Mathematical insights into the relationship between pavement roughness and vehicle vibration

ABSTRACT

Pavement roughness is an essential indicator used in road maintenance and asset management. As a well-recognised measurement technique, the responsive devices allow us to collect roughness information by measuring the in-car vibration. However, most of them predict the International Roughness Index (IRI) based on the statistical features of the vibration. Few studies shed lights on the mechanism involved. This paper derives a mathematical relationship between pavement roughness and in-car vibration considering the joint impact of roughness-induced and engine-induced vibration. The quarter-car model is characterised by a linear time-invariant system. The Laplace transform and power spectral density (PSD) analysis are applied to specify the vibration transfer through multi-layer suspension and pavement roughness. Through more than 200 km field tests, the performance of the proposed model was tested by comparing it with two mainstream methods under both low and high IRI scenarios. The results show that the proposed model outperformed the prevalent algorithm by the highest fitting goodness and the lowest average evaluation error. The sensitivity of the sampling rate and driving speed is further discussed and quantified. This paper provides a better understanding of the vehicle's responses to pavement roughness and develops an accurate model for rapid measurement.

KEYWORDS:

• Pavement roughness
• vehicle vibration
• rapid measurement
• mathematical model
• stochastic process
• power spectral density

Acknowledgements

This study was jointly supported by the Scientific Research Project of Shanghai Science and Technology Commission (19DZ1209100), National Natural Science Foundation of China (NSFC51978519), Chinese Postdoctoral Science Foundation (2020M671221). The authors are responsible for all views and opinions expressed in this paper. I would like to acknowledge the support provided by Xiaoming Zhang and Jinsong Yue for collecting the data. I also would like to express my very great appreciation to Tienan Li, who helped to proofread the paper.

Disclosure statement

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

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number NSFC51978519]; Science Foundation of Ministry of Education of China & China Mobile Communication Corp: [Grant Number 2018202004]; Chinese Postdoctoral Science Foundation: [Grant Number 2020M671221]; Scientific Research Project of Shanghai Science and Technology Commission: [Grant Number 19DZ1209100].