Assessment of road surface condition using iPhone LiDAR function

Roads are vital national assets that require regular maintenance to remain functional. Over time, pavements deteriorate due to traffic loads and environmental exposure, resulting in cracks, potholes, and surface wear. While manual inspection can identify these issues, it is often time-consuming, subjective, and inconsistent. Automated detection methods offer a more efficient and standardized alternative, but they vary significantly in accuracy, cost, and complexity. The method used in this study presents a cost-effective approach for detecting road distress by combining visual observation with an iPhone-mounted LiDAR sensor – focus on 300meter industrial road with variation types of road distress (potholes, alligator cracking, longitudinal cracking, patch deterioration, bleeding, ravelling and edge cracking) . The mobile LiDAR system captures high-resolution 3D surface data, enhancing the reliability of visual assessments while remaining portable and affordable. The objectives of the research is to compare the result of road distress from processed iPhone LiDAR data and conventional method (visual observation). Second, to evaluate the dominant road distress types such as at study area based on the frequency of occurrence in the study area. The findings shows that the comparison for processed iPhone LiDAR data and conventional method (visual observation) is dominated by high and severe level of road distress. The second finding shows that in terms of frequency of occurrence, potholes (8) and bleeding (8) is the most prominent road distress present in the study area while patch deterioration (1) is the least prominent road distress types. The results were used to produce a road distress plan, supporting data-driven maintenance decisions. The findings demonstrates that iPhone-integrated LiDAR can be a useful tool for quick, cost-effective road condition monitoring. The device successfully captured key surface distress features such as cracks, potholes, and rutting when used under controlled conditions. The point cloud data enabled analysis of pavement conditions and supported the classification of distress types based on frequency, contributing to better maintenance planning. Overall, this research highlights the potential of smartphone-based LiDAR as a scalable and accessible tool for road condition monitoring.