Artificial Intelligence Applications in Predictive Road Maintenance Management: Developing a Framework for Nigerian Road Agencies

Artificial intelligence and machine learning techniques offer transformative potential for predictive road maintenance management, enabling proactive rather than reactive infrastructure preservation at significantly reduced lifecycle costs. This study develops and validates an AI-based predictive road maintenance management framework specifically designed for the operational and data environments of Nigerian road management agencies. The research employs design science methodology across three stages: problem diagnosis through interviews with 40 engineers across seven Nigerian road agencies, framework development integrating convolutional neural network-based pavement distress detection, predictive deterioration modelling, and maintenance optimisation algorithms, and empirical validation through prototype deployment on a 150-kilometre test corridor in Niger State. Road condition data are collected using smartphone-based mobile sensing and GPS-enabled crack detection cameras as low-cost sensing alternatives to conventional pavement condition survey equipment. The AI distress detection module achieves a mean average precision of 87 percent in detecting and classifying pavement defects, validated against manual survey results. Predictive deterioration models using Long Short-Term Memory neural networks outperform traditional pavement management system models by 23 percent in forecast accuracy. Optimisation algorithms generate maintenance schedules demonstrating potential lifecycle cost reductions of 28 percent over current practice. The study makes original contributions to AI application in developing country infrastructure management, addressing the low-data, low-resource constraints that have hindered adoption of advanced analytics in Nigerian road agencies. A practical implementation roadmap and change management strategy accompany the technical framework. Keywords: artificial intelligence, predictive maintenance, road management, machine learning, Nigeria.