Development of pavement roughness regression models based on smartphone measurements

Abstract

Purpose The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods. Design/methodology/approach Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations. Findings According to the study results, several regression models were developed with a big variation in the coefficients of determination (R2). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses. Practical implications The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options. Originality/value To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

Nomogram for Predicting Asphalt Pavement Roughness After Preventive Maintenance Based on Long-Term Pavement Performance Data

“…Approximately six follow-up visits were conducted at each site, for a total of 2283 observations. The age of the IRI follow-up measurement was defined as the time distance from the completion of the PM activity to the survey date of that measurement ( 15 , 29 , 30 ). Because of the difference in follow-up times and frequencies of different test sections, age was considered as a continuous variable.…”

“…Researchers have invested extensive efforts into pavement performance predictive models, most of which have focused on the use of traditional predictive models (e.g., empirical equations or linear regressions) (14)(15)(16). However, traditional predictive models suffer from low flexibility and poor predictive performance, which are insufficient for complex pavement performance systems.…”