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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2020, Vol. 14 Issue (5) : 1083-1096     https://doi.org/10.1007/s11709-020-0654-z
TRANSDISCIPLINARY INSIGHT
Estimation of flexible pavement structural capacity using machine learning techniques
Nader KARBALLAEEZADEH1, Hosein GHASEMZADEH TEHRANI1(), Danial MOHAMMADZADEH SHADMEHRI2,3, Shahaboddin SHAMSHIRBAND4,5()
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
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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 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.

Keywords transportation infrastructure      flexible pavement      structural number prediction      Gaussian process regression      M5P model tree      random forest     
Corresponding Author(s): Hosein GHASEMZADEH TEHRANI,Shahaboddin SHAMSHIRBAND   
Just Accepted Date: 28 July 2020   Online First Date: 14 September 2020    Issue Date: 16 November 2020
 Cite this article:   
Nader KARBALLAEEZADEH,Hosein GHASEMZADEH TEHRANI,Danial MOHAMMADZADEH SHADMEHRI, et al. Estimation of flexible pavement structural capacity using machine learning techniques[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1083-1096.
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http://journal.hep.com.cn/fsce/EN/10.1007/s11709-020-0654-z
http://journal.hep.com.cn/fsce/EN/Y2020/V14/I5/1083
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Nader KARBALLAEEZADEH
Hosein GHASEMZADEH TEHRANI
Danial MOHAMMADZADEH SHADMEHRI
Shahaboddin SHAMSHIRBAND
model name mathematical form
AASHTO [14] SN=i=1 nhiag ( Ei Eg)1 3
Noureldin [23] SN eff= 4( rx)2 36 17.234 rx×Dx3
Jameson [24] S Neff=13.5 6.5log?D0+3.71log? D90
Rohde [25] SN ef f= 0.4728×S IP0.481× Hp0.7581, SIP=D0D1.5H p
Rohde et al. [26] MSN=SN+S Nsbg
SN sb g= 1.43+ 3.51log ?(CBR )0.85 (log ?(CBR ))2
Asgari [27] SNC= a0 ×( D0 )a1
COST [28] SN ef f= 1.69+842.8D 0D150 42.94D90
Modified version of COST’s model [29] SN ef f= 1.70+813D 0D150 39 D90
Romanoschi and Metcalf [30] SN=6.453.676×log?( D0)+ 3.727× log?( D150)
Hoffman [31] SN ef f= 0.0182× Esbg3× l0
Schnoor and Horak [27] SN ef f= e 5.12× BLI 0.31× Aupp 0.78
BLI=D0D30
Aupp= 5D02D30 2D60 D902
Wimsatt formula [27] SN ef f= 0.00045×D×EP3
Abdel-Khalek et al. [32] SN RWD=23.52× RWD 0.241.39×ln ?(SD) 150.69× RI0.8119.04+ RI 6.37
RI=(average RW D deflection)×(standard deviation of RW D deflection)
Howard [27] SN=0.971876+0.002543HP+785.4524 D0 D150+ 69.9904 D90 , SN2.5SN= 0.884209+0.000866HP+866.3272 D0 D150+ 22.6561 D90 , SN<2.5
Kim et al. [33] SNeff= K1× SIPk2× HPk3+(K4× (1+r 0 ) K5)+( K6× SCI K7×BDI K8)SI P= D0 D1.5 HP
SCI= D0D 20BD I= D20 D30
modified version of Kim et al.’s model [34] SNeff= (1.039×SIP0.412 ×H HP0.76)+( 0.22× (1+r 0 ) 5.541)( 0.0006× BDI3.607)SI P= D0 D1.5 HP
SCI= D0D 20BD I= D20 D30
Gedafa et al. [35] SN=0.1438 (log? (EAL))20.4115 log?(EAL) 0.406R ut+0.01D20.0091 EFCR+ 0.0062d 02+0.0836ETCR0.3364 d0+0.0004 EFCR20.0805 D+6.3763 0.0008 ( d0× D)
Elbagalati et al. [36] SN=0.29ln ?(ADT PLN) 14.27+27.55( ACthD0)0.046952.426 ln?(SD)
Kavussi et al. [37] SN ef f= 34.171× D00.638× D900.330
