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.
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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.
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Published In
Journal of Performance of Constructed Facilities
Volume 35 • Issue 6 • December 2021
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
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