Estimation of water film depth for rutting pavement using IMU and 3D laser imaging data

ABSTRACT

Rutting is a kind of pavement structural damage caused by repeated traffic loads, which is served as ‘reservoir’ in rainy days, and ponding water would bring in potential hydroplaning issues. However, the widely used water film depth (WFD) prediction models are empirical models for the pavements without ruts. This study develops an analytic model for WFD estimation by simulating the dynamics of the sheet flow on rutting pavements. Precipitation, pavement texture, road alignment and pavement depression are considered as the intervening factors on WFD. Herein, precipitation is automatically and continuously measured by RLMI, and pavement properties are automatically and continuously measured by the 3D line scanning laser system. Based on the proposed algorithm the impacts of pavement swelling and centre deformation on rutting measurement are eliminated. The measured dimensions of rutting and estimated depth of sheet flow on the pavement surface are contributed for WFD calculation. A case study for WFD estimation on 1.4 km asphalt pavement section is provided. Finally, a validation test referred to the ground truth obtained by field manual measurment shows the proposed methods are accurate in WFD estimation for rutting pavement. Based on the method proposed in this study the hazardous locations with strict water logging can be identified, so engineers can take remedial measures to reduce traffic accidents.

KEYWORDS:

• Water film depth
• rutting pavement
• inertial measurement unit (IMU)
• 3D laser imaging data
• automatic measurement
• analytic model

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Wenting Luo http://orcid.org/0000-0001-5585-8467

Additional information

Funding

This work was supported by the National Key R&D Program of China: [Grant Number 2018YFB1201601]; Outstanding Young Scientific Research Talent Program of Fujian Agricultural and Forestry University: [Grant Number XJQ2018007]; Fujian Natural Science Foundations: [Grant Number 2017J01682].