Zihan et al. [1] SN TS D= 24.28+0.31 ln?( ADT)+0.18(Tth) +8.65( D48 )0.11+18.67e0.013 D0
Tab.1  Summary of SNeff prediction models
parameter formula description
SCI D0D20 asphalt structural evaluation
BDI D30D60 base structural evaluation
BCI D60D90 subbase and subgrade structural evaluation
AREA 6( 2 D30 D0+ 2 D60 D0+ D90 D0+1) pavement and subgrade structural assessment
AUPP 5 D0 2 D30 2 D60D902 area under pavement profile;
structural evaluation of the pavement upper layers
F2 D 30 D 90 D60 shape factor;
evaluation of subgrade moduli
F3 D60D 120 D90 additional shape factor;
evaluation of lower layer
Tab.2  Deflection bowl parameters [40]
variable mean Min. Max. range median standard deviation coefficient of variation skewness kurtosis
D0 a 181.2265 47.9 800 752.1 164 92.55448 0.51 2.024 7.740
D20 135.6528 28 607 579 123.3 69.31714 0.51 2.113 8.293
D30 114.2369 24 596 472 104.1 57.31102 0.50 2.055 7.561
D45 84.6744 17 323 306 77 40.92214 0.48 1.712 5.032
D60 64.4802 12 220 208 57 31.51575 0.49 1.369 2.932
D90 40.7414 7 129 122 36 21.50732 0.53 0.923 0.502
D120 27.8439 2 82 80 24 16.04898 0.58 0.792 -0.149
D150 21.4805 0 56.1 56.1 18 12.69504 0.59 0.742 -0.346
D180 17.0314 0 97.5 97.5 14 10.47929 0.62 1.156 3.714
surface temp.b 25.1283 8 35.9 27.9 25.8 5.93612 0.24 -0.442 -0.329
SNeff 4.7774 1.5 9.4 7.95 4.7 1.40363 0.29 0.244 -0.478
Tab.3  Statistical characteristics of the study dataset
Fig.1  Distributions of the study variables: a) D0, b) D20, c) D30, d) D45, e) D60, f) D90, g) D120, h) D150, i) D180, j) Surfacetemp, and k) SNeff.
Kolmogorov-Smirnov Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
0.057 759 0.000 0.988 759 0.000
Tab.4  Tests of normality for SNeff
Variable D0 D20 D30 D45 D60 D90 D120 D150 D180 surface temp.
Spearman’s correlation coefficient −0.7 −0.617 −0.533 −0.347 −0.176 0.038 0.137 0.169 0.223−− −0.438
Tab.5  Spearman’s correlation coefficient between input variables and SNeff
category no. inputs target
1 D0, D20, D30, D45, D60, D90, D120, D150, D180, and Surface temp. SNeff
2 AUPP, SCI, BDI, F2, F3, and Surface temp.
3 AUPP, SCI, BDI, Surface temp., and D0
4 AUPP, SCI, D0×Surface temp., D0D60, and D20D30
5 AUPP, SCI, BDI, D0×Surface temp., F2, and F3
6 D0D60, D0D45, D20D30, and surfa cetem p.AU PP
7 AUPP, D0D45, Surfacetemp.(D0D60)×(D20D30), and 1BDI×SC I×AUPP×(D0D60)× (D20D30)
Tab.6  Target and seven input categories selected for prediction
category No. GPR M5P RF
R MAE RMSE R MAE RMSE R MAE RMSE
1 0.823 0.638 0.804 0.784 0.706 0.872 0.841 0.592 0.760
2 0.811 0.671 0.826 0.795 0.690 0.852 0.834 0.601 0.775
3 0.785 0.731 0.871 0.781 0.716 0.876 0.816 0.634 0.812
4 0.771 0.748 0.895 0.752 0.759 0.927 0.793 0.677 0.856
5 0.803 0.648 0.839 0.787 0.703 0.865 0.824 0.618 0.796
6 0.768 0.763 0.899 0.765 0.750 0.904 0.797 0.681 0.849
7 0.748 0.771 0.932 0.772 0.764 0.893 0.764 0.727 0.910
Tab.7  Performance metrics values for all seven input categories in GPR, M5P, and RF methods
Fig.2  Observed and predicted ?values of SNeff for GPR-1, M5P-2, and RF-1 models.
Fig.3  Scatter plots of observed ??and predicted SNeff values for GPR-1, M5P-2, and RF-1 models.
Fig.4  Taylor diagrams of predicted SN values.
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