A Systematic Literature Network Analysis: Research Mapping of International Roughness Index

Abstract

The International Roughness Index (IRI) is a widely used measure of pavement roughness that has important implications for vehicle safety, ride quality, and road maintenance. Over the years, many research studies have been conducted on IRI, but the literature is dispersed and lacks an overall research mapping. To address this issue, a Systematic Literature Network Analysis (SLNA) method to review the topic of IRI from scientific publications from 2000-2021 to obtain state-of-the-art mapping, trending topics, and future work projection. The results show a significant increase in scientific document publications. Network visualization contains 189 keywords divided into six clusters. The biggest cluster is focuses on measuring road surface conditions to obtain the IRI value as part of monitoring road surface conditions using a mechanical method and vibration response. The keywords featured on the word cloud are pavements, surface roughness, road and street, pavement performance, asphalt pavement, and concrete pavement. Top trend topics are predictive analytics, decision trees, machine learning, and roughness prediction. The keywords machine learning and learning algorithms are up-to-date topics and closely related to forecasting and the international roughness index. The IRI prediction model is still feasible for further research by using a machine learning prediction model.