Investigation of Mining Subsidence Prediction Under Tectonic Influences
Research output: Thesis › Doctoral Thesis
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2023. 177 p.
Research output: Thesis › Doctoral Thesis
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TY - BOOK
T1 - Investigation of Mining Subsidence Prediction Under Tectonic Influences
AU - Babaryka, Aleksandra
PY - 2023/12/12
Y1 - 2023/12/12
N2 - This dissertation addresses the challenge of predicting human-induced subsidence in tectonic settings. The study focuses on the non-symmetric and shape-defying nature of subsidence troughs in tectonic regions, which deviates from conventional symmetric models. The aim of the dissertation is to improve the accuracy of subsidence prediction by incorporating horizontal stress effects into empirical methods. Through a combination of numerical investigations and empirical modelling, the research reveals stress-induced patterns in subsidence profiles. The developed model, based on various concepts, successfully incorporates asymmetry and shape deviation, resulting in significantly improved prediction accuracy. Application of the model to a real subsidence case in a salt cavern shows a 30% improvement in prediction (based on mean squared error comparison with classical solution). This new solution covers subsidence profile patterns not previously considered by empirical models.
AB - This dissertation addresses the challenge of predicting human-induced subsidence in tectonic settings. The study focuses on the non-symmetric and shape-defying nature of subsidence troughs in tectonic regions, which deviates from conventional symmetric models. The aim of the dissertation is to improve the accuracy of subsidence prediction by incorporating horizontal stress effects into empirical methods. Through a combination of numerical investigations and empirical modelling, the research reveals stress-induced patterns in subsidence profiles. The developed model, based on various concepts, successfully incorporates asymmetry and shape deviation, resulting in significantly improved prediction accuracy. Application of the model to a real subsidence case in a salt cavern shows a 30% improvement in prediction (based on mean squared error comparison with classical solution). This new solution covers subsidence profile patterns not previously considered by empirical models.
KW - Senkung unter tektonischen Bedingungen
KW - Neue Methode zur Subsidenzvorhersage
KW - Asymmetrie
KW - Numerische Modellierung
KW - Subsidence under tectonic conditions
KW - New subsidence prediction method
KW - Asymmetry
KW - Shape deviation
KW - Numerical modeling
KW - Subsidence engineering
M3 - Doctoral Thesis
ER -