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Estimation of flexible pavement structural capacity using machine learning techniques

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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.

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References

  1. Uddin Ahmed Zihan Z, Elseifi M A, Gaspard K, Zhang Z. 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

    Google Scholar 

  2. Onyango M, Merabti S A, Owino J, Fomunung I, Wu W. 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

    Google Scholar 

  3. Kuo C M, Tsai T Y. Significance of subgrade damping on structural evaluation of pavements. Road Materials and Pavement Design, 2014, 15(2): 455–464

    Google Scholar 

  4. Lytton R L. Concepts of pavement performance prediction and modeling. In: Proceeddings of the 2nd North American Conference on Managing Pavements. Toronto, Ontario: Ministry of Transportation, 1987

    Google Scholar 

  5. Kargah-Ostadi N, Zhou Y, Rahman T. 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

    Google Scholar 

  6. Elbagalati O, Elseifi M, Gaspard K, Zhang Z. Development of the pavement structural health index based on falling weight deflect-ometer testing. International Journal of Pavement Engineering, 2018, 19(1): 1–8

    Google Scholar 

  7. Abd El-Raof H S, Abd El-Hakim R T, El-Badawy S M, Afify H A. 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

    Google Scholar 

  8. Moghadas Nejad F, Zare Motekhases F, Zakeri H, Mehrabi A. An image processing approach to asphalt concrete feature extraction. Journal of Industrial and Intelligent Information, 2015, 3(1): 54–60

    Google Scholar 

  9. Deng Y, Yang Q. Rapid evaluation of a transverse crack on a semirigid pavement utilising deflection basin data. Road Materials and Pavement Design, 2019, 20(4): 929–942

    Google Scholar 

  10. Karballaeezadeh N, Mohammadzadeh S D, Shamshirband S, Hajikhodaverdikhan P, Mosavi A, Chau K. 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

    Google Scholar 

  11. Pinkofsky L, Jansen D. Structural pavement assessment in Germany. Frontiers of Structural and Civil Engineering, 2018, 12 (2): 183–191

    Google Scholar 

  12. Liu P, Wang D, Otto F, Oeser M. 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

    Google Scholar 

  13. Dai S, Yan Q. 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

    Google Scholar 

  15. Chen R, Zhang P, Wu H, Wang Z, Zhong Z. Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 2019, 13(6): 1363–1378

    Google Scholar 

  16. Abed A, Thom N, Neves L. Probabilistic prediction of asphalt pavement performance. Road Materials and Pavement Design, 2019, 20(Sup 1): 1–18

    Google Scholar 

  17. Abaza K A. Deterministic performance prediction model for rehabilitation and management of flexible pavement. International Journal of Pavement Engineering, 2004, 5(2): 111–121

    Google Scholar 

  18. Dalla Valle P. Reliability in Pavement Design. Nottingham: University of Nottingham, 2015

    Google Scholar 

  19. Huang Y H. Pavement Analysis and Design. Englewood Cliffs, NJ: Prentice-Hall, 1993

    Google Scholar 

  20. Kargah-Ostadi N, Stoffels S M, Tabatabaee N. Network-level pavement roughness prediction model for rehabilitation recommendations. Transportation Research Record: Journal of the Transportation Research Board, 2010, 2155(1): 124–133

    Google Scholar 

  21. Yang J, Lu J J, Gunaratne M, Xiang Q. 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

    Google Scholar 

  22. Kargah-Ostadi N, Stoffels S M. Framework for development and comprehensive comparison of empirical pavement performance models. Journal of Transportation Engineering, 2015, 141(8): 04015012

    Google Scholar 

  23. Noureldin A S. New scenario for backcalculation of layer moduli of flexible pavements. Transportation Research Record, 1993, 1384: 23–28

    Google Scholar 

  24. Jameson G. Development of Procedures to Predict Structural Number and Subgrade Strength from Falling Weight Deflectometer Deflections. Vermont South, Victoria: ARRB Transport Research, 1993

