AI Article Synopsis

  • * A soil-water coupling prediction model has been developed to forecast land subsidence based on factors like groundwater level decline, stratum structures, and stress changes, validated against real data.
  • * This model is adaptable for different regions and can help predict subsidence in areas without monitoring data, making it a valuable tool for addressing groundwater-related problems.

Article Abstract

Due to the overexploitation of deep groundwater, the largest cone of depression in the world has formed in the North China Plain. This led to severe geological hazards, including land subsidence and ground fissures, and also caused economic losses. The prevention and treatment of subsidence needs to rely on the accurate prediction of subsidence amount. According to the one-dimensional consolidation theory and effective stress principle, combined with stratum structure, groundwater flow, stress distribution, and so forth, the high-pressure consolidation test results of 569.6 m deep borehole soil samples are adopted; with a specific focus on stress and deformation parameters under exploitation of groundwater condition, the soil-water coupling prediction model of groundwater level lowering depth and land subsidence has been established. Verification with measured subsidence data near the study sites demonstrated that the predicted curve is consistent with the measured one and the differences between them are acceptable. The model can be applied in different areas after making adjustment based on different regional stratigraphic structures. Its key advantage lies in the ability to provide land subsidence prediction for areas lacking monitoring data, making it highly valuable for widespread application. PRACTITIONER POINTS: There is a compressible stratum structure; it is the internal factors of land subsidence. The groundwater level decline causes the soil body stress to change. It is land subsidence of the external factors. Based on the one-dimensional consolidation theory and by combining stratigraphic structures, groundwater flow, and stress distribution, a ground settlement prediction model was established.

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Source
http://dx.doi.org/10.1002/wer.11111DOI Listing

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