Estimation of flexible pavement structural capacity using machine learning techniques

查看全文 作　　者：Nader [1]KARBALLAEEZADEH;Hosein GHASEMZADEH [1]TEHRANI;Danial MOHAMMADZADEH [2,3]SHADMEHRI;Shahaboddin [4,5]SHAMSHIRBAND

机构地区：[1]Civil Engineering Department,Shahrood University of Technology,Shahrood 3619995161,Iran;[2]Department of Civil Engineering,Ferdowsi University of Mashhad,Mashhad 9177948974,Iran;[3]Department of Elite Relations with Industries,Khorasan Construction Engineering Organization,Mashhad 9185816744,Iran;[4]Department for Management of Science and Technology Development,Ton Duc Thang University,Ho Chi Minh City,Vietnam;[5]Faculty of Information Technology,Ton Duc Thang University,Ho Chi Minh City,Vietnam

出　　处：《Frontiers of Structural and Civil Engineering》索引2020年第14卷第5期,共14页

摘　　要：The most common index for representing structural condition of the pavement is the structural number.The 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 manners.This 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 approach.In 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 pavements.Dataset 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 inputs.The accuracy of results was examined based on three criteria of R,MAE,and RMSE.Among 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 difficulty.Using 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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Estimation of flexible pavement structural capacity using machine learning techniques

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耦合文献(176)

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