Optimization of rural infrastructure conservation in the peruvian highlands using the HDM-4 model and the analytic hierarchy process

Mountain roads face persistent maintenance challenges due to steep topography, adverse weather conditions, and heavy truck traffic. This study presents a hybrid decision support framework that combines the Highway Development and Management Model (HDM-4), locally calibrated, with the Analytic Hierarchy Process (AHP) to optimize pavement preservation in high Andean corridors. The method was applied to the Huacarpay–Vicho segment (10 km) in Cusco, Peru, which in 2023 showed accelerated deterioration (International Roughness Index, IRI = 4.04 m/km; surface cracking = 27.9 %). Historical geometric and climatic records (2008–2023) were integrated with detailed field data: classified traffic converted to Equivalent Single Axle Loads (ESAL), IRI, macrotexture, Benkelman beam deflections and visual damage inventories. The subgrade was characterized through modified Proctor and California Bearing Ratio (CBR) tests. The calibration aligned HDM-4 predictions with observed data, achieving high predictive accuracy for roughness (R² = 0.86; mean absolute error = 0.31 m/ km). Subsequently, three maintenance alternatives were ranked using AHP, incorporating technical, economic and road safety criteria. The preventive option, patching, localized milling, crack sealing, and slurry seal, achieved the highest composite score (0.676). A Monte Carlo simulation (N = 1000) incorporating uncertainty in initial roughness (IRI₀), ESAL growth, and costs showed that the preventive strategy was selected in all runs within the analyzed uncertainty range, confirming its robustness. The proposed framework offers a transparent and replicable tool that extends pavement service life, reduces lifecycle costs, and improves road safety under mountainous conditions, providing engineers and policymakers with an evidence-based roadmap for pavement management in similar environments.