    Google Scholar 

  25. Rohde G T. Determining pavement structural number from FWD testing. Transportation Research Record, 1994, 1(1448): 61–68

    Google Scholar 

  26. Rohde G T, Jooste F, Sadzik E, Henning T. 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

    Google Scholar 

  27. Schnoor H, Horak E. 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

    Google Scholar 

  28. European Cooperation in Science and Technology (COST). Information gathering work of Task Group 2, in Falling Weight Deflectometer (COST 336). 1998

  29. Crook A L, Montgomery S R, Guthrie W S. 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

    Google Scholar 

  30. Dasari K V. Deflection based condition assessment for Rolling Wheel Deflectometer at network level. Thesis for the Master’s Degree. Louisiana: Louisiana State University, 2013

    Google Scholar 

  31. Hoffman M S. 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

    Google Scholar 

  32. Abdel-Khalek A M, Elseifi M A, Gaspard K, Zhang Z, Dasari K. 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

    Google Scholar 

  33. Kim M Y, Kim D Y, Murphy M R. 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

    Google Scholar 

  34. Abd El-Raof H S, Abd El-Hakim R T, El-Badawy S M, Afify H. 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

    Google Scholar 

  35. Gedafa D S, Hossain M, Miller R, Van T. Network-level flexible pavement structural evaluation. International Journal of Pavement Engineering, 2014, 15(4): 309–322

    Google Scholar 

  36. Elbagalati O, Elseifi M A, Gaspard K, Zhang Z. Prediction of in-service pavement structural capacity based on traffic-speed deflection measurements. Journal of Transportation Engineering, 2016, 142(11): 04016058

    Google Scholar 

  37. Kavussi A, Abbasghorbani M, Moghadas Nejad F, Bamdad Ziksari A. 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

    Google Scholar 

  38. Salvi R, Ramdasi A, Kolekar Y A, Bhandarkar L V. Use of Ground-Penetrating Radar (GPR) as An Effective Tool in Assessing Pavements—A Review. Geotechnics for Transportation Infrastructure, Singapore: Springer, 2019, 85–95

    Google Scholar 

  39. Benedetto A, Tosti F, Bianchini Ciampoli L, D’Amico F. An overview of ground-penetrating radar signal processing techniques for road inspections. Signal Processing, 2017, 132: 201–209

    Google Scholar 

  40. Horak E, Maina J W, Van Wijk I, Hefer A, Jordaan G, Olivier P, de Bruin P W. Revision of the South African Pavement Design Method. Draft Contract Report, No. SANRAL/SAPDM/B-2/2009-01. 2009

  41. Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff Plate. Computers, Materials & Continua, 2019, 59(2): 433–456

    Google Scholar 

  42. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

    Google Scholar 

  43. Hamdia K M, Ghasemi H, Zhuang X, Alajlan N, Rabczuk T. Computational machine learning representation for the flexoelectricity effect in truncated pyramid structures. Computers, Materials & Continua, 2019, 59(1): 79–87

    Google Scholar 

  44. Hamdia K M, Ghasemi H, Bazi Y, AlHichri H, Alajlan N, Rabczuk T. 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

    MathSciNet  Google Scholar 

  45. Sun A Y, Wang D, Xu X. Monthly streamflow forecasting using Gaussian process regression. Journal of Hydrology (Amsterdam), 2014, 511: 72–81

    Google Scholar 

  46. Williams C K, Rasmussen C E. Gaussian processes for machine learning. International Journal of Neural Systems, 2004, 14(2): 69–106

    Google Scholar 

  47. Arthur C K, Temeng V A, Ziggah Y Y. Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers, 2020, 36: 29–42

    Google Scholar 

  48. Li J J, Jutzeler A, Faltings B. Estimating urban ultrafine particle distributions with gaussian process models. In: CEUR Workshop Proceedings. Canberra, 2014, 145–153

