Assessment of Relationship Between Road Roid-Based International Roughness Index and Conventional Surface Distress Index: Two Pavement Evaluation Measures Currently Employed in Nepal

Abstract

Effective pavement condition evaluation is critical for ensuring the safety, usability, and longevity of road networks, especially in developing countries like Nepal where financial and technical constraints limit access to conventional high-end measurement technologies. Traditional methods such as the International Roughness Index (IRI) and Surface Distress Index (SDI) provide valuable insights but are often costly and resource intensive. This study investigates the potential of the RoadRoid smartphone application as a low-cost alternative for measuring IRI and examines its relationship with the conventionally obtained SDI. Calibration of RoadRoid measurements was conducted against benchmark IRI data collected using a Romdas Z-250 Laser Profiler over a 200-meter reference section. A chi-square goodness-of-fit test validated that there was no statistically significant difference between the RoadRoid-derived measurements and those from the reference profiler at a 95% confidence level. Regression analyses were performed to assess the relationship between RoadRoid-based IRI and SDI, evaluating linear, logarithmic, and third-order polynomial models. The third-order polynomial model exhibited the best fit with a coefficient of determination (R²) of 0.64, demonstrating a strong correlation between the two indicators. These results confirm that the RoadRoid application, when properly calibrated, can reliably measure pavement roughness and correlate well with surface distress conditions. The findings highlight the potential of smartphone-based technologies to offer affordable, scalable solutions for pavement condition assessment, supporting more efficient maintenance planning and infrastructure management in resource-constrained environments.