Machine learning modelling of tensile force in anchored geomembrane liners

Geomembrane liners (GM) anchored in the trenches of municipal solid waste (MSW) landfills undergo pullout failures when the applied tensile stresses exceed the ultimate strength of the liner. Present study estimates the tensile strength of GM liner against pullout failure from anchorage with the help of machine learning (ML) techniques. Five ML models, viz. multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector regression (SVR), random forest (RF) and locally weighted regression (LWR) were employed in this work. The effect of anchorage geometry, soil density and interface friction are studied with regard to the tensile strength of geomembrane. In this study, 1520 samples of soil-geomembrane interface friction were used. The ML models were trained and tested with 90% and 10% of data, respectively. The performance of ML models was statistically examined using the coefficients of determination (R2, R2adj) and mean square errors (MSE, RMSE). In addition, an external validation model and K-fold cross-validation techniques are used to check the models’ performance and accuracy. Among the chosen ML models, MLP was found to be superior in accurately predicting the tensile strength of GM liner. The developed methodology is useful for tensile strength estimation and beneficially employed in landfill design.