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

Basit Öğe Kaydı
dc.contributor.authorAlthoey, Fadi
dc.contributor.authorAkhter, Muhammad Naveed
dc.contributor.authorNagra, Zohaib Sattar
dc.contributor.authorAwan, Hamad Hassan
dc.contributor.authorAlanazi, Fayez
dc.contributor.authorKhan, Mohsin Ali
dc.contributor.authorJaved, Muhammad Faisal
dc.date.accessioned2024-02-23T14:02:39Z
dc.date.available2024-02-23T14:02:39Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractThis 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), 0.010en_US
dc.description.sponsorshipDeanship of Scientific Research at Najran University under the National Research Priorities funding program [NU/NRP/SERC/11/23]en_US
dc.description.sponsorshipThe authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the National Research Priorities funding program grant code (NU/NRP/SERC/11/23).en_US
dc.identifier.doi10.1016/j.cscm.2022.e01774
dc.identifier.issn2214-5095
dc.identifier.scopus2-s2.0-85144055351en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cscm.2022.e01774
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11793
dc.identifier.volume18en_US
dc.identifier.wosWOS:000906543200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofCase Studies In Construction Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMarshall Mix Parameteren_US
dc.subjectDeep Learningen_US
dc.subjectPrediction Modelsen_US
dc.subjectAsphalten_US
dc.subjectBio-Inspired Modelsen_US
dc.titlePrediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative studyen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu