Skip to main content
SpringerLink
Log in

Transfer learning for pavement performance prediction

  • Published:
International Journal of Pavement Research and Technology Aims and scope Submit manuscript

Abstract

Accurate pavement performance prediction models are essential to ensure optimal allocation of resources in maintenance management. These models are developed using inventory and monitoring data regarding pavement structure, climate, traffic, and condition.

However, numerous road agencies have limited pavement data. Due to the inexistence of historical data, data collection frequency, and/or quality issues, the amount of data available for the development of performance models is reduced. As a result, the resource allocati on process is significantly undermined.

This paper proposes a transfer learning approach to develop pavement performance prediction models in limited data contexts. The proposed transfer learning approach is based on a boosting algorithm. In particular, a modified version of the popular TrAdaBoost learning algorithm was used.

To test the proposed transfer learning approach, a case study was developed using data from the Long-Term Pavement Performance (LTPP) database and from the Portuguese road administration database.

The results of this work show that it is possible to develop accurate performance prediction models in limited data contexts when a transfer learning approach is applied. All the models resulting from this approach outperformed baseline models, especially in what regards long-term forecasts. The results also showed that the transfer learning models perform consistently over different time frames, with minor performance losses from one-step to multi-step forecasts.

The findings of this study should be of interest to road agencies facing limited data contexts and aiming to develop accurate prediction models that can improve their pavement management practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R. Haas, R. Hudson, L.C. Falls, Pavement asset management, John Wiley & Sons, 2015. https://doi.org/10.1002/9781119038849

    Google Scholar 

  2. S. Gowda, S. Jain, K. S. Patil, K. P. Biligiri, B. E. Prabhakar, Development of a Budget-Prioritized Pavement Management System based on Engineering Criteria, Int. J. Pavement Res. Technol. 8 (3) (2015) 206–220. https://doi.org/10.6135/ijprt.org.tw/2015.8(3).206

    Google Scholar 

  3. P. Marcelino, M. L. Antunes, E. Fortunato, Comprehensive performance indicators for road pavement condition assessment, Struct. Infrastruct. Eng. 14 (11) (2018) 1–13. https://doi.org/10.1080/15732479.2018.1446179

    Google Scholar 

  4. S. Cafiso, A. Di Graziano, D. G. Goulias, C. D'Agostino, Distress and profile data analysis for condition assessment in pavement management systems, Int. J. Pavement Res. Technol. 12 (5) (2019) 527–536. https://doi.org/10.1007/s42947-019-0063-7

    Google Scholar 

  5. T.A. Dawson, Evaluation of pavement management data and analysis of treatment effectiveness using multi-level treatment transition matrices, Michigan State University, East Lansing, Michigan, USA, 2012.

    Google Scholar 

  6. G. Y. Baladi, S. W. Haider, K. Chatti, L. Galehouse, T. A. Dawson, C. M. Dean, R. Muscott, N. Tecca, M. McClosky, Optimization of and Maximizing the Benefits from Pavement Management Data Collection, Federal Highway Administration, Washington, D.C, USA, 2009.

    Google Scholar 

  7. L.M. Pierce, G. McGovern, K.A. Zimmerman, Practical guide for quality management of pavement condition data collection, US Department of Transportation, Federal Highway Administration, Washington, D.C, USA, 2013.

    Google Scholar 

  8. R.L. Schmitt, S. Owusu-Ababio, K.D. Denn, Database Development for an HMA Pavement Performance Analysis System.No. MRUTC 07-11. Midwest Regional University Transportation Center, College of Engineering, Department of Civil and Environmental Engineering, University of Wisconsin, Madison, Wisconsin, USA, 2008.

    Google Scholar 

  9. A. Ferreira, L. Picado-Santos, Z. Wu, G. Flintsch, Selection of pavement performance models for use in the Portuguese PMS, Int. J. Pavement Eng. 12 (1) (2011) 87–97. https://doi.org/10.1080/10298436.2010.506538

    Google Scholar 

  10. L.N. Mills, N.O. Attoh-Okine, S. McNeil. Developing pavement performance models for Delaware, Transport. Res. Rec. 2304 (1) (2012) 97–103. https://doi.org/10.3141/2304-11

    Google Scholar 

  11. A. Ayed, Development of Empirical and Mechanistic Empirical performance Models at Project and Network Levels, (PhD Thesis), University of Waterloo, Canada, 2016.

