Estimation of flexible pavement structural capacity using machine learning techniques
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Estimation of flexible pavement structural capacity using machine learning techniques Nader KARBALLAEEZADEHa, Hosein GHASEMZADEH TEHRANIa* , Danial MOHAMMADZADEH SHADMEHRIb,c, Shahaboddin SHAMSHIRBANDd,e* a
Civil Engineering Department, Shahrood University of Technology, Shahrood 3619995161, Iran Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran c Department of Elite Relations with Industries, Khorasan Construction Engineering Organization, Mashhad 9185816744, Iran d Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam e Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam b
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Corresponding authors. E-mails: [email protected]; [email protected]
© Higher Education Press 2020
ABSTRACT 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 groundpenetrating 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. KEYWORDS transportation infrastructure, flexible pavement, structural number prediction, Gaussian process regression, M5P model tree, random forest
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Introduction
As the service life of pavement begins, various distresses debilitate its structural capacity. Structural capacity plays a vital role in identifying damaged pavements and choosing maintenance treatments [1]. The pavement engineers seek strategies to maintain pavement quality at an acceptable level. Thus, it is essential to implement a pavement management system (PMS) [2]. Although considerations related to pavement structural condition have not been taken into account in many PMSs, many agencies and Article history: Received Aug 27, 2019; Accepte
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