Statistical Evaluation of Pavement Condition in North Central Nigeria through Geotechnical-Climatic Interactions

Flexible pavements in tropical regions of sub-Saharan Africa often suffer premature deterioration due to the combined effects of variable lateritic subgrade properties, climatic variability, and repeated traffic loading. This study assessed pavement performance along major federal highways in North-Central Nigeria (A1, A2, and A124 corridors, including the Ilorin–Mokwa–Abuja–Lokoja route) by integrating geotechnical data, environmental parameters, traffic information, and Pavement Condition Index (PCI) surveys across 138 sample units. Laboratory tests determined California Bearing Ratio (CBR), Maximum Dry Density (MDD), and Optimum Moisture Content (OMC), while rainfall and temperature data captured seasonal wet–dry cycles. Results revealed wide variability in lateritic soils, with CBR ranging from 3% to 93%, MDD from 1.70 to 2.00 Mg/m³, OMC from 10.3% to 12.5%, and PCI values from 5 to 99, reflecting extreme heterogeneity in pavement performance. Correlation analysis showed strong positive relationships between PCI and MDD (r = 0.90), and negative correlations with OMC (r = -0.88), rainfall (r = -0.85), and temperature (r = -0.75). Regression models confirmed MDD as the most significant single predictor of PCI, followed by temperature and CBR, whereas rainfall alone had negligible explanatory power. Multiple linear regression provided a more robust evaluation, with Multiple R = 0.920, R² = 0.847, Adjusted R² = 0.836, F = 185.78 (p = 2.06 × 10⁻⁵³), demonstrating that approximately 84.7% of PCI variation is explained collectively by MDD, CBR, rainfall, and temperature. The model’s standard error of 26.15 indicates moderate dispersion, supporting its reliability. The findings confirm that pavement deterioration in tropical wet–dry climates is multifactorial, dominated by geotechnical variability and climatic influences. To enhance performance, highway management should prioritize adequate subgrade compaction, integrate effective drainage systems, and adopt climate-responsive, multivariate predictive models rather than single-variable assessments, ensuring more accurate evaluation and sustainable pavement management.