Mathematical insights into the relationship between pavement roughness and vehicle vibration. Issue 6 (12th May 2022) - Liu, Chenglong; Wu, Difei; Li, Yishun; Jiang, Shengchuan; Du, Yuchuan
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.