AI-Driven Systems for Renovation and Maintenance of Ageing Infrastructure in Sub-Saharan Africa

This study explores how artificial intelligence (AI) can support the renovation and maintenance of ageing infrastructure in Sub-Saharan Africa. Many roads, bridges, and buildings in the region are old and in poor condition, but fixing them is often delayed, expensive, and poorly planned. The objective of the research is to understand how AI technologies can help solve these problems and improve safety, reduce costs, and make better use of resources. The study combined scientometric analysis and narrative methods of published literature from Scopus and Web of Science in books, articles, and conferences. Scientometric tools like VOSviewer and Biblioshiny were used to track research trends and keywords. The findings showed that tools such as Building Information Modelling (BIM), digital twins, IoT, and machine learning are widely used in developed countries for infrastructure maintenance. However, there is a lack of similar studies and applications in Sub-Saharan Africa. The study also found barriers such as limited awareness, poor internet access, and low investment in digital tools. Despite these challenges, the research shows that AI has strong potential to make infrastructure safer and more cost-effective. The findings can help guide policymakers, engineers, and planners in making better decisions and planning future improvements. This research contributes to sustainable development by offering smarter ways to manage old infrastructure and improve living conditions in African cities.