Coupled Hydro-Mechanical Modeling of Swelling Processes in Clay–Sulfate Rocks

Abstract

Swelling of clay–sulfate rocks is a serious and devastating geo-hazard, often causing damage to geotechnical structures. Therefore, understanding underlying swelling processes is crucial for the safe design, construction, and maintenance of infrastructure. Planning appropriate countermeasures to the swelling problem requires a thorough understanding of the processes involved. We developed a coupled hydro-mechanical (HM) model to reproduce the observed heave in the historic city of Staufen in south-west Germany, which was caused by water inflow into the clay–sulfate bearing Triassic Grabfeld Formation (formerly Gipskeuper = “Gypsum Keuper”) after geothermal drilling. Richards’ equation coupled to a deformation process with linear kinematics was used to describe the hydro-mechanical behavior of clay–sulfate rocks. The mathematical model is implemented into the scientific open-source framework OpenGeoSys. We compared the model calculations with the measured long-term heave records at the study site. We then designed a sensitivity analysis to achieve a deeper insight into the swelling phenomena. The synthetic database obtained from the sensitivity analysis was used to develop a machine learning (ML) model, namely least-squares boosting ensemble (LSBoost) model coupled with a Bayesian optimization algorithm to rank the importance of parameters controlling the swelling. The HM model reproduced the heave observed at Staufen with sufficient accuracy, from a practical point of view. The ML model showed that the maximum swelling pressure is the most important parameter controlling the swelling. The other influential parameters rank as Young’s modulus, Poisson’s ratio, overburden thickness, and the initial volumetric water content of the swelling layer.

A constrained machine learning surrogate model to predict the distribution of water-in-oil emulsions in electrostatic fields

“…Its core principle involves the aggregation of multiple weak predictors, predominantly decision trees, to construct a robust predictive model. XGBoost addresses the common challenge of overfitting associated with tree-based algorithms by sequentially integrating numerous tree models 30 , 31 . The model expression can be written as follows 30 , 32 , 33 : where represents the -th tree model, stands for the predicted value for the sample , and the loss objective function for the learning process is defined as: where represents the differentiable convex loss function that measures the difference between the prediction and the target .…”

Experimental Data on Maximum Swelling Pressure of Clayey Soils and Related Soil Properties

“…The dataset presented is of significant value for a wide range of geotechnical applications, such as foundation design, slope stability analysis, landfill, containment design, and nuclear waste repositories. Engineers and practitioners dealing with expansive soils can derive practical insights from the maximum swelling pressure data to address challenges in these areas [26]. For foundation engineering, the dataset could help estimate the potential uplift pressures of structures built on expansive soils, guiding the selection of appropriate foundation types and materials to mitigate the effects of soil swelling [3].…”

Modeling Desiccation Cracks in Opalinus Clay at Field Scale with the Phase-Field Approach

“…Another important aspect is to consider the swelling properties of clay (61) and evaluate their effects on the strain and strain energy responses. The seasonal behavior reported in the CD-A experiment motivated the extension of the current method to account for cyclic drying-wetting paths and their effect on cracking, for example, primary and further drying-wetting retention paths and the correlation with crack opening-closing.…”