Geodetic monitoring of roads as a tool for determination of hazard zones in areas influenced by deep coal mining View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-08

AUTHORS

David Lamich, Marian Marschalko, Işık Yilmaz, Petra Bednářová, Dominik Niemiec, Václav Mikulenka, Karel Kubečka

ABSTRACT

The main objective of this study is to equip engineering geology with a new, independent tool of determination of zones of potential hazard in the linear direction in order to assess the influence induced by underground coal mining on future development. Currently, geodetic polygons, maintained by mining companies, are used to monitor subsidence and their evaluation results in how mining damage is assessed. However, the problem lies in the fact that the mining company is both the authority to monitor the conditions as well as the entity that compensates mining damage on buildings. The aspect of novelty in the paper is the proposed exploitation of regular geodetic monitoring of roads as an independent tool in the determination of zones of hazard in foundation engineering. The geodetic monitoring is carried out because of road maintenance in subsidence basins. However, so far, such monitoring has not been used to identify ground deformation parameters, on the basis of which building site categories could be determined and that could help to distinguish the zones of potential hazard before any buildings are constructed in subsidence basins. In the study area, a 9-year geodetic measuring identified radically altered conditions, i.e., from more suitable building site group (category V—87 %) in the actual year of monitoring to less suitable ones (categories I and II—34 %) actual year. This methodology was found to be valid generally and may be applied in areas with subsidence basins induced by deep coal mining worldwide. More... »

PAGES

1033-1044

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10064-015-0769-y

DOI

http://dx.doi.org/10.1007/s10064-015-0769-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012169596


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