LAUSR

ABSTRACT Pavement condition monitoring plays a pivotal role in ensuring a comfortable travel experience for road users. Pavement roughness must be accorded the highest importance as it directly influences the safety of the users and also increases vehicle operating costs. Presently, concerned authorities expend a tremendous amount of time, finance, and labour by employing conventional roughness measurement methods. Even though numerous researches are being conducted to estimate roughness using smartphone-sensors, little consideration is given to the sensitivity analysis of results with respect to surface distresses. The current work describes a smartphone-accelerometer-based road roughness estimation method and analyses the effect of surface distresses on the gathered results. The accuracy of the smartphone-accelerometers was validated with an external tri-axis-accelerometer. A model was developed between International Roughness Index (IRI) and Power Spectral Density (PSD) of acceleration values. The IRI-values were estimated before and after eliminating the acceleration windows that represent pavement anomalies. The results were statistically compared with IRI-values captured using Roughometer and segmented IRI maps were generated. The correlation of smartphone-based and Roughometer-based IRI was found to be as high as 0.862. It was observed that, considering pavement distresses in smartphone-based roughness estimation caused an increase of 61.8% in the average IRI-value.