Buoyancy of underground structures and pore water pressure conduction law in silty clay strata

Based on the effective stress principle, indoor model tests were conducted in this study to calculate the buoyancy of an underground structure and determine the law of pore water pressure conduction in silty clay strata. A comprehensive underground structure-water-soil interaction test system was established with four-in-one features: Elimination of lateral friction, controllable water head, circulating water supply and drainage, and simulation of groundwater flow. Four- and seven-gradient buoyancy continuous monitoring tests were completed using fine sand and silty clay, respectively, to verify the reliability and accuracy of the test system. The hydrostatic pressure and seepage-hydrostatic process of the silty clay strata were simulated separately to investigate the buoyancy of the underground structure of the strata, the buoyancy reduction coefficient, and the pore water pressure conduction law. The results show the reliability and accuracy of the comprehensive test system for underground structure-water-soil interaction. The concept of "buoyancy starting intercept" is proposed based on this system, where the underground water level value should be the head of water supply minus the "buoyancy starting intercept" when calculating buoyancy in weak permeable layers. Under hydrostatic action, the groundwater is phreatic, deeper burial depths show greater magnitude of this discount. When the groundwater is confined, the water head reduction coefficient increases with increase in the burial depth or hydraulic gradient. Buoyancy calculations of an underground structure within the range of confined water should not be reduced in this case. Whether in a seepage or hydrostatic state, the pore water pressure in the silty clay layer is below the theoretical value. The results of this work may provide a theoretical basis for further analysis of the pore water pressure conduction law and buoyancy reduction mechanism of clay soils. We also may provide a theoretical reference for the development of innovative underground structure-water-soil interaction comprehensive test systems.

Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling

Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management process. Prediction-based approaches are valuable to compare the actual measurements with the simulated values to proactively detect anomalies. However, the application of the conventional hydrostatic seasonal time (HST) has some limitations related to an instantaneous response of the dam to environmental factors, which can lead to inaccurate prediction and interpretation, especially for daily measurements. Besides, the generalization ability (GA) of these models is not analyzed enough despite its crucial importance in selecting the appropriate models. In this study, the multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and adaptive boosting (AdaBoost) models with nonlinear autoregressive exogenous (NARX) inputs are proposed to incorporate the response delay of the dam to the hydraulic load. Thus, these models were evaluated and compared with the HST model for predicting the daily pore water pressure in an embankment dam. Moreover, we proposed a classification method of the models into four categories, namely perfect, excellent, good, and poor according to the GA. Results show that, except for the AdaBoost, the other ML models outperformed the traditional statistical approach (HST) in terms of prediction accuracy as well as the GA. Overall, the study results provide new insights in enhancing the monitoring processes and dam safeties by detecting the anomalies early through the comparison of the measurements and simulated results produced by the best-fitted models from the confidence interval (CI) perspective.

Finite element analysis of biological soft tissue surrounded by a deformable membrane that controls transmembrane flow

Background

Many biological soft tissues are hydrated porous hyperelastic materials, which consist of a complex solid skeleton with fine voids and fluid filling these voids. Mechanical interactions between the solid and the fluid in hydrated porous tissues have been analyzed by finite element methods (FEMs) in which the mixture theory was introduced in various ways. Although most of the tissues are surrounded by deformable membranes that control transmembrane flows, the boundaries of the tissues have been treated as rigid and/or freely permeable in these studies. The purpose of this study was to develop a method for the analysis of hydrated porous hyperelastic tissues surrounded by deformable membranes that control transmembrane flows.

Results

For this, we developed a new nonlinear finite element formulation of the mixture theory, where the nodal unknowns were the pore water pressure and solid displacement. This method allows the control of the fluid flow rate across the membrane using Neumann boundary condition. Using the method, we conducted a compression test of the hydrated porous hyperelastic tissue, which was surrounded by a flaccid impermeable membrane, and a part of the top surface of this tissue was pushed by a platen. The simulation results showed a stress relaxation phenomenon, resulting from the interaction between the elastic deformation of the tissue, pore water pressure gradient, and the movement of fluid. The results also showed that the fluid trapped by the impermeable membrane led to the swelling of the tissue around the platen.

Conclusions

These facts suggest that our new method can be effectively used for the analysis of a large deformation of hydrated porous hyperelastic material surrounded by a deformable membrane that controls transmembrane flow, and further investigations may allow more realistic analyses of the biological soft tissues, such as brain edema, brain trauma, the flow of blood and lymph in capillaries and pitting edema.