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Evaluation of Road Surface Conditions using The International Roughness Index (IRI) Based on Mobile Mapping System (MMS) with Lidar AU20 Sensor

Rifa Putri Nur Azizah
Rifaldi Muhammad Zaki
Anisa Nabila Rizki Ramdhani, S.T., M.T.
Berliana Dwi Praptaningtyas, S.T.
Sofyan Chairul Anwar, S.T, MT.
Abstract
Evaluating road surface conditions is essential for infrastructure maintenance planning and asset management. Conventional methods are often time-consuming and inefficient, highlighting the need for more automated and precise approaches. This study aims to assess road roughness using a Mobile Mapping System (MMS) equipped with a LiDAR AU20 sensor and IMU integration. A 291.75-meter road segment in South Tangerang, Indonesia, was surveyed to generate high-resolution point cloud data. The data were processed using CoPre, CoPro, and ProVAL software to extract elevation profiles and calculate the International Roughness Index (IRI) at 10-meter intervals. The results showed an average IRI value of 3.052 m/km, classified as "Good" based on international standards, although variation was observed across segments. These findings indicate that MMS with integrated LiDAR and IMU offers an efficient and accurate solution for road evaluation. Further recommendations include field validation and enhancement of data processing algorithms to improve measurement accuracy.
Keywords: Mobile Mapping System (MMS), LiDAR, International Roughness Index (IRI), Road Surface Evaluation, Spatial Data Acquisition.
References
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