<![CDATA[Road condition assessment is an important indicator within the infrastructure maintenance management system. The International Roughness Index (IRI) is a standard service instrument used in various countries to evaluate road surface roughness. However, assessing road conditions using IRI faces several challenges, including the need for expensive equipment, long operational time and cost, limited coverage, and the fact that it only represents surface roughness, thereby requiring additional surveys to identify pavement distress types directly. The objective of this study is to develop an IRI prediction model based on the Surface Distress Index (SDI) parameters to optimize the efficiency of road condition assessment by using Artificial Neural Networks (ANN) to enhance road maintenance planning. This model was trained on five years of road-damage data across 42 urban road segments. The coefficient of determination from the study findings indicates excellent prediction accuracy, with an R2 value of 0.93, a Mean Absolute Error (MAE) of 0.73, and a Root Mean Square Error (RMSE) of 1.07. This model can serve as a more dynamic strategy for urban road maintenance management in developing countries facing resource limitations. The proposed framework offers an innovative and adaptive solution to issues in road maintenance management and provides support for more sustainable urban infrastructure management.]]>
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