Impact analysis of traffic loading on pavement performance using support vector regression model

ABSTRACT

This study aims to use traditional regression model and machine learning method to analyse the impact of traffic loading on pavement performance. Pavement condition data were obtained from pavement management systems (PMS) and axle loads of truck traffic were collected at weigh-in-motion (WIM) stations. Support vector regression (SVR) method was selected for modelling pavement performance since it provides the flexibility to find the appropriate hyperplane in higher dimensions to fit the data and customise control errors in an acceptable range. Compared to traditional nonlinear regression model, the accuracy of pavement performance prediction was significantly increased by utilising the SVR method. The model accuracy was further improved by considering the number of axles and fitted Gaussian distribution of axle load spectra in the performance model. The derived SVR models were further used to investigate the impact of overweight truck on pavement life reduction considering characteristics of axle load distributions. The proposed pavement performance model can be further used in determining pavement damage caused by overweight trucks for pavement rehabilitation strategy and fee analysis is permitted.

KEYWORDS:

• Weigh-in motion
• nonlinear regression
• support vector regression
• surface condition index
• axle load spectra

Acknowledgements

The authors acknowledge the following funding agencies for the financial support: North Dakota State University and the Mountain-Plains Consortium (MPC), a University Transportation Center (UTC) funded by the U.S. Department of Transportation. The support provided by the Pavement and Drainage Management and Technology Unit at New Jersey Department of Transportation is also appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Mountain-Plains Consortium, which is a competitively selected University Transportation Center sponsored by the U.S. Department of Transportation.