Using PSO-SVR Algorithm to Predict Asphalt Pavement Performance
Publication: Journal of Performance of Constructed Facilities
Volume 35, Issue 6
Abstract
Because of the relatively low accuracy of the current asphalt pavement performance prediction, a new pavement performance prediction model was established based on the particle swarm optimization (PSO) algorithm and support vector machine regression (SVR) algorithm. First, the SVR algorithm was introduced into the model to deal with the nonlinear regression. Then the PSO algorithm was applied to improve the searching efficiency and parameter continuity of the SVR algorithm. The pavement inspection data of an expressway in eastern China from 2006 to 2015 were used to verify the results, proving the feasibility of the PSO-SVR prediction model. The research results show that the model using particle swarm optimization has a fast convergence speed, and the optimized support vector machine has better rutting prediction performance and perfect generalization, and the prediction accuracy and reliability are higher than those of unoptimized support vector machine model.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (No. 51978068), the National Key R&D Program of China (No. 2018YFE0103800), the Shaanxi Provincial Science and Technology Department (No. 2020JM-217), the China Postdoctoral Science Foundation (No. 2017M620434), and the Special Fund for Basic Scientific Research of Central College of Chang’an University (No. 310821173501). The authors gratefully acknowledge their financial support.
References
Ahmad Jasmi, S. Z., M. F. Ayob, K. Abdul Rashid, and F. A. Mohd Rahim. 2018. “A review on the state of cost data inputs of life cycle cost (LCC) for rigid pavement maintenance and rehabilitation in Malaysia.” J. Des. Built Environ. 18: 26–38. https://doi.org/10.22452/jdbe.sp2018no1.3.
Bartoli, G., and M. Betti. 2013. “Cappella dei Principi in Firenze, Italy: Experimental analyses and numerical modeling for the investigation of a local failure.” J. Perform. Constr. Facil. 27 (1): 4–26. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000315.
Cai, L., Q. Chen, W. Cai, X. Xu, T. Zhou, and J. Qin. 2019. “SVRGSA: A hybrid learning based model for short-term traffic flow forecasting.” In IET intelligent transport systems, 1348–1355. London: Institution of Engineering and Technology.
Chen, D. H., and A. Wimsatt. 2010. “Inspection and condition assessment using ground penetrating radar.” J. Geotech. Geoenviron. Eng. 136 (1): 207–214. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000190.
Chen, D.-H., D.-F. Lin, and H.-L. Luo. 2003. “Effectiveness of preventative maintenance treatments using fourteen SPS-3 sites in Texas.” J. Perform. Constr. Facil. 17 (3): 136–143. https://doi.org/10.1061/(ASCE)0887-3828(2003)17:3(136).
Chowdhury, M. S. 2020. “Quantifying the effects of climate change on pavement performance.” Master of Science in Civil Engineering, Dept. of Civil Engineering, Boise State Univ.
Di Graziano, A., V. Marchetta, and S. Cafiso. 2020. “Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review.” J. Traffic Transp. Eng. 7 (5): 639–651. https://doi.org/10.1016/j.jtte.2020.08.001.
Duan, M. 2018. “Short-time prediction of traffic flow based on PSO optimized SVM.” In Proc., 2018 Int. Conf. on Intelligent Transportation, Big Data & Smart City (ICITBS), 41–45. New York: IEEE. https://doi.org/10.1109/ICITBS.2018.00018.
Elamrani Abou Elassad, Z., H. Mousannif, and H. Al Moatassime. 2020. “A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution.” Knowl.-Based Syst. 205 (Oct): 106314. https://doi.org/10.1016/j.knosys.2020.106314.
Fakhri, M., and R. Shahni Dezfoulian. 2019. “Pavement structural evaluation based on roughness and surface distress survey using neural network model.” Constr. Build. Mater. 204 (Apr): 768–780. https://doi.org/10.1016/j.conbuildmat.2019.01.142.
Frank, L. R., Y. M. Ferreira, E. P. Julio, F. H. C. Ferreira, B. J. Dembogurski, and E. F. Silva. 2019. “Multilayer perceptron and particle swarm optimization applied to traffic flow prediction on smart cities.” In Proc., Computational Science and Its Applications—ICCSA 2019, 35–47. New York: Springer International.
Gong, H., Y. Sun, X. Shu, and B. Huang. 2018. “Use of random forests regression for predicting IRI of asphalt pavements.” Constr. Build. Mater. 189 (Nov): 890–897. https://doi.org/10.1016/j.conbuildmat.2018.09.017.