    Google Scholar 

  12. P. Marcelino, M. L. Antunes, E. Fortunato, M. G. Gomes, Machine learning approach for pavement performance prediction, Int. J. Pavement Eng. (2019) 1–14. https://doi.org/10.1080/10298436.2019.1609673

    Google Scholar 

  13. S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22 (10) (2009) 1345–1359. https://doi.org/10.1109/TKDE.2009.191

    Google Scholar 

  14. Q. Zou, Y. Cao, Q. Li, Q. Mao, S. Wang, CrackTree: Automatic crack detection from pavement images, Pattern Recogn. Lett. 33 (2) (2012) 227–238. https://doi.org/10.1016/j.patrec.2011.11.004

    Google Scholar 

  15. H.C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R.M. Summers. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, In IEEE Trans. Med. Imaging. 35 (5) (2016) 1285–1298. https://doi.org/10.1109/tmi.2016.2528162

    Google Scholar 

  16. K. Gopalakrishnan, S.K. Khaitan, A. Choudhary, A. Agrawal. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection, Construction and Building Materials 157 (2017) 322–330. https://doi.org/10.1016/j.conbuildmat.2017.09.110

    Google Scholar 

  17. K. Zhang, H.D. Cheng, B. Zhang B. Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning, J. Comput. Civil Eng. 32 (2) (2018). https://doi.org/10.1061/(asce)cp.1943-5487.0000736

    Google Scholar 

  18. B. Kim, S. Cho, Automated vision-based detection of cracks on concrete surfaces using a deep learning technique, Sensors 18 (10) (2018) 3452. https://doi.org/10.3390/s18103452

    Google Scholar 

  19. W. R. Silva, D.S. Lucena DS, Concrete cracks detection based on deep learning image classification, In Multidisciplinary Digital Publishing Institute Proceedings, 2018. https://doi.org/10.3390/icem18-05387

    Google Scholar 

  20. K. Gopalakrishnan, H. Gholami, A. Vidyadharan, A. Choudhary, A. Agrawal, Crack damage detection in unmanned aerial vehicle images of civil infrastructure using pre-trained deep learning model, Int. J. Traffic Transp. Eng. 8 (1) (2018) 1–4. https://doi.org/10.7708/ijtte.2018.8(1).01

    Google Scholar 

  21. J. Garcke, T. Vanck. Importance weighted inductive transfer learning for regression. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Nancy, France, 2014, pp. 466–481. https://doi.org/10.1007/978-3-662-44848-9_30

    Google Scholar 

  22. R.E. Schapire, The boosting approach to machine learning: An overview. In: Denison D.D., Hansen M.H., Holmes C.C., Mallick B., Yu B. (eds) Nonlinear estimation and classification. Springer, New York, USA, 2003, pp. 149–171.

    Google Scholar 

  23. W. Dai, Q. Yang, G.R. Xue, Y. Yu, Boosting for transfer learning, In Proceedings of the 24th international conference on Machine learning, Oregon, USA, 2007, pp. 193–200. https://doi.org/10.1145/1273496.1273521

    Google Scholar 

  24. H. Drucker, Improving regressors using boosting techniques, In Proceedings of the 14th International Conference on Machine Learning, Nashville, USA, 1997, pp. 107–115.

    Google Scholar 

  25. D. Pardoe, P. Stone, Boosting for regression transfer, In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 2010, pp. 863–870.

    Google Scholar 

  26. Y. Freund, R. Schapire, A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55 (1997) 119–139. https://doi.org/10.1006/jcss.1997.1504

    MATH  Google Scholar 

  27. A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, In Proceedings of the 11th Annual Conference on Computational Learning Theory, Madison, USA, 1998, pp. 92–100.

    Google Scholar 

  28. M. Belkin, P. Niyogi, V. Sindhwani, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7 (Nov) (2006) 2399–434.

    Google Scholar 

  29. X. Zhu, Z. Ghahramani, Learning from labeled and unlabeled data with label propagation, Carnegie Mellon University, Pittsburgh, USA, 2002.

