A Survey of Lifecycle Management for Artificial Intelligence Systems in Urban Infrastructure Across Long-Term Operations
TL;DR
The survey concludes that urban infrastructure AI should be managed as a long-term socio-technical asset, with continuous validation, model-health monitoring, controlled adaptation, audit-ready governance, and retirement planning integrated into infrastructure operations.
Abstract
AI technologies are becoming operational components of urban infrastructure systems, including transport networks, structural health monitoring platforms, water utilities, energy systems, and public facilities. These systems support prediction, diagnosis, control, maintenance planning, and asset-management decisions across long service periods. However, much of the existing literature still evaluates infrastructure AI at the model-design or deployment-performance stage, with limited attention to post-deployment validity, operational degradation, update control, and end-of-life management. This survey examines AI applications in urban infrastructure from a lifecycle-management perspective, covering deployment, runtime monitoring, maintenance and adaptation, governance, and retirement. The review applies a PRISMA-guided search and screening protocol to classify retained studies by lifecycle phase, infrastructure domain, deployment evidence, monitoring strategy, adaptation mechanism, governance control, and benchmark support. The cross-domain analysis indicates that deployment-stage accuracy alone is not sufficient for long-term reliability assessment, because sensor wear, environmental variation, asset aging, data drift, maintenance intervention, topology change, and operating-regime shifts can alter model behavior after deployment. The findings further show that current research provides limited support for linking model outputs to maintenance actions, validating model updates under operational constraints, documenting governance evidence, estimating lifecycle cost, defining retirement criteria, and building shared lifecycle benchmarks. The survey concludes that urban infrastructure AI should be managed as a long-term socio-technical asset, with continuous validation, model-health monitoring, controlled adaptation, audit-ready governance, and retirement planning integrated into infrastructure operations.