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CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

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Abstract

The use of non-destructive testing (NDT) equipment, such as the falling weight deflectometer (FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale field accelerated pavement testing track named as RIOHTrack (Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm (CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59. The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Significantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

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References

  1. Zheng J, Lü S, Liu C. Technical system, key scientific problems and technical frontier of long-life pavement. Chin Sci Bull, 2020, 65: 3219–3229

    Article  Google Scholar 

  2. Wang Y, Chong D. Long-life flexible pavement: myth, reality, and the way forward. In: New Frontiers in Road and Airport Engineering. ASCE, Shanghai, 2015. 268–283

    Chapter  Google Scholar 

  3. Liu A, Li H, Zhang P. Long-term performance study of long life pavement pilot section in Jiangsu, China. In: Proceedings of the Transportation Research Congress 2016: Innovations in Transportation Research Infrastructure. ASCE, Beijing, 2018. 353–363

    Google Scholar 

  4. Huang W, Liang S M, Wei Y. Surface deflection-based reliability analysis of asphalt pavement design. Sci China Tech Sci, 2020, 63: 1824–1836

    Article  Google Scholar 

  5. Hong F, Chen D. Evaluation of asphalt overlay permanent deformation based on ground-penetrating radar technology. J Test Eval, 2016, 44: 20130241

    Article  Google Scholar 

  6. Fu G, Zhao Y, Zhou C. Determination of effective frequency range excited by falling weight deflectometer loading history for asphalt pavement. Const Building Mater, 2020, 235: 117792

    Article  Google Scholar 

  7. Wang X D. Design of pavement structure and material for full-scale test track. J Highw & Transp Res & Dev, 2017, 34–6: 30–37

    Google Scholar 

  8. Simonin J M, Villain G. Detection and survey of interface defects within a pavement structure with ultrasonic pulse echo. In: Proceedings of the 8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements. Nantes: Springer, 2016. 673–678

    Google Scholar 

  9. Zhao S, Al-Qadi IL, Wang S. Prediction of thin asphalt concrete overlay thickness and density using nonlinear optimization of GPR data. NDT E Int, 2018, 100: 20–30

    Article  Google Scholar 

  10. Wang D, Shi J. Study on infrared differential thermal non-destructive testing technology of the permeability of hot mix asphalt pavements. IOP Conf Ser-Earth Environ Sci, 2017, 69: 012109

    Article  Google Scholar 

  11. Loganathan K, et al. Mechanistic empirical estimation of remaining service life of flexible pavements based on simple deflection parameters: A case study for the state of Texas. In: Airfield and Highway Pavements 2019: Design, Construction, Condition Evaluation, and Management of Pavements. ASCE, Chicago, 2019. 294–305

    Chapter  Google Scholar 

  12. Gedafa D S, et al. Relationship between flexible pavement cracking and surface deflections. In: Proceedings of the Transportation Research Board 91st Annual Meeting. Transportation Research Board, 2012. 12–1350

  13. Garbowski T, PoŻarycki A. Multi-level backcalculation algorithm for robust determination of pavement layers parameters. Inverse Problems Sci Eng, 2017, 25: 674–693

    Article  MathSciNet  Google Scholar 

  14. Habbouche J, Hajj E Y, Sebaaly P E. Damage assessment for ME rehabilitation design of modified asphalt pavements: Challenges and findings. Transp Res Record, 2018, 2672: 228–241

    Article  Google Scholar 

  15. Ma X, Dong Z, Yu X. Monitoring the structural capacity of airfield pavement with built-in sensors and modulus back-calculation algorithm. Const Building Mater, 2018, 175: 552–561

    Article  Google Scholar 

  16. Zhang L, Zhou X, Wang X. Research progress of long-life asphalt pavement behavior based on the RIOHTrack full-scale accelerated loading test. Chin Sci Bull, 2020, 65: 3247–3258

    Article  Google Scholar 

  17. Wang X, Zhou X, Guan W. Characteristics and analysis of the mechanical response inside the structure of asphalt pavement. Chin Sci Bull, 2020, 65: 3298–3307

    Article  Google Scholar 

  18. Wang Y B, Zheng P, Peng T. Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives. Sci China Tech Sci, 2020, 63: 1600–1611

    Article  Google Scholar 

  19. Di Q F, Wu Z H, Chen T. Artificial intelligence method for predicting the maximum stress of an off-center casing under non-uniform ground stress with support vector machine. Sci China Tech Sci, 2020, 63: 2553–2561

    Article  Google Scholar 

  20. Wang R X, Gao X, Gao J M. An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems. Sci China Tech Sci, 2020, 63: 1–13

    Article  Google Scholar 

  21. Zhang W J, Qin J, Mei F. Short-term power load forecasting using integrated methods based on long short-term memory. Sci China Tech Sci, 2020, 63: 614–624

    Article  Google Scholar 

  22. Xiong J, Zhang T Y, Shi S Q. Machine learning of mechanical properties of steels. Sci China Tech Sci, 2020, 63: 1247–1255

    Article  Google Scholar 

  23. Fakhri M, Shahni Dezfoulian R. Pavement structural evaluation based on roughness and surface distress survey using neural network model. Const Building Mater., 2019, 204: 768–780

