Automated,economical,and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO_(2)emissions during pavement construction and maintenance.Additionally,the laboratory mix design process,which involves selecting aggregate gradation and binder content,is time-consuming and labor-intensive.To accelerate the traditional mix design procedure,this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning(ML)and a meta-heuristic algorithm.Specifically,ML approaches were employed to model the relationship between volumetric properties(mixture bulk specific gravity(Gmb)and air void(VV))and both mixture component properties and mixture proportion,based on a dataset collected from literature with 660 mixture designs.Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization(MOGWO)algorithm,an automatic asphalt mix design was proposed to pursue three goals,including VV,cost,and CO_(2)emission.The results indicated that least squares support vector regression(LSSVR)and e Xtreme gradient boosting(XGBoost)achieved the highest prediction accuracies(correlation coefficient:0.92 for VV and 0.96 for Gmb).The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs.cost vs.CO_(2)emission.Compared to the traditional laboratory design,the optimal mixture with VV of4%achieves a cost saving of 2.46%and a reduction of 4.03%in carbon emission.The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.