Beyond initial costs: a holistic optimization model for sustainable pavement maintenance in Iraq

This study develops a framework for optimizing pavement maintenance by integrating life cycle cost analysis with MATLAB-based modeling and a weighted-sum genetic algorithm (GA). Unlike prior approaches that focus on initial costs, the framework simultaneously optimizes four objectives: international roughness index (IRI), energy consumption, user vehicle operating costs and agency costs (lifecycle improvement costs). The model incorporates assumptions, including a pavement deterioration rate (β) of 0.05 per year, a 12% discount rate and energy consumption primarily from diesel-powered maintenance operations. Using real-world data from Baghdad’s road network, a 10-year simulation demonstrates that the optimized strategy reduces the network mean IRI from 3.88 to 2.49 m/km, decreases agency costs by 20% and lowers lifecycle energy consumption by 38.7%. The GA identifies trade-offs among the four objectives, generating maintenance strategies for each pavement section over the decade. These results highlight the potential of multi-objective optimization to enhance cost efficiency, environmental performance and ride quality in pavement management. Limitations include reliance on diesel energy and a restricted set of maintenance strategies, motivating future research on renewable energy integration and broader alternatives. The framework provides policymakers and practitioners with quantitative guidance for sustainable and resilient road asset management under evolving infrastructure demands.