Digital Twins, Predictive Infrastructure and Resilience: A Theoretical Framework for Asset Management and Climate Adaptation in Infrastructure-Deficit Cities
DOI:
https://doi.org/10.68854/jhvrnh59Keywords:
digital twins, infrastructure, asset management, predictive maintenance, urban resilience, climate adaptation, flooding, infrastructure deficit, artificial intelligence, AfricaAbstract
Digital twins, continuously updated computational models of physical infrastructure, allow cities to predict failures, simulate interventions and plan for climate stress, and they are being promoted to African cities facing both an infrastructure deficit and intensifying floods, heat and storms. This theoretical paper asks what predictive infrastructure management offers a city whose problem is not the degradation of assets it has but the absence of assets it needs. Drawing on infrastructure-studies and asset-management theory, on the urban-resilience and climate-adaptation literatures, and on recent research on digital twins and artificial intelligence in infrastructure, it distinguishes three decision problems, maintaining existing assets, prioritising new investment, and responding to acute events, and shows that digital twins are strongest on the first, which is the least pressing in infrastructure-deficit cities, and weakest on the second, which is the most. The paper develops a framework for predictive infrastructure under deficit conditions in which the value of modelling lies in investment prioritisation and in drainage, flood and heat planning rather than in asset optimisation; derives seven propositions about the data, institutional and financial conditions under which such systems improve resilience; and examines the risk that twin-driven investment favours the already served. Flood-exposed Lagos anchors the analysis. The paper closes with implications for city governments, infrastructure financiers, vendors and the research agenda of African urban infrastructure studies. It concludes that the decision a deficit city most needs to improve is not how to maintain the assets it has but what to build, for whom, before the next flood, and that a predictive model reoriented toward that decision, less a replica of the city than an honest estimate of its hazards and needs, would be the first such tool built for the cities that need it most.
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