CBR Prediction of Pavement Materials in Unsoaked Condition Using LSSVM, LSTM-RNN, and ANN Approaches

The present research introduces the best architecture model for predicting the unsoaked California bearing ratio (CBRu) of soil by comparing the models based on the least square support vector machine (LSSVM), long- short-term memory (LSTM), and artificial neural network (ANN) approach. The two kernel functions, linear and polynomial, have been selected to create LSSVM models. The developed LSTM models have been optimized by the Adam algorithm. In the employed ANN models, the Levenberg–Marquardt (LM), BFGS Quasi-Newton (BFG), scaled conjugate gradient (SCG), gradient descent with momentum (GDM), gradient descent (GD), and gradient descent with adaptive learning (GDA) algorithms have been used in the backpropagation process. For this purpose, three databases, such as training, testing and validation, have been compiled from the published research. A laboratory database has been developed by performing laboratory experiments for soil samples collected from and around Kota, Rajasthan, used for cross-validation of the best architecture model. The statistical tools, such as root means square error (RMSE), mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), variance accounted for (VAF), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), normalized mean bias error (NMBE), Legate and McCabe’s index (LMI), root mean square error to observation's standard deviation ratio (RSR), a20-index, index of agreement (IOA) and index of scatter (IOS) have been used to measure the performance of the models. The LSTM model MD 14 has achieved higher performance and accuracy (RMSE = 0.9127%, MAE = 0.8114%, R = 0.9863%, MAPE = 9.0772%, VAF = 97.26, WMAPE = 0.0669%, NS = 0.9708, NMBE = 0.0687%, LMI = 0.1926, RSR = 0.1708, a20-index = 93.88, IOA = 0.9037 and IOS = 0.0752) in testing phase. For the performance validation, model (MD) 14 has predicted the CBRu of the validation database. Also, model MD 14 has attained higher performance (RMSE = 1.2671%, MAE = 1.0161%, R = 0.9909) in the validation phase. By comparing the performances and performing score analysis, the LSTM model MD 14 has been recognized as the best architecture model for predicting the unsoaked CBR of soil. Moreover, model MD 14 has gained over 96% ( R = 0.9689) accuracy in predicting the CBRu of laboratory-tested soil samples. The present research also represents that the nonlinear approach has achieved higher performance with a high overfitti