Deep Learning–Based Pavement Performance Modeling Using Multiple Distress Indicators and Road Work History
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 149, Issue 1
Abstract
The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can help maximize the use of available resources for pavement management agencies through better coordinated preservation and maintenance activities. This paper uses deep neural networks such as the convolutional neural network (CNN) and the long short-term memory (LSTM) to model the pavement deterioration process. In this paper, pavement condition data and maintenance and rehabilitation history collected by the Texas Department of Transportation over the past 18 years were used. Twenty-one flexible pavement condition indicators, including cracking, rutting, raveling, and roughness, collected from more than 100,000 pavement sections were included in the proposed models. Promising preliminary results were obtained. Case study results show that the proposed CNN model outperforms standard machine learning models in predicting pavement condition values.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Because of a nondisclosure agreement with the sponsor (TxDOT), pavement condition and maintenance work data used in this research are not available to the public. But all of the models and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Texas Department of Transportation Grant (Project No. 0-6988). The authors would like to thank all Texas Department of Transportation personnel, who have helped this research study. All opinions, errors, omissions, and recommendations in this paper are the responsibility of the authors.
References
Assaad, M., R. Boné, and H. Cardot. 2008. “A new boosting algorithm for improved time-series forecasting with recurrent neural networks.” Inf. Fusion 9 (1): 41–55. https://doi.org/10.1016/j.inffus.2006.10.009.
Attoh-Okine, N. O. 1994. “Predicting roughness progression in flexible pavements using artificial neural networks.” In Proc., Transportation Research Board Conf. Washington, DC: Transportation Research Board.
Attoh-Okine, N. O. 1999. “Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance.” Adv. Eng. Software 30 (4): 291–302. https://doi.org/10.1016/S0965-9978(98)00071-4.
Black, M., A. Brint, and J. Brailsford. 2005. “A semi-Markov approach for modelling asset deterioration.” J. Oper. Res. Soc. 56 (11): 1241–1249. https://doi.org/10.1057/palgrave.jors.2601967.
Butt, A. A., M. Shahin, S. Carpenter, and J. Carnahan. 1994. “Application of markov process to pavement management systems at network level.” In Proc., 3rd Int. Conf. on Managing Pavements. Princeton, NJ: Citeseer.
Chen, D.-H., J. Bilyeu, T. Scullion, D.-F. Lin, and F. Zhou. 2003. “Forensic evaluation of premature failures of Texas Specific Pavement Study–1 Sections.” J. Perform. Constr. Facil. 17 (2): 67–74. https://doi.org/10.1061/(ASCE)0887-3828(2003)17:2(67).
Choi, J.-H., T. M. Adams, and H. U. Bahia. 2004. “Pavement roughness modeling using back-propagation neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 19 (4): 295–303. https://doi.org/10.1111/j.1467-8667.2004.00356.x.
Choi, S., and M. Do. 2020. “Development of the road pavement deterioration model based on the deep learning method.” Electronics 9 (1): 3. https://doi.org/10.3390/electronics9010003.
Chollet, F., et al. 2015. “Keras.” Accessed August 10, 2020. https://github.com/fchollet/keras.
Chu, C.-Y., and P. L. Durango-Cohen. 2008. “Empirical comparison of statistical pavement performance models.” J. Infrastruct. Syst. 14 (2): 138–149. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:2(138).
Cui, Z., W. Chen, and Y. Chen. 2016. “Multi-scale convolutional neural networks for time series classification.” Preprint, submitted May 11, 2016. https://arxiv.org/abs/1603.06995.
Fukushima, K. 1979. “Neural network model for a mechanism of pattern recognition unaffected by shift in position-neocognitron.” IEICE Tech. Rep. A 62 (10): 658–665.
Gajewski, J., and T. Sadowski. 2014. “Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating artificial neural networks and finite element method.” Comput. Mater. Sci. 82 (Sep): 114–117. https://doi.org/10.1016/j.commatsci.2013.09.025.
Gao, L., J. P. Aguiar-Moya, and Z. Zhang. 2011. “Bayesian analysis of heterogeneity in modeling of pavement fatigue cracking.” J. Comput. Civ. Eng. 26 (1): 37–43. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000114.
