Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

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dc.contributor.authorLiang, Chunyu
dc.contributor.authorXu, Xin
dc.contributor.authorChen, Heping
dc.contributor.authorWang, Wensheng
dc.contributor.authorZheng, Kunkun
dc.contributor.authorTan, Guojin
dc.contributor.authorGu, Zhengwei
dc.contributor.authorZhang, Hao
dc.date.accessioned2021-07-30T20:13:38Z
dc.date.available2021-07-30T20:13:38Z
dc.date.issued2021-01
dc.description.abstractAsphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R<sup>2</sup>. After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.
dc.description.departmentEngineering
dc.formatText
dc.format.extent26 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationLiang, C., Xu, X., Chen, H., Wang, W., Zheng, K., Tan, G., Gu, Z., & Zhang, H. (2021). Machine learning approach to develop a novel multi-objective optimization method for pavement material proportion. Applied Sciences, 11(2), 835.
dc.identifier.doihttps://doi.org/10.3390/app11020835
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/10877/14148
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rights.holder© 2021 The Authors.
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
dc.sourceApplied Sciences, 2021, Vol. 11, No. 2, Article 835.
dc.subjectasphalt mixture
dc.subjectproportion optimization
dc.subjectparticle swarm optimization
dc.subjectGaussian process regression
dc.subjectmachine learning
dc.subjectIngram School of Engineering
dc.titleMachine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion
dc.typeArticle

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