Guo, X.-X., C. Zhang, B.-X. Cui, D. Wang, and J. Tsai. 2013. “Analysis of impact of transverse slope on hydroplaning risk level.” Procedia Social Behav. Sci. 96 (Nov): 2310–2319. https://doi.org/10.1016/j.sbspro.2013.08.260.
Hearst, M. A., S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. 1998. “Support vector machines.” IEEE Intell. Syst. Appl. 13 (4): 18–28. https://doi.org/10.1109/5254.708428.
Heba, A., and G. J. Assaf. 2018. “Bayes linear regression performance model depending on experts’ knowledge and current road condition.” Adv. Civ. Eng. Mater. 7 (3): 20170115. https://doi.org/10.1520/ACEM20170115.
Kennedy, J., and R. Eberhart. 1995. “Particle swarm optimization.” In Vol. 1944 of Proc., ICNN’95—Int. Conf. on Neural Networks, 1942–1948. New York: IEEE. https://doi.org/10.1109/ICNN.1995.488968.
Lai, Y., S. Easa, D. Sun, and Y. Wei. 2020. “Bus arrival time prediction using wavelet neural network trained by improved particle swarm optimization.” J. Adv. Transp. 2020 (Jan): 1–9. https://doi.org/10.1155/2020/7672847.
Lee, K.-W. W., K. Wilson, and S. A. Hassan. 2017. “Prediction of performance and evaluation of flexible pavement rehabilitation strategies.” J. Traffic Transp. Eng. 4 (2): 178–184.
Lee, Y.-J., S. Jong-Wan, and M.-J. 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, H., M. Lin, and Q. Wang. 2019. “Prediction of performance of expressway asphalt pavement based on IFA-SVM.” J. Highway Transp. Res. Dev. 36 (12): 8–14. https://doi.org/10.1061/JHTRCQ.0000738.
Li, N., J. Xu, and T. Xu. 2021. “Preparation, properties and modification mechanism of vulcanized eucommia ulmoides gum modified asphalt.” Constr. Build. Mater. 274 (Mar): 121992. https://doi.org/10.1016/j.conbuildmat.2020.121992.
Li, X., and Q. Wang. 2019. “Prediction of surrounding rock classification of highway tunnel based on PSO-SVM.” In Proc., 2019 Int. Conf. on Robots & Intelligent System (ICRIS), 443–446. New York: IEEE. https://doi.org/10.1109/ICRIS.2019.00116.
Liu, H., W. Guo, C. Zhang, and H. Yang. 2019. “Research on the Grey Verhulst model based on particle swarm optimization and Markov chain to predict the settlement of high fill subgrade in Xiangli expressway.” In Mathematical problems in engineering. London: Hindawi. https://doi.org/10.1155/2019/1878296.
Lourenço, P. B., and P. Medeiros. 2013. “Learning from failure of a long curved veneer wall: Structural analysis and repair.” J. Perform. Constr. Facil. 27 (1): 53–64. https://doi.org/10.1061/(asce)cf.1943-5509.0000313.
Ministry of Transport of the People’s Republic of China. 2018. Highway performance assessment standards. JTG 5210-2018. Beijing: China Communications Press.
Naito, C., D. Cox, Q.-S. K. Yu, and H. Brooker. 2013. “Fuel storage container performance during the 2011 Tohoku, Japan, Tsunami.” J. Perform. Constr. Facil. 27 (4): 373–380. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000339.
Nguyen-Le, D. H., Q. B. Tao, V.-H. Nguyen, M. Abdel-Wahab, and H. Nguyen-Xuan. 2020. “A data-driven approach based on long short-term memory and hidden Markov model for crack propagation prediction.” Eng. Fract. Mech. 235 (Aug): 107085. https://doi.org/10.1016/j.engfracmech.2020.107085.
Pan, N.-F., C.-H. Ko, M.-D. Yang, and K.-C. Hsu. 2011. “Pavement performance prediction through fuzzy regression.” Expert Syst. Appl. 38 (8): 10010–10017. https://doi.org/10.1016/j.eswa.2011.02.007.
Parsa, A. B., H. Taghipour, S. Derrible, and A. Mohammadian. 2019. “Real-time accident detection: Coping with imbalanced data.” Accid. Anal. Prev. 129 (Aug): 202–210. https://doi.org/10.1016/j.aap.2019.05.014.
