Semester

Spring

Date of Graduation

2014

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mining Engineering

Committee Chair

Keith A. Heasley

Committee Co-Chair

Christopher J. Bise

Committee Member

Gabriel S. Esterhuizen

Committee Member

Keith A. Heasley

Committee Member

Yi Luo

Committee Member

Brijes Mishra

Abstract

Over the years, the various versions of the ARMPS and the LaModel programs have been widely used in the U.S. coal fields. In a recent deep-cover database analysis, LaModel was found to more accurately classify a small deep-cover database than ARMPS. However, the LaModel analysis of each case study required several days to complete, as compared to a requirement of only several minutes for each ARMPS analysis. If the LaModel analysis of an ARMPS-type mine design, could be made as easily and quickly as an ARMPS analysis and calibrated against a large database, the potential exists to improve the quality of overall mine design.;In this research, a computer code (ARMPS-LAM) has been developed to effectively integrate the LaModel and ARMPS programs. This program takes the basic ARMPS input for defining the mining plan and then automatically develops, runs, and analyzes a full LaModel analysis to output the stability factor and other data for mine design analysis, all without further user input. This new program allows an ARMPS-type LaModel analysis to be developed and run in just a few minutes. The ARMPS-LAM program consists of three primary modules: pre-processing, numerical solution and post-processing. The pre-processing module includes the component subroutines to import data, develop and calibrate the laminated overburden model. The numerical solution module solves the laminated overburden model. The post-processing module can automatically extract and calculate the stability factor and output other important data. The program can also be run in batch mode to quickly analyze a large database.;To initially evaluate the ARMPS-LAM program, it was used to analyze the entire 645 case histories of the ARMPS database and compared to the ARMPS output. It was found that the ARMPS-LAM SF is generally about 8% higher than the ARMPS SF. Further, the laminated overburden model (as implemented in ARMPS-LAM) distributes less load on the AMZ for shallow cover (1,000 ft) than ARMPS.;Next, the ARMPS-LAM classification accuracy was determined. This analysis involved a multi-variable regression using the five most significant input variables of the ARMPS-LAM program (AMZ SF, depth, seam height, barrier pillar SF and pillar width-to-height ratio). Based on this statistical analysis, the ARMPS-LAM program was seen to be slightly more accurate than ARMPS by a factor of 72% versus 63%. In the future, this classification accuracy may yet increase with additional research on improving the accuracy of the calibrated laminated overburden model.;In this dissertation, the ARMPS-LAM program has been successfully developed and validated. In the future, it is planned to be implemented into ARMPS as an effective tool to help engineers perform pillar design, and the batch version will also serve as a platform to assist researchers in evaluating new ground control approaches.

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