Cover Image

Evolution, Monitoring and Predicting Models of Rockburst : Precursor Information for Rock Failure.

Bibliographic Details
Main Author: Wang, Chunlai.
Format: eBook
Language:English
Published: Singapore : Springer Singapore Pte. Limited, 2018.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Acknowledgements
  • About the Author
  • Contents
  • 1 Introduction
  • 1.1 The Overview of Rockburst
  • 1.2 Current Status on Rockburst Induced Conditions
  • 1.3 Current Status on the Precursor Characteristics of Rockbursts
  • 1.4 Current Status on the Evolution of Rockburst
  • 1.5 Current Status on Predicting of Rockburst
  • 1.5.1 Study on the Synergetic Monitoring of Rockburst
  • 1.5.2 Study on the Predicting Model of Rockburst
  • 1.5.3 Study on the Field Predicting of Rockburst Hazard
  • References
  • 2 Experimental Materials and Equipments
  • 2.1 Experimental Materials
  • 2.2 Experimental Equipments
  • 2.2.1 Laboratory Equipments
  • 2.2.2 Field Equipment
  • 2.3 SEM and EDS
  • Reference
  • 3 Mechanism and Predicting Theory-Based of Rockburst Evolution
  • 3.1 Introduction
  • 3.2 Mechanism of Rockburst
  • 3.3 Mechanism of Rockburst Stress-Energy Evolution
  • 3.4 Nonlinear Dynamic Theory of Predicting Rockburst
  • 3.4.1 Mutation Theory
  • 3.4.2 Damage Theory
  • 3.4.3 Load/Unload Response Ratio Theory
  • 3.4.4 Entropy Theory
  • 3.4.5 Fuzzy Matter Element Theory
  • 3.4.6 Bayesian Theory and Network Model
  • References
  • 4 Three-Dimensional Reconstruction Model and Numerical Simulation of Rock Fissures
  • 4.1 Introduction
  • 4.2 CT Scanning Experiment Under Uniaxial Cyclic Load/Unload
  • 4.3 Cracks Extraction and Calculation
  • 4.3.1 Processing the CT Images
  • 4.3.1.1 Characteristics of CT Images Information
  • 4.3.1.2 CT Image Processing Contents and Methods
  • 4.3.2 Calculating the Length and Area of Cracks
  • 4.3.2.1 Calculating the Length of Cracks
  • 4.3.2.2 Calculating the Area of Cracks
  • 4.4 Three-Dimensional Reconstruction Model and Numerical Simulation
  • 4.4.1 Establishing a Three-Dimensional Finite Element Model
  • 4.4.1.1 Establishing the Model of Fissured Rock.
  • 4.4.1.2 Three-Dimensional Reconstruction Model of Failured Sandstone
  • 4.4.1.3 Mesh Generation of Reconstructed Model
  • 4.4.1.4 Three-Dimensional Model Parameters
  • 4.4.2 Numerical Simulation of Fissured Rock
  • 4.4.2.1 Selection of Material Parameters
  • 4.4.2.2 Numerical Analysis of Model
  • References
  • 5 Experimental Investigation on Nonlinear Dynamic Evolution Patterns of Cracks in&amp
  • blank Rock Failure Process
  • 5.1 Introduction
  • 5.2 Pattern of Nonlinear Dynamics Evolution of Rock Cracks
  • 5.2.1 Fractal Theory
  • 5.2.2 Results Analysis on Fractal Dimension of Rock Cracks
  • 5.2.2.1 Calculated Procedure of Fractal Box Dimension
  • 5.2.2.2 Results Analysis on Fractal Dimension of Sandstone Cracks
  • 5.2.3 Entropy Model of Rock System Based on Fractal Dimension
  • 5.3 Chaotic Characteristics of Cracks Evolution in Rock Failure Process
  • 5.3.1 Chaos Theory
  • 5.3.2 Discrimination of Chaotic Characteristics Defined by Li-Yorke
  • 5.3.3 Construction of Crack Growth Factor Model
  • 5.4 Conclusion
  • References
  • 6 Experimental Investigation on AE Precursor Information of Rockburst
  • 6.1 Introduction
  • 6.2 Spatio-temporal Evolution Pattern of Rock Failure
  • 6.2.1 AE Experiment
  • 6.2.2 Principle of AE Event Location
  • 6.2.3 Spatio-temporal Distribution Characteristics of Rock Failure
  • 6.3 Characteristics of Relatively Quiet Period for Rock Failure
  • 6.3.1 Deformation and Failure Characteristics of Loaded Rock
  • 6.3.2 Relationship Between the Constitutive Characteristics and the Accumulative AE Counts
  • 6.3.3 Relationship Between AE Rate and Constitutive Characteristics
  • 6.3.4 Evolution Characteristics of AE Amplitude in the Time Domain
  • 6.3.5 Analyzation of Precursory Characteristics of Rock Failure
  • 6.4 Variation Regulation of AE Energy Parameters for Rock Failure
  • 6.4.1 Experimental Setup and Procedure.
  • 6.4.2 Energy Parameters Variations in Each Channel
  • 6.4.3 Relationship of Energy and Stress-Strain Curve on Time Domain
  • 6.4.4 Analysis of Damage Features
  • 6.4.5 Energy Release Rate of Rock Damage
  • 6.5 Evolution Patterns of Spatial-Temporal-Energy on Rock Fracture Surface
  • 6.5.1 Experimental Setup and Procedure
  • 6.5.2 Relationship Between Spatial Distribution of AE Events and Rock Fracture
  • 6.5.3 Spatial-Temporal Evolution of Strong AE Events on Rock Fracture
  • 6.5.4 Spatial-Temporal-Energy Evolution Model of Strong AE Events in Limestone Fracture
  • 6.5.5 Spatial Fractal Dimension Evolution Model of AE Events on Fracture
  • 6.6 Conclusion
  • References
  • 7 Experimental Investigations on Multi-means and Synergistic Prediction for Rockburst
  • 7.1 Introduction
  • 7.2 Predicting Points of Infrared Precursor for Coal Failure
  • 7.2.1 Laboratory Test of Coal Failure
  • 7.2.2 Theoretical Bases of IRR Detection
  • 7.2.3 IRT Characteristics of Coal Samples
  • 7.2.4 IRT Prediction for Rock Failure
  • 7.3 Experimental Investigation on Predicting Rock Failure Using Load/Unload Energy Response Ratio Theory
  • 7.3.1 LURR Characteristics of Coal Samples
  • 7.3.2 LURR Prediction of Coal Failure
  • 7.4 Experimental Investigation on Predicting Points Using Tangent Damage Factor for Rock Failure
  • 7.4.1 Laboratory Test of Rock Failure
  • 7.4.2 Define the TDF
  • 7.4.3 Damage Characteristic of Limestone Failure
  • 7.4.4 Predicting Models of TDF
  • 7.5 Experimental Investigation on Predicting Points Using Information Entropy Theory for Rock Failure
  • 7.5.1 AE Dominant Frequency
  • 7.5.2 AE Dominant Frequency Entropy
  • 7.5.3 AE Dominant Frequency and Entropy
  • 7.6 Predicting Key Point Identification Using Traditional Monitoring Method
  • 7.6.1 Methods for Instrument Installation and Data Collection
  • 7.6.2 Recognition Method of Predicting Hazards.
  • 7.7 b Value Characteristics with MS Activity in Deep Mining
  • 7.7.1 The Magnitude-Frequency Relation-b Value
  • 7.7.2 Changes of b Value Caused by Rockmass Excavation
  • 7.8 Identification of Predicting Key Point Using AE/MS Monitoring System
  • 7.9 Model of Multi-means and Synergistic Prediction for Rockburst
  • 7.10 Conclusion
  • References
  • 8 Predicting Model of Rockburst Based on Nondeterministic Theory
  • 8.1 Introduction
  • 8.2 Predicting Model of Rockburst Based on Bayesian Theory
  • 8.2.1 An Overview of Bayesian Theory
  • 8.2.1.1 Empirical Probability
  • 8.2.1.2 Mean Values and Covariance
  • 8.2.1.3 Empirical Discriminant
  • 8.2.1.4 Posterior Probability and Verification
  • 8.2.2 Key Factors of Rockburst Tendency
  • 8.2.2.1 Induced Factors of Rockburst
  • 8.2.2.2 Critical Factors
  • 8.3 Predicting Model of Rockburst Based on Fuzzy Matter-Element Theory
  • 8.3.1 Fuzzy Matter-Element and Composite Fuzzy Matter-Element
  • 8.3.2 Standard Fuzzy Matter-Element and Difference Square Composite Fuzzy Matter-Element
  • 8.3.3 Weight Coefficients Determined by Entropy Method
  • 8.3.4 Closeness Degree and Comprehensive Evaluation
  • 8.4 Conclusions
  • References
  • 9 Field Case
  • 9.1 Design of Microseismic Monitoring System in Huize Lead-Zine Mine
  • 9.1.1 Main Influence Factor
  • 9.1.2 Microseismic Monitoring System
  • 9.1.3 Sensor Arrangement Design
  • 9.2 Case Verification
  • 9.2.1 Predicting Case Based on Displacement Nephogram
  • 9.2.2 Predicting Case Based on Apparent Stress
  • 9.3 Predicting Key Points Identification of Dynamic Hazard
  • 9.3.1 Dynamic Hazard Predicting Using Routine Monitoring
  • 9.3.2 Predicting Key Point Identification Using CAV and MS Events Activity
  • 9.4 Predicting Model of Rockburst Based on Bayesian Theory
  • 9.4.1 Training Samples
  • 9.4.2 Predicting Rockburst Tendency Using a Bayesian Model.
  • 9.4.3 Verifying Accuracy of Bayesian Model
  • 9.5 Predicting Model of Rockburst Based on Fuzzy Matter Element Theory
  • 9.5.1 Testing and Field Condition
  • 9.5.1.1 Mining Conditions and Method
  • 9.5.1.2 Laboratory Testing
  • 9.5.1.3 Stress Estimation
  • 9.5.2 Predicting Rockburst Tendency
  • 9.5.2.1 Predicting Rockburst Tendency Using Traditional Method
  • 9.5.2.2 Predicting Rockburst Tendency Using the Proposed Model
  • 9.5.3 Predicting Model of Fuzzy Matter-Element Theory
  • 9.5.3.1 Comparison and Analysis of Predicting Model
  • 9.5.3.2 Data Analysis and Discussions
  • 9.6 Conclusion
  • References.

Similar Items