  49. Chu W, Ghahramani Z. Gaussian processes for ordinal regression. Journal of Machine Learning Research, 2005, 6: 1019–1041

    MathSciNet  MATH  Google Scholar 

  50. Samui P. Utilization of Gaussian process regression for determination of soil electrical resistivity. Geotechnical and Geological Engineering, 2014, 32(1): 191–195

    MathSciNet  Google Scholar 

  51. Samui P, Jagan J. Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach. Frontiers of Structural and Civil Engineering, 2013, 7(2): 133–136

    Google Scholar 

  52. Quinlan J R. Learning with continuous classes. In: The 5th Australian joint conference on artificial intelligence. Hobart, Tasmania: World Scientific, 1992

    Google Scholar 

  53. Wang Y, Witten I H. Induction of model trees for predicting continuous classes. Hamilton: University of Waikato, 1996

    Google Scholar 

  54. Singh T, Pal M, Arora V. 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

    Google Scholar 

  55. Behnood A, Behnood V, Modiri Gharehveran M, Alyamac K E. Prediction of the compressive strength of normal and highperformance concretes using M5P model tree algorithm. Construction & Building Materials, 2017, 142: 199–207

    Google Scholar 

  56. Witten I H, Frank E, Hall M A. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, MA: Morgan Kauffman, 2011

    Google Scholar 

  57. Blaifi S, Moulahoum S, Benkercha R, Taghezouit B, Saim A. M5P model tree based fast fuzzy maximum power point tracker. Solar Energy, 2018, 163: 405–424

    Google Scholar 

  58. Breiman L, Friedman J, Olshen R, Stone C. Classification and regression trees. Wadsworth Int. Group, 1984, 37(15): 237–251

    MATH  Google Scholar 

  59. Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32

    MATH  Google Scholar 

  60. Loh W Y. Classification and regression trees. Wiley Interdisciplinary Reviews, Data Mining and Knowledge Discovery, 2011, 1(1): 14–23

    Google Scholar 

  61. Sadler J, Goodall J L, Morsy M M, Spencer K. Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest. Journal of Hydrology (Amsterdam), 2018, 559: 43–55

    Google Scholar 

  62. Sun H, Gui D, Yan B, Liu Y, Liao W, Zhu Y, Lu C, Zhao N. Assessing the potential of random forest method for estimating solar radiation using air pollution index. Energy Conversion and Management, 2016, 119: 121–129

    Google Scholar 

  63. Xu T, Valocchi A J, Ye M, Liang F. 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

    Google Scholar 

  64. Taylor K E. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, D, Atmospheres, 2001, 106(D7): 7183–7192

    Google Scholar 

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Authors and Affiliations

  1. Civil Engineering Department, Shahrood University of Technology, Shahrood, 3619995161, Iran

    Nader Karballaeezadeh & Hosein Ghasemzadeh Tehrani

  2. Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran

    Danial Mohammadzadeh Shadmehri

  3. Department of Elite Relations with Industries, Khorasan Construction Engineering Organization, Mashhad, 9185816744, Iran

    Danial Mohammadzadeh Shadmehri

  4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Shahaboddin Shamshirband

  5. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Shahaboddin Shamshirband

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  1. Nader Karballaeezadeh
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  2. Hosein Ghasemzadeh Tehrani
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  3. Danial Mohammadzadeh Shadmehri
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  4. Shahaboddin Shamshirband
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Correspondence to Hosein Ghasemzadeh Tehrani or Shahaboddin Shamshirband.

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Karballaeezadeh, N., Ghasemzadeh Tehrani, H., Mohammadzadeh Shadmehri, D. et al. Estimation of flexible pavement structural capacity using machine learning techniques. Front. Struct. Civ. Eng. 14, 1083–1096 (2020). https://doi.org/10.1007/s11709-020-0654-z

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