    Google Scholar 

  30. M.W. Sayers, S.M. Karamihas, The Little Book of Profiling, University of Michigan, Transportation Research Institute, Ann Arbor, Michigan, USA, 1998.

    Google Scholar 

  31. K. Golabi, P. Pereira, Innovative pavement management and planning system for road network of Portugal. J. Infrastructure Syst. 9 (2) (2003) 75–80. https://doi.org/10.1061/(ASCE)1076-0342(2003)9:2(75)

    Google Scholar 

  32. L. Picado-Santos, A. Ferreira, Development and implementation of a new pavement management system. In Proceedings of the 5th International Symposium on Maintenance and Rehabilitation of Pavements and Technological Control, Iowa City, USA, 2007, pp. 433–438.

    Google Scholar 

  33. L. Picado-Santos, A. Ferreira, Contributions to the development of the Portuguese road administration's pavement management system, In Proceedings of the 3rd European Pavement and Asset Management Conference 1138, Coimbra, Portugal, 2008, pp. 1–10.

    Google Scholar 

  34. A. Luz, L.G. Picado-Santos, Contribution to the quality index model in the Description of the national road network, Universidade de Lisboa, Instituto Superior Técnico, Lisbon, Portugal, 2011.

    Google Scholar 

  35. P. Marcelino, M. L. Antunes, E. Fortunato, Current international practices on pavement condition assessment, In Pavement and Asset Management: Proceedings of the World Conference on Pavement and Asset Management (WCPAM 2017), Milan, Italy, 2017.

    Google Scholar 

  36. S. Nassiri, M. H. Shafiee, A. Bayat, Development of roughness prediction models using Alberta transportation's pavement management system, Int. J. Pavement Res. Technol. 6 (6) (2013) 714–720. https://doi.org/10.6135/ijprt.org.tw/2013.6(6).714

    Google Scholar 

  37. Federal Highway Administration, National Performance Management Measures, Federal Register 80 (2), Washington, D.C, USA, 2015, pp. 326–393.

    Google Scholar 

  38. LTPP DataPave online, Long-Term Pavement Performance, (LTPP, 2018). Database. https://infopave.fhwa.dot.gov/ Accessed May 2018.

    Google Scholar 

  39. J. Osborne, Notes on the use of data transformations. Pract. Assess. Res. Eval. 8(1) (2002) 42–50.

    Google Scholar 

  40. L. Breiman, Random forests. Mach. Learn. 45 (1) (2001) 5–32. https://doi.org/10.1023/A:1010933404324

    MATH  Google Scholar 

  41. R.E. Schapire, The strength of weak learnability, Mach. Learn. 5 (2) (1990) 197–227. https://doi.org/10.1007/BF00116037

    Google Scholar 

  42. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res. 12 (9) (2011) 2825–2830.

    MathSciNet  MATH  Google Scholar 

  43. R.W. Perera, S.D. Kohn, Issues in pavement smoothness, National Research Council, Transportation Research Board, Washington, D. C., USA, 2002.

    Google Scholar 

  44. N. Abdelaziz, R. El-Hakim, S. El-Badawy, H. Afify, International Roughness Index prediction model for flexible pavements. Int. J. Pavement Eng. 21 (1) (2018) 1–12. https://doi.org/10.1080/10298436.2018.1441414

    Google Scholar 

  45. G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning, Springer, New York, USA, 2013.[dataset]

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by Fundação para a Ciência e a Tecnologia (FCT) [grant number SFRH/BD/129907/2017].

Author information

Authors and Affiliations

  1. National Laboratory for Civil Engineering, Avenida do Brasil 101, Lisbon, 1700-066, Portugal

    Pedro Marcelino, Maria de Lurdes Antunes & Eduardo Fortunato

  2. CERIS, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1, Lisbon, 1049-001, Portugal

    Marta Castilho Gomes

Authors
  1. Pedro Marcelino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria de Lurdes Antunes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eduardo Fortunato
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marta Castilho Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Marcelino.

Additional information

Peer review under responsibility of Chinese Society of Pavement Engineering.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marcelino, P., de Lurdes Antunes, M., Fortunato, E. et al. Transfer learning for pavement performance prediction. Int. J. Pavement Res. Technol. 13, 154–167 (2020). https://doi.org/10.1007/s42947-019-0096-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42947-019-0096-z

Keywords

Search

Navigation