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<p>This study utilizes the effectiveness of machine learning techniques, namely multilayer perceptron (MLP), radial basis function neural network (RBFNN), and long short-term memory (LSTM) for predicting the undrained shear strength of clay (S<sub>U</sub>). 400 datasets were generated using seven inputs, viz., water content, specific gravity, void ratio, liquid limit, plastic limit, height of the clay layer, and water table position. Performance parameters, including coefficient of determination (R<sup>2</sup>), variance account factor (VAF), Legate and McCabe index (LMI), root mean square error (RMSE), maximum absolute error (UAE), and expanded uncertainty (U<sub>95</sub>), were used to assess the model’s performance. Among three models, LSTM outperformed in all the three phases due to highest value of R<sup>2</sup> (Train = 0.995, Test = 0.992 and Validation = 0.986), VAF (Train = 99.644, Test = 99.405 and Validation = 98.80), LMI (Train = 0.931, Test = 0.911 and Validation = 0.875) and lowest value of RMSE (Train = 0.014, Test = 0.017 and Validation = 0.016), UAE (Train = 0.040, Test = 0.060 and Validation = 0.030) and U<sub>95</sub> (Train = 0.386, Test = 0.370 and Validation = 0.270). The model’s performance was also assessed using the convergence curve, reliability index, regression plot, William’s plot, external validation, and comparative analysis.</p>

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Assessment of Undrained Shear Strength of Clay Using Computational Tools

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Rashid Mustafa,
  • Md Talib Ahmad,
  • Md Asfaque Ansari,
  • Pawan Kishor Sah,
  • Meyarul Islam

摘要

This study utilizes the effectiveness of machine learning techniques, namely multilayer perceptron (MLP), radial basis function neural network (RBFNN), and long short-term memory (LSTM) for predicting the undrained shear strength of clay (SU). 400 datasets were generated using seven inputs, viz., water content, specific gravity, void ratio, liquid limit, plastic limit, height of the clay layer, and water table position. Performance parameters, including coefficient of determination (R2), variance account factor (VAF), Legate and McCabe index (LMI), root mean square error (RMSE), maximum absolute error (UAE), and expanded uncertainty (U95), were used to assess the model’s performance. Among three models, LSTM outperformed in all the three phases due to highest value of R2 (Train = 0.995, Test = 0.992 and Validation = 0.986), VAF (Train = 99.644, Test = 99.405 and Validation = 98.80), LMI (Train = 0.931, Test = 0.911 and Validation = 0.875) and lowest value of RMSE (Train = 0.014, Test = 0.017 and Validation = 0.016), UAE (Train = 0.040, Test = 0.060 and Validation = 0.030) and U95 (Train = 0.386, Test = 0.370 and Validation = 0.270). The model’s performance was also assessed using the convergence curve, reliability index, regression plot, William’s plot, external validation, and comparative analysis.