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

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 Ma...

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Vydáno v:Case Studies in Construction Materials Ročník 18; s. e01774
Hlavní autoři: Althoey, Fadi, Akhter, Muhammad Naveed, Nagra, Zohaib Sattar, Awan, Hamad Hassan, Alanazi, Fayez, Khan, Mohsin Ali, Javed, Muhammad Faisal, Eldin, Sayed M., Özkılıç, Yasin Onuralp
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2023
Elsevier
Témata:
Asphalt
Bio-Inspired models
Deep Learning
Marshall Mix Parameter
Prediction models
Bio-Inspired models
Asphalt
Prediction models
Marshall Mix Parameter
Deep Learning
ISSN:2214-5095, 2214-5095
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Abstract 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 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 %) > 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 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.
AbstractList 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 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 %) > 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 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.
ArticleNumber e01774
Author Javed, Muhammad Faisal
Althoey, Fadi
Awan, Hamad Hassan
Khan, Mohsin Ali
Akhter, Muhammad Naveed
Özkılıç, Yasin Onuralp
Nagra, Zohaib Sattar
Alanazi, Fayez
Eldin, Sayed M.
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  givenname: Muhammad Naveed
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  givenname: Mohsin Ali
  surname: Khan
  fullname: Khan, Mohsin Ali
  email: mohsin.ali@cecos.edu.pk, moak.pg18mce@student.nust.edu.pk
  organization: Department of Civil Engineering, CECOS University of IT and Emerging Science, Peshawar, Pakistan
– sequence: 7
  givenname: Muhammad Faisal
  surname: Javed
  fullname: Javed, Muhammad Faisal
  email: arbabfaisal@cuiatd.edu.pk
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  givenname: Sayed M.
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  givenname: Yasin Onuralp
  surname: Özkılıç
  fullname: Özkılıç, Yasin Onuralp
  email: yozkilic@erbakan.edu.tr
  organization: Department of Civil Engineering, Faculty of Engineering, Necmettin Erbakan University, Konya, Turkey
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Keywords Bio-Inspired models
Asphalt
Prediction models
Marshall Mix Parameter
Deep Learning
Language English
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Snippet This research study utilizes four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy...
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SubjectTerms Asphalt
Bio-Inspired models
Deep Learning
Marshall Mix Parameter
Prediction models
Title Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study
URI https://dx.doi.org/10.1016/j.cscm.2022.e01774
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