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.
Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables.
Abdulaziz Almaleh, Abdullah M. Alqahtani· Mathematics· 0 citations