Research from the Cangzhou area of China highlights the challenges posed by groundwater over-exploitation, which has led to significant land subsidence threatening urban infrastructure and public services. With nearly 80% of the region's water sourced from groundwater, the continual extraction has resulted in the ground sinking, leaving experts scrambling for predictive models to mitigate risks associated with this phenomenon.
Land subsidence, largely driven by the compression of clayey soil layers, reached its peak severity in 2017, where extensive areas experienced annual subsidence rates exceeding 50 millimeters. To counter this alarming trend, the local government has attempted remedial measures since 2005, limiting groundwater extraction and improving surface water supplies. While these initiatives have shown promise, substantial challenges remain, particularly with areas still experiencing rapid subsidence.
The objective of the highlighted study was to develop the NCE_CS nonlinear creep model, aimed at improving the accuracy of predicting soil behavior under varying stress conditions. Through extensive creep tests on clayey soil spectrums buried at depths between 65.7 to 121.7 meters, the study successfully illustrated how soil deformation could be characterized by instantaneous strain, primary consolidation strain, and subsequent creep strain over time.
Findings indicate clear nonlinear characteristics within the creep curves of the clay specimens, demonstrating how creep deformation tends to increase over time but at decreasing rates. This nonlinear behavior is particularly important for constructing accurate predictive models for land subsidence.
By employing the Boltzmann superposition principle, researchers established well-defined creep curves during their tests, enabling the formulation of the NCE_CS model, which articulates how primary consolidation contributes significantly to the delayed aspects of soil creep. This innovative model encompasses five parameters derived using genetic algorithms, providing insights not previously achievable with classic rheological models.
Through rigorous testing and model optimization, the authors report the NCE_CS model's results align remarkably well with observed data. Specifically, test comparisons indicate the model effectively captures the non-linear development trend characteristic of creep behavior, outperforming four traditional classical models, namely the Kelvin, Maxwell, Merchant, and Burgers models.
Significantly, the validated NCE_CS model showed remarkable efficacy when applied to another subsidence region, Renqiu City, indicating its potential as a generalized tool across various impacted areas of Cangzhou.
The clarity brought forth by NCE_CS, particularly its ability to delineate the onset time of creep stages during the consolidation, establishes it as an invaluable resource for future studies aimed at addressing land subsidence. With such predictive accuracy, the model offers insights not only for Cangzhou but can be adapted for broader applications concerning urban planning and groundwater management globally amid worsening climate conditions.
Overall, the research sets the groundwork for mitigating the impacts of land subsidence by enhancing our predictive capabilities concerning clayey soil responses under varying stress, reinforcing the necessity for sustainable groundwater practices and increased knowledge within local governance.
