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    Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study
    (Elsevier, 2023) Althoey, Fadi; Akhter, Muhammad Naveed; Nagra, Zohaib Sattar; Awan, Hamad Hassan; Alanazi, Fayez; Khan, Mohsin Ali; Javed, Muhammad Faisal
    This research study utilizes four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree Bagging (DT-Bagging) for the development of new and advanced models for prediction of Marshall Stability (MS), and Marshall Flow (MF) of asphalt mixes. A compre-hensive and detailed database of 343 data points was established for both MS and MF. The predicting variables were chosen among the four most influential, and easy-to-determine pa-rameters. The models were trained, tested, validated, and the outcomes of the newly developed models were compared with actual outcomes. The root squared error (RSE), Nash-Sutcliffe effi-ciency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), regression coefficient (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed that in the case of MS, the rising order of input significance was bulk specific gravity of compacted aggregate, Gmb (38.56 %) > Percentage of Aggregates, Ps (19.84 %) > Bulk Specific Gravity of Aggregate, Gsb (19.43 %) > maximum specific gravity paving mix, Gmm (7.62 %), while in case of MF the order followed was: Ps (36.93 %) > Gsb (14.11 %) > Gmb (10.85 %) > Gmm (10.19 %). The outcomes of parametric analysis (PA) consistency of results in relation to previous research findings. The DT-Bagging model outperformed all other models with values of 0.971 and 0.980 (R), 16.88 and 0.24 (MAE), 28.27 and 0.36 (RMSE), 0.069 and 0.041 (RSE), 0.020 and 0.032 (RRMSE), 0.010