Please wait a minute...

Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2020, Vol. 14 Issue (5) : 1083-1096
Estimation of flexible pavement structural capacity using machine learning techniques
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
Download: PDF(1993 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.
E-mail this article
E-mail Alert
Articles by authors
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
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
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.
1 Z Uddin Ahmed Zihan, M A Elseifi, K Gaspard, Z Zhang. Development of a structural capacity prediction model based on traffic speed deflectometer measurements. Transportation Research Record: Journal of the Transportation Research Board, 2018, 2672(40): 315–325
2 M Onyango, S A Merabti, J Owino, I Fomunung, W Wu. Analysis of cost effective pavement treatment and budget optimization for arterial roads in the city of Chattanooga. Frontiers of Structural and Civil Engineering, 2018, 12(3): 291–299
3 C M Kuo, T Y Tsai. Significance of subgrade damping on structural evaluation of pavements. Road Materials and Pavement Design, 2014, 15(2): 455–464
4 R L Lytton. Concepts of pavement performance prediction and modeling. In: Proceeddings of the 2nd North American Conference on Managing Pavements. Toronto, Ontario: Ministry of Transportation, 1987
5 N Kargah-Ostadi, Y Zhou, T Rahman. Developing performance prediction models for pavement management systems in local governments in absence of age data. Transportation Research Record: Journal of the Transportation Research Board, 2019, 2673(3): 0361198119833680
6 O Elbagalati, M Elseifi, K Gaspard, Z Zhang. Development of the pavement structural health index based on falling weight deflectometer testing. International Journal of Pavement Engineering, 2018, 19(1): 1–8
7 H S Abd El-Raof, R T Abd El-Hakim, S M El-Badawy, H A Afify. Simplified closed-form procedure for network-level determination of pavement layer moduli from falling weight deflectometer data. Journal of Transportation Engineering, Part B: Pavements, 2018, 144(4): 04018052
8 F Moghadas Nejad , F Zare Motekhases, H Zakeri, A Mehrabi. An image processing approach to asphalt concrete feature extraction. Journal of Industrial and Intelligent Information, 2015, 3(1): 54–60
9 Y Deng, Q Yang. Rapid evaluation of a transverse crack on a semi-rigid pavement utilising deflection basin data. Road Materials and Pavement Design, 2019, 20(4): 929–942
10 N Karballaeezadeh, D Mohammadzadeh S, S Shamshirband, P Hajikhodaverdikhan, A Mosavi, K Chau. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Engineering Applications of Computational Fluid Mechanics, 2019, 13(1): 188–198
11 L Pinkofsky, D Jansen. Structural pavement assessment in Germany. Frontiers of Structural and Civil Engineering, 2018, 12(2): 183–191
12 P Liu, D Wang, F Otto, M Oeser. Application of semi-analytical finite element method to analyze the bearing capacity of asphalt pavements under moving loads. Frontiers of Structural and Civil Engineering, 2018, 12(2): 215–221
13 S Dai, Q Yan. Pavement evaluation using ground penetrating radar. In: Pavement Materials, Structures, and Performance. American Society of Civil Engineers, 2014, 222–230
14 AASHTO. Guide for Design of Pavement Structures. Washington, D.C.: American Association of State Highway and Transportation Officials, 1993
15 R Chen, P Zhang, H Wu, Z Wang, Z Zhong. Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 2019, 13(6): 1363–1378
16 A Abed, N Thom, L Neves. Probabilistic prediction of asphalt pavement performance. Road Materials and Pavement Design, 2019, 20(Sup 1): 1–18
17 K A Abaza. Deterministic performance prediction model for rehabilitation and management of flexible pavement. International Journal of Pavement Engineering, 2004, 5(2): 111–121
18 P Dalla Valle. Reliability in Pavement Design. Nottingham: University of Nottingham, 2015
19 Y H. Huang Pavement Analysis and Design. Englewood Cliffs, NJ: Prentice-Hall, 1993
20 N Kargah-Ostadi, S M Stoffels, N Tabatabaee. Network-level pavement roughness prediction model for rehabilitation recommendations. Transportation Research Record: Journal of the Transportation Research Board, 2010, 2155(1): 124–133