Gao, L., Z. Zhang, and S. L. Tighe. 2007. “Using Markov process and method of moments for optimizing management strategies of pavement infrastructure.” In Proc., Transportation Research Board 86th Annual Meeting. Washington, DC: Transportation Research Board.
Gharaibeh, N., et al. 2012. Evaluation and development of pavement scores, performance models and needs estimates for the TXDOT pavement management information system. Austin, TX: Texas Transportation Institute.
Golabi, K., R. B. Kulkarni, and G. B. Way. 1982. “A statewide pavement management system.” Interfaces 12 (6): 5–21. https://doi.org/10.1287/inte.12.6.5.
Goldberg, Y. 2016. “A primer on neural network models for natural language processing.” J. Artif. Intell. Res. 57 (4): 345–420. https://doi.org/10.1613/jair.4992.
Han, Z., J. D. Porras-Alvarado, C. Stone, and Z. Zhang. 2019. “Incorporating uncertainties into determination of flexible pavement preventive maintenance interval.” Transp. A: Transp. Sci. 15 (1): 34–54. https://doi.org/10.1080/23249935.2018.1433730.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Hong, F., and J. A. Prozzi. 2010. “Roughness model accounting for heterogeneity based on in-service pavement performance data.” J. Transp. Eng. 136 (3): 205–213. https://doi.org/10.1061/(ASCE)0733-947X(2010)136:3(205).
Hong, F., and J. A. Prozzi. 2015. “Pavement deterioration model incorporating unobserved heterogeneity for optimal life-cycle rehabilitation policy.” J. Infrastruct. Syst. 21 (1): 04014027. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000168.
Jiang, Y., M. Saito, and K. C. Sinha. 1988. Bridge performance prediction model using the Markov chain. Transportation Research Record 1180, 25–32. Washington, DC: Transportation Research Board.
Kobayashi, K., K. Kaito, and N. Lethanh. 2012. “A statistical deterioration forecasting method using hidden Markov model for infrastructure management.” Transp. Res. Part B Methodol. 46 (4): 544–561. https://doi.org/10.1016/j.trb.2011.11.008.
Krbaş, U., and M. Karaşahin. 2016. “Performance models for hot mix asphalt pavements in urban roads.” Constr. Build. Mater. 116 (Jul): 281–288. https://doi.org/10.1016/j.conbuildmat.2016.04.118.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “Imagenet classification with deep convolutional neural networks.” Commun. ACM 60 (6): 84–90.
LeCun, Y., B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. 1989. “Handwritten digit recognition with a back-propagation network.” Adv. Neural Inf. Process. Syst. 2.
LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-based learning applied to document recognition.” Proc. IEEE 86 (11): 2278–2324. https://doi.org/10.1109/5.726791.
Lee, Y., J. Sun, and M. Lee. 2019. “Development of deep learning based deterioration prediction model for the maintenance planning of highway pavement.” Korean J. Constr. Eng. Manage. 20 (6): 34–43. https://doi.org/10.6106/KJCEM.2019.20.6.034.
Li, N., W.-C. Xie, and R. Haas. 1996. “Reliability-based processing of markov chains for modeling pavement network deterioration.” Transp. Res. Rec. 1524 (1): 203–213. https://doi.org/10.1177/0361198196152400124.
Li, Z. 2005. “A probabilistic and adaptive approach to modeling performance of pavement infrastructure.” Ph.D. thesis, Univ. of Texas at Austin.
Madanat, S., S. Bulusu, and A. Mahmoud. 1995. “Estimation of infrastructure distress initiation and progression models.” J. Infrastruct. Syst. 1 (3): 146–150. https://doi.org/10.1061/(ASCE)1076-0342(1995)1:3(146).
Morcous, G., and Z. Lounis. 2005. “Prediction of onset of corrosion in concrete bridge decks using neural networks and case-based reasoning.” Comput.-Aided Civ. Infrastruct. Eng. 20 (2): 108–117. https://doi.org/10.1111/j.1467-8667.2005.00380.x.
Ogunmolu, O., X. Gu, S. Jiang, and N. Gans. 2016. “Nonlinear systems identification using deep dynamic neural networks.” Preprint, submitted October 5, 2016. https://arxiv.org/abs/1610.01439.
Ortiz-Garca, J. J., S. B. Costello, and M. S. Snaith. 2006. “Derivation of transition probability matrices for pavement deterioration modeling.” J. Transp. Eng. 132 (2): 141–161. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:2(141).
