Comparative Life Cycle Cost Analysis of Selected Maintenance Strategies for Low-Volume Roads: A Case Study of Ubungo District, Tanzania

Low-volume roads in developing countries face significant maintenance challenges due to resource constraints and inadequate strategic planning frameworks. This study conducted a comparative life cycle cost analysis of maintenance strategies for low-volume roads in Ubungo District, Tanzania, to optimise resource allocation and improve infrastructure sustainability. Using a mixed-methods research design, data were collected through structured questionnaires from 67 qualified professionals across various stakeholder categories. The study employed the Relative Importance Index (RII) analysis to identify critical life cycle cost components and developed a multiple regression model to predict strategic maintenance costs. Results revealed seven high-importance cost factors with RII values ≥ 0.88: Periodic Maintenance Costs (RII = 0.892), Emergency Repair Costs (RII = 0.886), User Delay and Disruption Costs (RII = 0.883), Initial Construction Costs (RII = 0.880), Environmental Impact Costs (RII = 0.874), Spot Maintenance Costs (RII = 0.871), and Routine Maintenance Costs (RII = 0.866). A robust predictive model was developed with exceptional performance (R² = 0.98, F = 387.45, p < 0.001). The model equation: Strategic Maintenance Cost = 0.04 + 0.02(PMC) + 0.04(ERC) + 0.02(UDC) + 0.03(ICC) + 0.02(EIC) + 0.02(SMC) + 0.01(RMC) demonstrated statistical significance across all variables. Validation testing across five road scenarios showed model confidence ranging from 20% (very severe conditions) to 84% (very good conditions), confirming the model's discriminatory capability. The findings demonstrate that proactive maintenance strategies significantly enhance cost predictability and infrastructure performance compared to reactive approaches. This research provides evidence-based recommendations for Tanzania Rural and Urban Roads Agency (TARURA) and similar organisations to optimise maintenance planning, improve resource allocation efficiency, and achieve sustainable road infrastructure management in low-volume road networks.