Author ORCID Identifier

https://orcid.org/0009-0001-8553-3660

Date Available

11-19-2025

Year of Publication

2025

Document Type

Master's Thesis

Degree Name

Master of Science in Mining Engineering (MSMIE)

College

Engineering

Department/School/Program

Mining Engineering

Faculty

Steven Schafrik

Faculty

Zach Agioutantis

Abstract

Subsidence prediction is a critical field in mining engineering, focusing on anticipating ground movements caused by underground coal extraction. Accurately forecasting these movements is essential for protecting surface structures and infrastructure from damage. However, despite extensive research by mining professionals worldwide, reliable prediction remains challenging, especially in complex environments like subcritical mining areas and locations characterized by highly variable topography. These conditions introduce non-linear rock mechanics that often complicate the application of traditional predictive models. This thesis utilizes measured subsidence data related to the extraction of two adjacent longwall panels in a deep coal mine in the eastern US to develop site-specific best-fit models for subsidence prediction. The Surface Deformation Prediction System (SDPS) software package was used, which employs the influence function method. Analyses were conducted both for final and dynamic conditions. Prediction and measurement values are compared using the root mean square error and the relative root mean square error. The development of site-specific models was impacted by measured upsidence values due to the valley effect. In addition, subsidence movements over an already mined panel continue during mining of the neighboring panel due to the highly subcritical nature of the operation.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2025.519

Funding Information

  • Office of Surface Mining Reclamation and Enforcement
  • S24AC00036
  • May/2024-August/2024.

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