ICGCM Papers:
Ground Control Design Tools
 
 
Calibrating LaModel for Subsidence
35th International Conference on Ground Control in Mining
Calibrating LaModel for Subsidence
by
Jian YangKeith A Heasley, West Virginia University, Morgantown, United States
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[Conference] 35th International Conference on Ground Control in Mining
[Price] Free  [Comments] 0
[Topical Area] Ground Control Design Tools
[Author] Jian YangKeith A Heasley, West Virginia University, Morgantown, United States
[Abstract] 
Key Conclusions:
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Four different empirical formulas relating the lamination thickness to the panel width-to-depth ratio were determine for the four cases of: subcritical or supercritical panels, with and without offsets. These new subsidence prediction formulas are being implemented into new material wizards in LaModel.
Key Findings:
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For optimum surface subsidence prediction, it was found that the overburden stiffness as defined by the laminations thickness and the gob convergence as defined by the final gob modulus were the two most critical parameters that needed to be calibrated. Using the WVU (Comprehensive and Integrated Subsidence Prediction Model) (CISPM) program as the best empirical subsidence curve, numerous LaModel runs were performed in order to find the values of lamination thickness and final gob modulus which minimized the least-square error between the CISPM and the LaModel subsidence curves.
Objective of the Paper:
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The objective of this paper is to develop a methodology for calibrating the critical input parameters in LaModel to produce the most accurate surface subsidence prediction.
Problem Statement:
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Up to this point, the material property wizards in LaModel were primarily designed for calculating accurate stress redistribution in single and multiple-seam situations and for investigating and optimizing pillar sizes and layouts in relation to overburden, abutment and multiple-seam stresses. However, the critical input parameters which will give the most accurate seam-level stress distribution do not necessarily produce the best surface subsidence prediction.