Construction of large underground infrastructure facilities routinely leads to leakage of groundwater and reduction of pore water pressures, causing time-dependent deformation of overburden soft soil. Coupled hydro-geomechanical numerical models can provide estimates of subsidence, caused by the complex time-dependent processes of creep and consolidation, thereby increasing our understanding of when and where deformations will arise and at what magnitude. However, such hydro-mechanical models are computationally expensive and generally not feasible at larger scales, where decisions are made on design and mitigation. Therefore, a computationally efficient Machine Learning-based metamodel is implemented, which emulates 2D finite element scenario-based simulations of ground deformations with the advanced Creep-SCLAY-1S-model. The metamodel employs decision tree-based ensemble learners random forest (RF) and extreme gradient boosting (XGB), with spatially explicit hydrostratigraphic data as features. In a case study in Central Gothenburg, Sweden, the metamodel shows high predictive skill (Pearson's r of 0.9–0.98) on 25 % of unseen data and good agreement with the numerical model on unseen cross-sections. Through interpretable Machine Learning, Shapley analysis provides insights into the workings of the metamodel, which alignes with process understanding. The approach provides a novel tool for efficient, scenario-based decision support on large scales based on an advanced soil model emulated by a physically plausible metamodel.

中文翻译：

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用于大规模估计随时间变化的地下水引起的沉降的元模型


大型地下基础设施的建设通常会导致地下水渗漏和孔隙水压力降低，导致覆盖层软土发生随时间变化的变形。耦合的水文地质力学数值模型可以提供对由复杂的与时间相关的蠕变和固结过程引起的沉降的估计，从而增加我们对何时何地发生变形以及变形程度的了解。然而，这种流体力学模型的计算成本很高，并且通常在较大规模上不可行，因为在较大规模上需要根据设计和缓解做出决策。因此，实现了一种计算高效的基于机器学习的元模型，该元模型使用先进的 Creep-SCLAY-1S 模型来模拟基于二维有限元场景的地面变形模拟。该元模型采用基于决策树的集成学习器随机森林（RF）和极限梯度提升（XGB），以空间明确的水文地层数据作为特征。在瑞典哥德堡中部的一项案例研究中，元模型对 25% 的未见数据表现出较高的预测能力（Pearson r 为 0.9-0.98），并且与未见横截面的数值模型具有良好的一致性。通过可解释的机器学习，Shapley 分析提供了对元模型工作原理的见解，这与流程理解相一致。该方法提供了一种新颖的工具，可以基于物理上合理的元模型模拟的高级土壤模型，在大规模上提供高效、基于场景的决策支持。