AI-Driven Optimization of Energy-Efficient Rural Road Infrastructure and Water Conservation Systems in Resource- Constrained Regions
Keywords:
Artificial intelligence; Rural road infrastructure; Energy efficiency; Water conservation; Sustainable development; Resource-constrained regionsAbstract
Rural road infrastructure and water conservation systems in resource-constrained regions are often planned and managed separately, leading to energy inefficiencies, accelerated pavement deterioration, unmanaged surface runoff, and missed opportunities for sustainable water reuse. Recent advances in artificial intelligence offer new possibilities for integrating transport and water systems through data-driven optimization.
This study aims to develop and assess an AI-driven optimization framework that jointly enhances the energy efficiency of rural road infrastructure and the performance of road- based water conservation systems in resource-constrained settings.
The research adopts a model-based analytical approach that integrates machine learning for pavement condition and runoff prediction with multi-objective optimization algorithms. Life-cycle energy indicators, hydrological performance metrics, and cost considerations are incorporated into a unified framework. Scenario-based simulations are used to compare conventional planning approaches with AI-optimized interventions under varying infrastructure and environmental conditions.
The results indicate that AI-driven optimization can substantially improve pavement performance and reduce vehicle energy consumption while simultaneously increasing runoff capture and water retention efficiency. Compared with baseline scenarios, the integrated approach demonstrates clear trade-offs and synergies between energy efficiency, water conservation, and lifecycle costs, highlighting the advantages of coordinated infrastructure planning.
The proposed framework provides a practical decision-support tool for policymakers, engineers, and development agencies seeking cost-effective and sustainable infrastructure solutions in low-resource rural regions. By linking transport energy efficiency with water conservation objectives, the study supports climate-resilient infrastructure planning aligned with sustainable development goals.
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