21 J Yang, J J Lu, M Gunaratne, Q Xiang. Forecasting overall pavement condition with neural networks: Application on Florida highway network. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1853(1): 3–12
22 N Kargah-Ostadi, S M Stoffels. Framework for development and comprehensive comparison of empirical pavement performance models. Journal of Transportation Engineering, 2015, 141(8): 04015012
23 A S. Noureldin New scenario for backcalculation of layer moduli of flexible pavements. Transportation Research Record, 1993, 1384: 23–28
24 G Jameson. Development of Procedures to Predict Structural Number and Subgrade Strength from Falling Weight Deflectometer Deflections. Vermont South, Victoria: ARRB Transport Research, 1993
25 G T. Rohde Determining pavement structural number from FWD testing. Transportation Research Record, 1994, 1(1448): 61–68
26 G T Rohde, F Jooste, E Sadzik, T Henning. The calibration and use of HDM-IV performance models in a pavement management system. In: The Fourth International Conference on Managing Pavements. Durban, South Africa: Pretoria, 1998
27 H Schnoor, E Horak. Possible method of determining structural number for flexible pavements with the falling weight deflectometer. In: Abstracts of the 31st Southern African Transport Conference (SATC 2012). Pretoria, South Africa: Minister of Transport, 2012
28 European Cooperation in Science and Technology (COST). Information gathering work of Task Group 2, in Falling Weight Deflectometer (COST 336). 1998
29 A L Crook, S R Montgomery, W S Guthrie. Use of falling weight deflectometer data for network-level flexible pavement management. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2304(1): 75–85
30 K V. DasariDeflection based condition assessment for Rolling Wheel Deflectometer at network level. Thesis for the Master’s Degree. Louisiana: Louisiana State University, 2013
31 M S Hoffman. Direct method for evaluating structural needs of flexible pavements with falling-weight deflectometer deflections. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1860(1): 41–47
32 A M Abdel-Khalek, M A Elseifi, K Gaspard, Z Zhang, K Dasari. Model to estimate pavement structural number at network level with rolling wheel deflectometer data. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2304(1): 142–149
33 M Y Kim, D Y Kim, M R Murphy. Improved method for evaluating the pavement structural number with falling weight deflectometer deflections. Transportation Research Record: Journal of the Transportation Research Board, 2013, 2366(1): 120–126
34 H S Abd El-Raof, R T Abd El-Hakim, S M El-Badawy, H Afify. Structural number prediction for flexible pavements based on falling weight deflectometer data. In: Transportation Research Board 97th Annual Meeting. Washington D.C.: Transportation Research Board, 2018
35 D S Gedafa, M Hossain, R Miller, T Van. Network-level flexible pavement structural evaluation. International Journal of Pavement Engineering, 2014, 15(4): 309–322
36 O Elbagalati, M A Elseifi, K Gaspard, Z Zhang. Prediction of in-service pavement structural capacity based on traffic-speed deflection measurements. Journal of Transportation Engineering, 2016, 142(11): 04016058
37 A Kavussi, M Abbasghorbani, F Moghadas Nejad, A Bamdad Ziksari. A new method to determine maintenance and repair activities at network-level pavement management using falling weight deflectometer. Journal of Civil Engineering and Management, 2017, 23(3): 338–346
38 R Salvi, A Ramdasi, Y A Kolekar, L V Bhandarkar. Use of Ground-Penetrating Radar (GPR) as An Effective Tool in Assessing Pavements—A Review. Geotechnics for Transportation Infrastructure, Singapore: Springer, 2019, 85–95
39 A Benedetto, F Tosti, L Bianchini Ciampoli, F D’Amico. An overview of ground-penetrating radar signal processing techniques for road inspections. Signal Processing, 2017, 132: 201–209
40 E Horak, J W Maina, I Van Wijk, A Hefer, G Jordaan, P Olivier, P W de Bruin. Revision of the South African Pavement Design Method. Draft Contract Report, No. SANRAL/SAPDM/B-2/2009-01. 2009
