, Fuzzy matter-element model for predicting rockburst, Tangent Damage Factor identification predicting of rockburst, Tangent Modulus identification predicting of rockburst, Time-space-frequency-energy evolution patterns of rock failure, Decreased information entropy value predicting of rockburst, Decreased b value predicting patterns of rockburst, Load, unload response ratio predicting of rockburst, Infrared radiation precursor predicting of rockburst, Cumulative apparent volume and MS rate predicting of rockburst, Case studies: predicting method of rockburst