<![CDATA[Pavement damage directly impacts road performance, particularly safety, comfort, and mobility. Therefore, regular pavement condition assessments are essential to support the selection of appropriate maintenance actions. This study focuses on identifying pavement distress types, assessing road conditions using the Pavement Condition Index (PCI) and Bina Marga methods, and mapping the spatial distribution of damage using a Geographic Information System (GIS). This study was conducted on the Gayam–Sidorejo road section in Kediri District, approximately 8.383 km in length, divided into nine segments to facilitate analysis. Field surveys were undertaken to document the type, severity, and size of pavement damage. The obtained data were further analyzed using the PCI method to produce a numerical representation of pavement condition, while the Bina Marga approach was used to determine condition classifications and maintenance priority levels. The results show that the dominant types of damage include alligator cracking, edge cracking, longitudinal cracking, transverse cracking, rutting, patching, potholes, and surface wear. The PCI values range from 36.20 to 87.10, with the lowest value observed in Segment 5 (36.20), indicating a very poor condition and severe pavement deterioration. Meanwhile, the Bina Marga method assigns priority values between 2 and 6, with higher values indicating higher maintenance priority levels, particularly for Segment 5. Moreover, GIS mapping provides a clear representation of pavement damage distribution and helps identify critical segments requiring priority maintenance. The analysis indicates that Segment 5 is the most deteriorated section according to both methods, supporting more effective maintenance decision-making. Contribution to Sustainable Development Goals (SDGs):SDG 9: Industry, Innovation and InfrastructureSDG 11: Sustainable Cities and Communities]]>