41 H Guo, X Zhuang, T Rabczuk. A deep collocation method for the bending analysis of Kirchhoff Plate. Computers, Materials & Continua, 2019, 59(2): 433–456
42 C Anitescu, E Atroshchenko, N Alajlan, T Rabczuk. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359
43 K M Hamdia, H Ghasemi, X Zhuang, N Alajlan, T. Rabczuk Computational machine learning representation for the flexoelectricity effect in truncated pyramid structures. Computers, Materials & Continua, 2019, 59(1): 79–87
44 K M Hamdia, H Ghasemi, Y Bazi, H AlHichri, N Alajlan, T Rabczuk. A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization. Finite Elements in Analysis and Design, 2019, 165: 21–30
45 A Y Sun, D Wang, X Xu. Monthly streamflow forecasting using Gaussian process regression. Journal of Hydrology (Amsterdam), 2014, 511: 72–81
46 C K Williams, C E Rasmussen. Gaussian processes for machine learning. International Journal of Neural Systems, 2004, 14(2): 69–106
47 C K Arthur, V A Temeng, Y Y Ziggah. Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers, 2020, 36: 29–42
48 J J Li, A Jutzeler, B. Faltings Estimating urban ultrafine particle distributions with gaussian process models. In: CEUR Workshop Proceedings. Canberra, 2014, 145–153
49 W Chu, Z Ghahramani. Gaussian processes for ordinal regression. Journal of Machine Learning Research, 2005, 6: 1019–1041
50 P Samui. Utilization of Gaussian process regression for determination of soil electrical resistivity. Geotechnical and Geological Engineering, 2014, 32(1): 191–195
51 P Samui, J Jagan. Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach. Frontiers of Structural and Civil Engineering, 2013, 7(2): 133–136
52 J R Quinlan. Learning with continuous classes. In: The 5th Australian joint conference on artificial intelligence. Hobart, Tasmania: World Scientific, 1992
53 Y, Wang I H. Witten Induction of model trees for predicting continuous classes. Hamilton: University of Waikato, 1996
54 T Singh, M Pal, V Arora. Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree. Frontiers of Structural and Civil Engineering, 2019, 13(3): 674–685
55 A Behnood, V Behnood, M Modiri Gharehveran, K E Alyamac. Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Construction & Building Materials, 2017, 142: 199–207
56 I H Witten, E Frank, M A Hall. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, MA: Morgan Kauffman, 2011
57 S Blaifi, S Moulahoum, R Benkercha, B Taghezouit, A Saim. M5P model tree based fast fuzzy maximum power point tracker. Solar Energy, 2018, 163: 405–424
58 L Breiman, J, Friedman R Olshen, C. Stone Classification and regression trees. Wadsworth Int. Group, 1984, 37(15): 237–251
59 L Breiman. Random forests. Machine Learning, 2001, 45(1): 5–32
60 W Y Loh. Classification and regression trees. Wiley Interdisciplinary Reviews, Data Mining and Knowledge Discovery, 2011, 1(1): 14–23
61 J Sadler, J L Goodall, M M Morsy, K Spencer. Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest. Journal of Hydrology (Amsterdam), 2018, 559: 43–55
62 H Sun, D Gui, B Yan, Y Liu, W Liao, Y Zhu, C Lu, N Zhao. Assessing the potential of random forest method for estimating solar radiation using air pollution index. Energy Conversion and Management, 2016, 119: 121–129
63 T Xu, A J Valocchi, M Ye, F Liang. Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model. Water Resources Research, 2017, 53(5): 4084–4105
64 K E Taylor. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, D, Atmospheres, 2001, 106(D7): 7183–7192
Related articles from Frontiers Journals
[1] Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU. SPT based determination of undrained shear strength: Regression models and machine learning[J]. Front. Struct. Civ. Eng., 2020, 14(1): 185-198.
[2] Tanvi SINGH, Mahesh PAL, V. K. ARORA. Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree[J]. Front. Struct. Civ. Eng., 2019, 13(3): 674-685.
[3] Stella O. OBAZEE-IGBINEDION, Oludare OWOLABI. Pavement sustainability index for highway infrastructures: A case study of Maryland[J]. Front. Struct. Civ. Eng., 2018, 12(2): 192-200.
[4] Pijush Samui, Jagan J. Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach[J]. Front Struc Civil Eng, 2013, 7(2): 133-136.
Full text