Machine-learning modelling of tensile force in anchored geomembrane liners

Abstract

Geomembrane (GM) liners anchored in the trenches of municipal solid waste (MSW) landfills undergo pull-out failure when the applied tensile stresses exceed the ultimate strength of the liner. The present study estimates the tensile strength of GM liner against pull-out failure from anchorage with the help of machine-learning (ML) techniques. Five ML models, namely 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 were studied with regards to the tensile strength of the GM. In this study, 1520 samples of soil–GM 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 were 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 can be beneficially employed in landfill design.