A metamodel for estimating time-dependent groundwater-induced subsidence at large scales
Artikel i vetenskaplig tidskrift, 2024

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.

Groundwater

Regional subsidence

Metamodeling

Machine Learning

Författare

Ezra Haaf

Geologi och geoteknik

Pierre Wikby

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Ayman Abed

Geologi och geoteknik

Jonas Sundell

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Lars Rosen

Geologi och geoteknik

Minna Karstunen

Geologi och geoteknik

Engineering Geology

0013-7952 (ISSN)

Vol. 341 107705

Digital Twin Cities Centre

VINNOVA (2019-00041), 2020-02-29 -- 2024-12-31.

Modellering av tidsberoende grundvattensänkning, markdeformationer och dess skaderisker

Trafikverket (TRV2020/54637), 2023-10-01 -- 2025-12-19.

Trafikverket (TRV2020/54637), 2020-09-01 -- 2023-08-31.

Drivkrafter

Hållbar utveckling

Styrkeområden

Transport

Ämneskategorier

Geoteknik

Vattenteknik

DOI

10.1016/j.enggeo.2024.107705

Mer information

Senast uppdaterat

2024-10-17