Measurement and Analysis of International Roughness Index using IoT-based System

The study proposes an IoT-based system for measuring road roughness using a combination of accelerometer and GPS sensors. The system collects data on road surface roughness and location, which is processed by a microcontroller and transmitted to a cloud-based MQTT broker. The data is analyzed and visualized via Node-RED and MATLAB, allowing for the calculation of IRI values for different road segments and the creation of IRI maps. The average IRI values for the eight segmented data, along with the corresponding pavement conditions, were analyzed in this study. The results indicated that Segmented Data 2 had the lowest mean IRI value of 1.24 m/km, which indicated a good pavement condition. Segmented Data 1, 3, 4, 6, 7, and 8 had IRI values ranging from 2.04 to 2.79 m/km, which indicated a fair pavement condition. On the other hand, Segmented Data 5 had the highest IRI value of 6.50 m/km, which indicated a bad pavement condition. The results demonstrate the IoT-based system’s capability of assessing the road roughness of different pavement conditions. The IRI mapping is based on real-time data collected from the IoT-based system, which allows for efficient measurement of road roughness. The information obtained can be useful for road management authorities to plan and prioritize road maintenance and repair works, improving road safety.