A metamodel for estimating time-dependent groundwater-induced subsidence at large scales
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 com...
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| Published in: | Engineering geology Vol. 341; p. 107705 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.11.2024
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| ISSN: | 0013-7952 |
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| Abstract | 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.
•A ML-based metamodel emulates a hydro-geomechanical model accurately.•Subsidence due to pore-pressure reductions in soft soil is estimated at large scale.•Predictions possible at high resolution with high computational efficiency.•Interpretable ML confirms that the metamodel matches physics.•Metamodels are a reliable basis for large scale infrastructure decision support. |
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| AbstractList | 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. 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. •A ML-based metamodel emulates a hydro-geomechanical model accurately.•Subsidence due to pore-pressure reductions in soft soil is estimated at large scale.•Predictions possible at high resolution with high computational efficiency.•Interpretable ML confirms that the metamodel matches physics.•Metamodels are a reliable basis for large scale infrastructure decision support. |
| ArticleNumber | 107705 |
| Author | Abed, Ayman Rosén, Lars McGivney, Eric Haaf, Ezra Sundell, Jonas Wikby, Pierre Karstunen, Minna |
| Author_xml | – sequence: 1 givenname: Ezra surname: Haaf fullname: Haaf, Ezra email: ezra@chalmers.se organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden – sequence: 2 givenname: Pierre surname: Wikby fullname: Wikby, Pierre organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden – sequence: 3 givenname: Ayman surname: Abed fullname: Abed, Ayman organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden – sequence: 4 givenname: Jonas surname: Sundell fullname: Sundell, Jonas organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden – sequence: 5 givenname: Eric surname: McGivney fullname: McGivney, Eric organization: Qasa AB, Sweden – sequence: 6 givenname: Lars surname: Rosén fullname: Rosén, Lars organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden – sequence: 7 givenname: Minna surname: Karstunen fullname: Karstunen, Minna organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden |
| BackLink | https://research.chalmers.se/publication/543116$$DView record from Swedish Publication Index (Chalmers tekniska högskola) |
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| Keywords | Metamodeling Regional subsidence Machine learning |
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