A method to estimate the equivalent diffusion coefficient and partition coefficient of a coextruded ethylene-vinyl alcohol (EVOH) geomembrane (GM) is presented. The method is based on the weighted harmonic mean of diffusion coefficients of each layer and the partition coefficient of the outer layers. The method was evaluated using experimental data obtained with five volatile organic compounds (VOCs) typically found in landfill leachate [methylene chloride (MC), methyl tertiary butyl ether (MTBE), trichloroethylene (TCE), toluene (TOL), and chlorobenzene (CB)] and a coextruded GM composed of outer layers of low-density polyethylene (LLDPE) or high-density polyethylene (HDPE), an adhesive tie layer (maleic anhydride), and a core of EVOH. Equivalent diffusion coefficients computed with the recommended equation were found to be no different statistically from equivalent diffusion coefficients measured using a modified double-compartment apparatus. VOC transport through a composite liner using a coextruded EVOH GM was predicted using a finite-difference model with the equivalent transport parameters as input. Good agreement was obtained between predictions from the model and measured concentrations from a column test simulating a composite liner.

The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.

This study evaluated migration of five common volatile organic compounds (VOCs) through simulated composite landfill liners that employ a co-extruded geomembrane (GM) with an interior layer of ethylene-vinyl alcohol (EVOH). A series of column tests was conducted with composite liner configurations employing 1.5-mm- and 1.0-mm-co-extruded EVOH GMs with outer layers of high-density polyethylene (HDPE) or linear low-density polyethylene (LLDPE) and 1.5-mm HDPE GMs. A simulated compacted clay liner (CCL) or a geosynthetic clay liner (GCL) was placed under the GM in each test. VOC breakthrough in composite liners employing HDPE GMs (approximately 30 days) occurred approximately four times faster than for co-extruded EVOH GMs with a HDPE outer layer (approximately 130 days) and with significantly higher VOC concentrations. Co-extruded EVOH GMs with LLDPE and HDPE as an outer layer have diffusion coefficients ($0.11–0.58\times {10}^{-13}{\mathrm{m}}^2/\mathrm{s}$ ) more than 20 times lower than those for conventional HDPE GM ($2.86–11.05\times {10}^{-13}{\mathrm{m}}^2/\mathrm{s}$ ). Numerical modeling of toluene migration through a composite liner with a 0.6-m-thick CCL and a co-extruded EVOH GM indicates that toluene concentrations are approximately 42% lower at one hundred years compared with a similar composite liner with a HDPE GM.