People’s Communications Press. 2017. Specifications for design of highway asphalt pavement. Beijing: People’s Communications Press.
Premkumar, L., and W. R. Vavrik. 2016. “Enhancing pavement performance prediction models for the Illinois Tollway system.” Int. J. Pavement Res. Technol. 9 (1): 14–19. https://doi.org/10.1016/j.ijprt.2015.12.002.
Qiu, J., X. Liu, and R. Liu. 2018. “Short-term traffic state prediction based on support vector machine.” In Proc., 2018 World Transport Convention. New York: IEEE. https://doi.org/10.1109/TITS.2013.2258916.
Sheng, X., T. Xu, and M. Wang. 2020. “Preparation, shape memory performance and microstructures of emulsified asphalt modified by multi-walled carbon nanotubes.” Constr. Build. Mater. 230 (Jan): 116954. https://doi.org/10.1016/j.conbuildmat.2019.116954.
Wan, A., and J. Fang. 2020. “Risk prediction of expressway PPP project based on PSO-SVM algorithm.” In Proc., ICCREM 2020, 55–63. Reston, VA: ASCE. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000313.
Wang, D., A. Cannone Falchetto, M. Goeke, W. Wang, T. Li, and M. P. Wistuba. 2017. “Influence of computation algorithm on the accuracy of rut depth measurement.” J. Traffic Transp. Eng. 4 (2): 156–164. https://doi.org/10.1016/j.jtte.2017.03.001.
Wang, X., J. Zhao, Q. Li, N. Fang, P. Wang, L. Ding, and S. Li. 2020a. “A hybrid model for prediction in asphalt pavement performance based on support vector machine and grey relation analysis.” J. Adv. Transp. 2020 (Feb): 1–14. https://doi.org/10.1155/2020/7534970.
Wang, Z., N. Guo, X. Yang, and S. Wang. 2020b. “Micromechanical prediction model of viscoelastic properties for asphalt mastic based on morphologically representative pattern approach.” Adv. Mater. Sci. Eng. 2020 (Jun): 1–12. https://doi.org/10.1155/2020/7915140.
Xu, J., Y. Zhao, N. Liang, and M. Qin. 2018. “Life prediction of high modulus asphalt pavement based on fatigue cumulative damage.” J. Chang Univ. Nat. Sci. Ed. 38 (2): 26–33.
Yang, S., M. Guo, X. Liu, P. Wang, Q. Li, and H. Liu. 2019. “Highway performance evaluation index in semiarid climate region based on fuzzy mathematics.” In Advances in materials science and engineering. London: Hindawi. https://doi.org/10.1155/2019/6708102.
Zhang, C., Y. Tian, and N. Deng. 2010. “The new interpretation of support vector machines on statistical learning theory.” Sci. China Ser. A Math. 53 (1): 151–164. https://doi.org/10.1007/s11425-010-0018-6.
Zhang, D., X. Li, Y. Zhang, and H. Zhang. 2019. “Prediction method of asphalt pavement performance and corrosion based on grey system theory.” Int. J. Corros. 2019: 1–9. https://doi.org/10.1155/2019/2534794.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 28, 2021
Accepted: Jul 29, 2021
Published online: Oct 4, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 4, 2022
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.
Cited by
- Zhiyuan Luo, Hui Wang, Shenglin Li, Prediction of International Roughness Index Based on Stacking Fusion Model, Sustainability, 10.3390/su14126949, 14, 12, (6949), (2022).
- Ran Duan, Jie Liu, Jianzhong Zhou, Pei Wang, Wei Liu, An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions, Sensors, 10.3390/s22020525, 22, 2, (525), (2022).
- Hongjian Zhang, Wei Xiong, Ruicheng Zhang, Hao Su, Prediction of gas consumption based on LSTM-BPNN hybrid model, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 10.1080/15567036.2022.2157520, 44, 4, (10665-10680), (2022).
- Tong-tong Dai, Liang Ren, Zi-guang Jia, Ye-tian Li, Yang Li, Crack extension identification based on distributed fiber sensing measurement and optimized support vector regression, Ocean Engineering, 10.1016/j.oceaneng.2021.110515, 245, (110515), (2022).
- Chunfeng Zhao, Yufu Zhu, Zhihang Zhou, Machine learning-based approaches for predicting the dynamic response of RC slabs under blast loads, Engineering Structures, 10.1016/j.engstruct.2022.115104, 273, (115104), (2022).