Estimation of flexible pavement structural capacity using machine learning techniques

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

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

[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

[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

[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

[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

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

[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

[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

[9] Deng Y, Yang Q. 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] 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

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

[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

[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

[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

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

[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

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

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

[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

[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

[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

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

[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

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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

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

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

[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

[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

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

[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

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

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

[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

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

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

[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

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

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

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

[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

[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

[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

[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

RIGHTS & PERMISSIONS

2020 Higher Education Press