Journal of Civil Structural Health Monitoring

, Volume 9, Issue 1, pp 91–102 | Cite as

Smart pothole detection system using vehicle-mounted sensors and machine learning

  • Ali AnaissiEmail author
  • Nguyen Lu Dang Khoa
  • Thierry Rakotoarivelo
  • Mehrisadat Makki Alamdari
  • Yang Wang
Original Paper


Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to managers and engineers. VRNI uses acceleration data from vehicle-mounted sensors to assess road conditions. It proposes a novel road damage detection method based on two adaptive one-class support vector machine models, which were applied on the vertical and lateral acceleration data. We evaluated this method on data from a real deployment on school buses in New South Wales, Australia. Experimental results show that our method consistently detects 97.5% of the road damage with a 4% false alarm rate that relate to benign anomalies such as expansion joints.


Road condition assessment Machine learning One-class support vector machine Accelerometers Sensors Pothole 



  1. 1.
    Alamdari MM, Rakotoarivelo T, Khoa NLD (2017) A spectral-based clustering for structural health monitoring of the sydney harbour bridge. Mech Syst Signal Process 87(Part A):384–400. Retrieved from
  2. 2.
    Anaissi A, Braytee A, Naji, M. (2018) Gaussian kernel parameter optimization in one-class support vector machines. In: 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–8Google Scholar
  3. 3.
    Anaissi A, Goyal M, Catchpoole DR, Braytee A, Kennedy PJ (2016) Ensemble feature learning of genomic data using support vector machine. PloS One 11(6):e0157330CrossRefGoogle Scholar
  4. 4.
    Anaissi A, Kennedy PJ, Goyal M (2011) Feature selection of imbalanced gene expression microarray data. In: Software engineering, artificial intelligence, networking and parallel/distributed computing (snpd), 2011 12th acis International Conference on, IEEE, pp 73–78Google Scholar
  5. 5.
    Anaissi A, Kennedy PJ, Goyal M, Catchpoole DR (2013) A balanced iterative random forest for gene selection from microarray data. BMC Bioinform 14(1):261CrossRefGoogle Scholar
  6. 6.
    Anaissi A, Khoa NLD, Alamdari MM, Wang Y, Mustapha S, Chen F (2017a) Adaptive one-class support vector machine for damage detection in structural health monitoring. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, pp 459–471Google Scholar
  7. 7.
    Anaissi A, Khoa NLD, Rakotoarivelo T, Alamdari MM, Wang Y (2017b) Self-advised incremental one-class support vector machines: an application in structural health monitoring. In: International Conference on Neural Information Processing, Springer, pp 484–496Google Scholar
  8. 8.
    Benjamin JR, Cornell CA (2014) Probability, statistics, and decision for civil engineers. Courier Corporation, ChelmsfordGoogle Scholar
  9. 9.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefzbMATHGoogle Scholar
  10. 10.
    Burningham S, Stankevich N (2005) Why road maintenance is important and how to get it done. The World Bank, Transport Note TRN-4Google Scholar
  11. 11.
    Chen Y, Zhou XS, Huang TS (2001) One-class svm for learning in image retrieval. In: Image Processing, 2001. Proceedings. 2001 International Conference on, vol 1, IEEE, pp 34–37Google Scholar
  12. 12.
    Clifford G (2005) Singular value decomposition and independent component analysis. J Biomed Image Process 93:49Google Scholar
  13. 13.
    Cong F, Hautakangas H, Nieminen J, Mazhelis O, Perttunen M, Riekki J, Ristaniemi T (2013) Applying wavelet packet decomposition and one-class support vector machine on vehicle acceleration traces for road anomaly detection. In: International Symposium on Neural Networks, Springer, pp 291–299Google Scholar
  14. 14.
    Cortes C, Vapnik V (1995) Support vector machine. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  15. 15.
    Eriksson J, Girod L, Hull B, Newton R, Madden S, Balakrishnan H (2008) The pothole patrol: using a mobile sensor network for road surface monitoring. In: proceedings of the 6th International Conference on Mobile systems, applications, and services, ACM, pp 29–39Google Scholar
  16. 16.
    Hampel FR (1974) The influence curve and its role in robust estimation. J Am Stat Assoc 69(346):383–393MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Harikumar R, Vijayakumar T, Sreejith M (2011) Performance analysis of svd and support vector machines for optimization of fuzzy outputs in classification of epilepsy risk level from eeg signals. In: Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE, IEEE, pp 718–723Google Scholar
  18. 18.
    Hassanpour H, Mesbah M, Boashash B (2004) Time-frequency feature extraction of newborn eeg seizure using svd-based techniques. EURASIP J Appl Signal Process 2004:2544–2554zbMATHGoogle Scholar
  19. 19.
    Jeff R (2006) Guidelines for measuring and reporting the condition of road assets.
  20. 20.
    Kemmler M, Rodner E, Wacker E-S, Denzler J (2013) One-class classification with gaussian processes. Pattern Recognit 46(12):3507–3518CrossRefGoogle Scholar
  21. 21.
    Khazai S, Homayouni S, Safari A, Mojaradi B (2011) Anomaly detection in hyperspectral images based on an adaptive support vector method. IEEE Geosci Remote Sens Lett 8(4):646–650CrossRefGoogle Scholar
  22. 22.
    Khoa NLD, Zhang B, Wang Y, Liu W, Chen F, Mustapha S, Runcie P (2015) On damage identification in civil structures using tensor analysis. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, pp 459–471Google Scholar
  23. 23.
    Perttunen M, Mazhelis O, Cong F, Kauppila M, Leppänen T, Kantola J, Collin J, Pirttikangas S, Haverinen J, Ristaniemi T, et al (2011) Distributed road surface condition monitoring using mobile phones. In: Ubiquitous Intelligence and Computing, Springer, pp 64–78Google Scholar
  24. 24.
    Runcie P, Mustapha S, Rakotoarivelo T (2014) Advances in structural health monitoring system architecture. In: International Symposium on Life-Cycle Civil Engineering, pp 1064 –1071Google Scholar
  25. 25.
    Schölkopf B, Williamson RC, Smola AJ, Shawe-Taylor J, Platt JC, et al (1999) Support vector method for novelty detection. In: NIPS, vol 12, Citeseer, pp 582–588Google Scholar
  26. 26.
    Shin K, Hammond J, White P (1999) Iterative svd method for noise reduction of low-dimensional chaotic time series. Mech Syst Signal Process 13(1):115–124CrossRefGoogle Scholar
  27. 27.
    Xiao Y, Wang H, Xu W (2015) Parameter selection of gaussian kernel for one-class svm. IEEE Trans Cybern 45(5):941–953CrossRefGoogle Scholar
  28. 28.
    Xiao Y, Wang H, Zhang L, Xu W (2014) Two methods of selecting gaussian kernel parameters for one-class svm and their application to fault detection. Knowl Based Syst 59:75–84CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and ITThe University of SydneyCamperdownAustralia
  2. 2.Data61|CSIRO, EveleighCanberraAustralia
  3. 3.School of Civil and Environmental EngineeringUniversity of New South WalesSydneyAustralia

Personalised recommendations