Owusu-Ababio, S. 1998. “Application of neural networks to modeling thick asphalt pavement performance.” Artif. Intell. Math. Methods Pavement Geomechanical Syst. 1998 (Nov): 23–30.
Pantuso, A., G. W. Flintsch, S. W. Katicha, and G. Loprencipe. 2019. “Development of network-level pavement deterioration curves using the linear empirical Bayes approach.” Int. J. Pavement Eng. 22 (6): 780–793. https://doi.org/10.1080/10298436.2019.1646912.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (2011): 2825–2830.
Pérez-Acebo, H., S. Bejan, and H. Gonzalo-Orden. 2018. “Transition probability matrices for flexible pavement deterioration models with half-year cycle time.” Int. J. Civ. Eng. 16 (9): 1045–1056. https://doi.org/10.1007/s40999-017-0254-z.
Politis, S. S., Z. Zhang, Z. Han, J. J. Hasenbein, and M. Arellano. 2020. “Stochastic analysis of network-level bridge maintenance needs using Latin hypercube sampling.” J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 7 (1): 04020049. https://doi.org/10.1061/AJRUA6.0001099.
Prozzi, J., and S. Madanat. 2000. “Using duration models to analyze experimental pavement failure data.” Transp. Res. Rec. 1699 (1): 87–94. https://doi.org/10.3141/1699-12.
Prozzi, J. A., and S. M. Madanat. 2004. “Development of pavement performance models by combining experimental and field data.” J. Infrastruct. Syst. 10 (1): 9–22. https://doi.org/10.1061/(ASCE)1076-0342(2004)10:1(9).
Simonyan, K., and A. Zisserman. 2014. “Very deep convolution networks for large-scale image recognition.” Preprint, submitted April 10, 2015. https://arxiv.org/abs/1409.1556.
Tabatabaee, N., M. Ziyadi, and Y. Shafahi. 2013. “Two-stage support vector classifier and recurrent neural network predictor for pavement performance modeling.” J. Infrastruct. Syst. 19 (3): 266–274. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000132.
Thomas, O., and J. Sobanjo. 2013. “Comparison of Markov chain and semi-Markov models for crack deterioration on flexible pavements.” J. Infrastruct. Syst. 19 (2): 186–195. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000112.
Thube, D. T. 2012. “Artificial neural network (Ann) based pavement deterioration models for low volume roads in India.” Int. J. Pavement Res. Technol. 5 (2): 115.
Wang, K. C. P., J. Zaniewski, and G. Way. 1994. “Probabilistic behavior of pavements.” J. Transp. Eng. 120 (3): 358–375. https://doi.org/10.1061/(ASCE)0733-947X(1994)120:3(358).
Wang, Y., K. C. Mahboub, and D. E. Hancher. 2005. “Survival analysis of fatigue cracking for flexible pavements based on long-term pavement performance data.” J. Transp. Eng. 131 (8): 608–616. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:8(608).
Wang, Y., K. C. Mahboub, and D. E. Hancher. 2008. “Dynamic panel data model for predicting performance of asphalt concrete overlay.” J. Transp. Eng. 134 (2): 86–92. https://doi.org/10.1061/(ASCE)0733-947X(2008)134:2(86).
Wang, Z., W. Yan, and T. Oates. 2017. “Time series classification from scratch with deep neural networks: A strong baseline.” In Proc., 2017 Int. Joint Conf. on Neural Networks, 1578–1585. New York: IEEE.
Zhang, Z., and I. Damnjanović. 2006. “Applying method of moments to model reliability of pavements infrastructure.” J. Transp. Eng. 132 (5): 416–424. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:5(416).
Zhang, Z., and L. Gao. 2016. “A nested modelling approach to infrastructure performance characterization.” Int. J. Pavement Eng. 19 (2): 174–180. https://doi.org/10.1080/10298436.2016.1172712.
Ziari, H., and M. M. Khabiri. 2007. “Interface condition influence on prediction of flexible pavement life.” J. Civ. Eng. Manage. 13 (1): 71–76. https://doi.org/10.3846/13923730.2007.9636421.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 149 • Issue 1 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 28, 2021
Accepted: Jul 10, 2022
Published online: Oct 22, 2022
Published in print: Mar 1, 2023
Discussion open until: Mar 22, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.