Quantitative Assessment of Key Performance Indicators Influencing the Pavement Condition Index for Cold Mix Asphalt Pavement in Low Volume Roads

This study quantitatively examines the factors affecting the performance of low-volume cold mix asphalt pavements using a data-driven analytical framework. Documented field records and historical pavement performance data from costal region were used to evaluate seven Key Performance Indicators (KPIs): Rutting Depth (RD), International Roughness Index (IRI), Crack Density (CD), Moisture in Subgrade (MS), Traffic Load (TL), Rainfall (RF), and Binder Content (BC). Descriptive statistics, Stability Index (SI), correlation analysis, and a combined weighting method were applied to assess deterioration patterns and the relative influence of each KPI on the Pavement Condition Index (PCI). Correlation analysis showed strong negative relationships between PCI and RD (-0.99), IRI (-0.995), CD (-0.985), MS (-0.997), TL (-0.995), and RF (-0.992), indicating declining pavement condition with increasing distress, loading, and environmental factors. BC demonstrated a strong positive correlation (0.991), highlighting its significance in maintaining pavement durability. Stability Index results identified BC (SI = 16.953) as the most stable and reliable parameter, while CD (SI = 1.017) and RD (SI = 1.339) were the most variable. The combined weighting analysis ranked BC (23%), RF (19%), and MS (18%) as the most influential KPIs, followed by TL (17%) and IRI (14%), with RD (7%) and CD (2%) having lower impacts. Min–max normalization ensured comparability across variables with different units and scales, and radar plots were used to visualise overall KPI contributions. The study provides a comprehensive evidence base for predictive modeling of PCI and recommends improved binder design, moisture control, traffic regulation, and timely maintenance to enhance pavement service life and performance