    Article  Google Scholar 

  24. Yang Q, Deng Y. Evaluation of cracking in asphalt pavement with stabilized base course based on statistical pattern recognition. Int J Pavement Eng, 2019, 20: 417–424

    Article  Google Scholar 

  25. Hussan S, Kamal M A, Hafeez I. Modelling asphalt pavement analyzer rut depth using different statistical techniques. Road Mater Pavement Des, 2020, 21: 117–142

    Article  Google Scholar 

  26. Karballaeezadeh N, Ghasemzadeh Tehrani H, Mohammadzadeh Shadmehri D. Estimation of flexible pavement structural capacity using machine learning techniques. Front Struct Civ Eng, 2020, 14: 1083–1096

    Article  Google Scholar 

  27. Xu X, Gu Y, Huang W. Structural optimization of steel-epoxy asphalt pavement based on orthogonal design and GA-BP algorithm. Crystals, 2021, 11: 417

    Article  Google Scholar 

  28. Li M, Wang H. Prediction of asphalt pavement responses from FWD surface deflections using soft computing methods. J Transp Eng Part B-Pave, 2018, 144: 04018014

    Article  Google Scholar 

  29. Li M, Wang H. Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. Int J Pavement Eng, 2019, 20: 490–498

    Article  Google Scholar 

  30. Zhang X, Ji C. Asphalt pavement roughness prediction based on gray GM(1,1—sin) model. Int J Comput Intell Syst, 2019, 12: 897

    Article  Google Scholar 

  31. Kaloop M R, Kumar D, Samui P. Particle swarm optimization algorithm-extreme learning machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases. Appl Sci, 2019, 9: 3221

    Article  Google Scholar 

  32. Liang C, Xu X, Chen H. Machine learning approach to develop a novel multi-objective optimization method for pavement material proportion. Appl Sci, 2021, 11: 835

    Article  Google Scholar 

  33. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2016. 785–794

  34. Friedman J H. Greedy function approximation: A gradient boosting machine. Ann Stat, 2001, 29: 1189–1232

    Article  MathSciNet  MATH  Google Scholar 

  35. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the ICNN’95-International Conference on Neural Networks. Perth, 1995. 1942–1948

  36. Shi Y. Particle swarm optimization: Developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, 2001. 81–86

  37. Shi Y, Eberhart R. A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation Proceedings. Anchorage, 1998. 69–73

  38. Zhu H, Wu Y. A PSO algorithm with high speed convergence. Control Decis, 2010, 25: 20–24

    MATH  Google Scholar 

  39. Yang C H, Gu L S, Gui W H. Particle swarm optimization algorithm with adaptive mutation. Comput Eng, 2008, 16: 188–190

    Google Scholar 

  40. Tang X, Zhou W, Zhang H, et al. Robot soccer defensive strategy based on multi-objective chaotic PSO. J Syst Simul, 2014, 26: 51–55

    Google Scholar 

  41. Song Y, Chen Z, Yuan Z. New chaotic PSO-based neural network predictive control for nonlinear process. IEEE Trans Neural Netw, 2007, 18: 595–601

    Article  Google Scholar 

  42. Zhou K, Qin J. PID controller parameters tuning of main steam temperature based on chaotic particle swarm optimization. In: Proceedings of the 2011 IEEE International Conference on Computer Science and Automation Engineering. Zhangjiajie, 2011. 647–650

  43. Cheng M Y, Huang K Y, Chen H M. K-means particle swarm optimization with embedded chaotic search for solving multidimensional problems. Appl Math Comput, 2012, 219: 3091–3099

    MathSciNet  MATH  Google Scholar 

  44. Xiang T, Liao X, Wong K. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Appl Math Comput, 2007, 190: 1637–1645

    MathSciNet  MATH  Google Scholar 

  45. Shi Y, Eberhart R C. Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation. Washington D.C., 1999. 1945–1950

  46. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9: 1735–1780

    Article  Google Scholar 

  47. Li C, Xiao F, Fan Y. An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit. Energies, 2019, 12: 1592

    Article  Google Scholar 

  48. Pedregosa F, Varoquaux G, Gramfortet A, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res, 2011, 12: 2825–2830

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mathematics, Southeast University, Nanjing, 210096, China

    ZhuoXuan Li & JinDe Cao

  2. School of Cyber Science & Engineering, Southeast University, Nanjing, 210096, China

    XinLi Shi

  3. Yonsei Frontier Lab, Yonsei University, Seoul, 120-749, Korea

    JinDe Cao

  4. Research Institute of Highway, Ministry of Transport, Beijing, 100088, China

    XuDong Wang

  5. Intelligent Transportation System Research Center, Southeast University, Nanjing, 210096, China

    Wei Huang

Authors
  1. ZhuoXuan Li
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  2. XinLi Shi
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  3. JinDe Cao
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  4. XuDong Wang
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  5. Wei Huang
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Corresponding author

Correspondence to JinDe Cao.

Additional information

This work was supported by the National Key Research and Development Program of China (Grant No. 2020YFA07I4300), the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084), and the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20200355).

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Li, Z., Shi, X., Cao, J. et al. CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction. Sci. China Technol. Sci. 65, 1470–1481 (2022). https://doi.org/10.1007/s11431-021-1972-7

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  • DOI: https://doi.org/10.1007/s11431-021-1972-7

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