Nepal’s National Highway Pavement Optimal Management Through Life Cycle Cost Minimization

Abstract

Nepal’s National Highway (NH) network is vital to meet the country's transportation demands and socio-economic development. However, a big fraction of NH network faces rapid deterioration due to increasing traffic, harsh climatic conditions and inefficient maintenance planning. This study presents a data driven framework for road management planning, utilizing the Markov hazard model for pavement deterioration for two major pavement types – Surface Dressing (SD) and Asphalt Concrete (AC) across two major climatic conditions - Tropical Savannah (Aw) and Temperate Climate with Dry winter (Cw). The framework incorporates Surface Distress Index (SDI), traffic volumes, and life cycle cost analysis (LCCA) to evaluate the long-term effectiveness of various maintenance and upgrading strategies using the Markov model. The result shows that Combined Maintenance (CM) which involves integration of routine and recurrent maintenance activities significantly delay pavement deterioration process, reducing periodic maintenance costs and improving road network performance. Upgrading SD pavement with higher deterioration rates to AC proves highly effective for high traffic roads, improving durability and overall network condition. The study evaluates several maintenance and upgrading strategies, highlighting the balance between LCC and the good to fair road percentage, enabling road agencies to set performance targets within budget constraints. The findings provide valuable information for policymakers and road agencies, emphasizing the importance of proactive, data-driven decision-making in road maintenance planning. Future research could explore indirect benefits, such as vehicle operating cost savings and reduced travel times, to further enhance the decision-making framework.