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, R²_adj) 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.

中文翻译：

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锚固土工膜衬垫中拉力的机器学习建模

当施加的拉伸应力超过衬里的极限强度时，固定在城市固体废物 (MSW) 填埋场沟渠中的土工膜 (GM) 衬里会发生拔出故障。本研究在机器学习 (ML) 技术的帮助下估计了 GM 衬里抗从锚固拔出失败的抗拉强度。这项工作采用了五种 ML 模型，即多层感知器 (MLP)、极端梯度提升 (XGB)、支持向量回归 (SVR)、随机森林 (RF) 和局部加权回归 (LWR)。研究了锚固几何形状、土壤密度和界面摩擦力对 GM 抗拉强度的影响。在这项研究中，使用了 1520 个土壤-GM 界面摩擦样本。ML 模型分别使用 90% 和 10% 的数据进行训练和测试。R ² , R ² _adj ) 和均方误差 (MSE, RMSE)。此外，还使用了外部验证模型和 K 折交叉验证技术来检查模型的性能和准确性。在所选的 ML 模型中，发现 MLP 在准确预测 GM 衬里的抗拉强度方面表现出色。所开发的方法可用于抗拉强度估算，并可有益地用于垃圾填埋场设计。