Underground infrastructure of any kind can affect the surface by inducing ground movements. The ability of precise subsidence prediction is crucial for environmental management. Prediction methods in practice are mainly based on influence functions that are symmetrical and provide comparably smooth profiles. In the past, deviations from the predictions have been detected. The ability of modern geomonitoring data makes the deviation even more obvious today. One of the reasons for the deviations are the regional tectonic stress conditions. To justify further investigation into the impact of tectonic conditions on the subsidence parameters, numerical experiments were conducted based on a hypothetical case of a homogenous sedimentary rock under different stress conditions. As a result, deviation of up to 7% of the subsidence profile parameters was detected. The results can be considered significant and encourage researchers to investigate the topic further to extend the currently used prediction methods to take into count the tectonic conditions. The research is based on numerical simulation and provides only theoretical result, implementation and validation of the theory in the field are left for further investigation.
Mining-induced subsidence can have significant environmental and infrastructural impacts, making subsidence engineering a crucial consideration. However, the unique nature of salt caverns and the increasing demand for reliable subsidence prediction models in the context of energy storage require special attention. This study provides a comparative analysis of existing prediction models and highlights their advantages and disadvantages to determine the most appropriate approach. The study primarily focuses on theoretically developing an empirical influence function for asymmetrical subsidence prediction. It significantly contributes to the field by correcting and extending the existing method, providing a generalized solution applicable to any type of asymmetrical distribution around the cavern. Future research directions include implementing the proposed model in relation to real-world data. The insights gained from this study can help advance subsidence prediction models in the field of salt cavern energy storage, addressing a significant need in the industry.
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