VTechWorks staff will be away for the winter holidays starting Tuesday, December 24, 2024, through Wednesday, January 1, 2025, and will not be replying to requests during this time. Thank you for your patience, and happy holidays!
 

Enhancing Mine Subsidence Prediction and Control Methodologies for Long-Term Landscape Stability

Simple item page
dc.contributor.authorAndrews, Kevinen
dc.contributor.committeechairKarmis, Michael E.en
dc.contributor.committeememberAgioutantis, Zachariasen
dc.contributor.committeememberWestman, Erik C.en
dc.contributor.committeememberKarfakis, Mario G.en
dc.contributor.departmentMining and Minerals Engineeringen
dc.date.accessioned2014-03-14T20:40:42Zen
dc.date.adate2008-08-01en
dc.date.available2014-03-14T20:40:42Zen
dc.date.issued2008-06-23en
dc.date.rdate2008-08-01en
dc.date.sdate2008-06-27en
dc.description.abstractPrediction and control methodologies for ground deformation due to underground mining (commonly referred to as mine subsidence) provide engineers with the means to minimize negative effects on the surface. Due to the complexity of subsidence-related movements, numerous techniques exist for predicting mine subsidence behavior. This thesis focuses on the development, implementation, and validation of numerous enhanced subsidence prediction methodologies. To facilitate implementation and validation, the improved methodologies have been incorporated into the Surface Deformation Prediction System (SDPS), a computer program based primarily on the influence function method for subsidence prediction. The methodologies include dynamic subsidence prediction, alternative model calibration capability, and enhanced risk-based damage assessment. Also, the influence function method is further validated using measured case study data. In addition to discussion of previous research for each of the enhanced methodologies, a significant amount of background information on subsidence and subsidence-related topics is provided. The results of the research presented in this thesis are expected to benefit the mining industry, as well as initiate ideas for future research.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-06272008-153059en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06272008-153059/en
dc.identifier.urihttp://hdl.handle.net/10919/33782en
dc.publisherVirginia Techen
dc.relation.haspartMicrosoftWord-ThesisFinalSubmitJuly2008.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectsubsidence predictionen
dc.subjectrisk-based damage analysisen
dc.subjectlong-term stabilityen
dc.subjectdynamic subsidenceen
dc.subjectground strainen
dc.subjectground deformationen
dc.subjectsubsidenceen
dc.subjectmine subsidenceen
dc.titleEnhancing Mine Subsidence Prediction and Control Methodologies for Long-Term Landscape Stabilityen
dc.typeThesisen
thesis.degree.disciplineMining and Minerals Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
MicrosoftWord-ThesisFinalSubmitJuly2008.pdf
Size:
35.03 MB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses