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|The increased impact of mining subsidence in the U.S. coalfields and the recent regulatory constraints legislated by government and state agencies have provoked an intense research effort on this topic by government agencies, coal companies and academic institutions. Subsidence and horizontal strain predictions are a basic requirement if subsidence and its associated effects are to be controlled and contained within acceptable environmental levels. As a result, several such prediction techniques have been proposed or are currently under development to assist coal operators in minimizing the detrimental effects of these surface deformations. VPI&SU has been actively involved in this research effort in the past few years, with particular emphasis on the Appalachian coalfield. During the initial stages of this research a large number of subsidence case studies were collected from the literature, the coal industry and government agencies. In total, data from 32 longwall panels and 60 room and pillar panels were collected. This data bank was used to determine relationships between the basic mining and subsidence parameters, in order to allow the evaluation of the various prediction methods for the Appalachian coal region. Based on this data two subsidence prediction techniques, one based on profile functions and the other on influence functions have been developed. Profile function methods provide a tool for a fast calculation of subsidence on a line across a mine panel. Influence function methods, on the other hand, allow for a more detailed calculation of ground movements, in any mode and at any depth, on a grid or at any specific point. The limitations of the data of the collected case studies, i.e. accuracy of surveys, frequency of monitoring, lack of horizontal movement measurements, etc, led VPI&SU to the initiation of a detailed subsidence and strain monitoring program above a number of active mines, located in three major coal producing counties of Virginia. The aim of this program was to enhance the data base with accurate and complete measurements of surface movements and to allow, therefore, the refinement of the prediction techniques. This paper presents this monitoring program, discusses in detail the results obtained from three specific case studies and provides recommendations for the implementation of the field data in the general prediction methods.|