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Estimation of flexible pavement structural capacity using machine learning techniques

Frontiers of Structural and Civil Engineering 2020年第5期14卷 1083-1096页

作者： Nader KARBALLAEEZADEH hosein ghasemzadeh tehrani Danial MOHAMMADZADEH SHADMEHRI Shahaboddin SHAMSHIRBAND Civil Engineering Department Shahrood University of TechnologyShahrood ***Iran Department of Civil Engineering Ferdowsi University of MashhadMashhad ***Iran Department of Elite Relations with Industries Khorasan Construction Engineering OrganizationMashhad ***Iran Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

The most common index for representing structural condition of the pavement is the structural *** current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests,recording pavement surface deflections,and analyzing recorded deflections by back-calculation *** procedure has two drawbacks:falling weight deflectometer and ground-penetrating radar are expensive tests;back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error *** this study,three machine learning methods entitled Gaussian process regression,M5P model tree,and random forest used for the prediction of structural numbers in flexible *** of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes“structural number”as output and“surface deflections and surface temperature”as *** accuracy of results was examined based on three criteria of R,MAE,and *** the methods employed in this paper,random forest is the most accurate as it yields the best values for above criteria(R=0.841,MAE=0.592,and RMSE=0.760).The proposed method does not require to use ground penetrating radar test,which in turn reduce costs and work *** machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

关键词： transportation infrastructure flexible pavement structural number prediction Gaussian process regression M5P model tree random forest

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