Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{ALTHOEY:2023:cscm,

• author = "Fadi Althoey and Muhammad Naveed Akhter and Zohaib Sattar Nagra and Hamad Hassan Awan and Fayez Alanazi and Mohsin Ali Khan and Muhammad Faisal Javed and Sayed M. Eldin and Yasin Onuralp Ozkilic",
• title = "Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study",
• journal = "Case Studies in Construction Materials",
• volume = "18",
• pages = "e01774",
• year = "2023",
• ISSN = "2214-5095",
• DOI = "doi:10.1016/j.cscm.2022.e01774",
• URL = "https://www.sciencedirect.com/science/article/pii/S2214509522009068",
• keywords = "genetic algorithms, genetic programming, Marshall Mix Parameter, Deep Learning, Prediction models, Asphalt, Bio-Inspired models",
• abstract = "This research study uses 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 comprehensive 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 parameters. 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 efficiency (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 percent) > Percentage of Aggregates, Ps (19.84 percent) > Bulk Specific Gravity of Aggregate, Gsb (19.43 percent) > maximum specific gravity paving mix, Gmm (7.62 percent), while in case of MF the order followed was: Ps (36.93 percent) > Gsb (14.11 percent) > Gmb (10.85 percent) > Gmm (10.19 percent). 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 and 0.016 (PI), 0.931 and 0.959 (NSE), for MS and MF, respectively. The results of the comparison analysis showed that ANN, ANFIS, MEP, and DT-Bagging are all effective and reliable approaches for the estimation of MS and MF. The MEP-derived mathematical expressions represent the novelty of MEP and are relatively simple and reliable. Roverall values for MS and MF were in the order of DT-Bagging >MEP >ANFIS >ANN with all values exceeding the permitted range of 0.80 for both MS and MF. Hence, all the modeling approaches showed higher performance, possessed high generalization and predication capabilities, and assess the relative significance of input parameters in the prediction of MS and MF. Hence, the findings of this research study would assist in the safer, faster, and sustainable prediction of MS and MF, from the standpoint of resources and time required to perform the Marshall tests",

}

Genetic Programming entries for Fadi Althoey Muhammad Naveed Akhter Zohaib Sattar Nagra Hamad Hassan Awan Fayez Alanazi Mohsin Ali Khan Muhammad Faisal Javed Sayed M Eldin Yasin Onuralp Ozkilic

Citations