Human-in-the-Loop Digital Twins for Urban Infrastructure Decision-Making: Integrating Engineering Judgment with Structural Health Monitoring in Tehran

Digital twin technology has increasingly been adopted to support monitoring, assessment, and prediction of urban infrastructure performance. While data-driven models and automated analytics offer significant advantages, fully autonomous decision-making remains problematic in safety-critical infrastructure systems due to uncertainty, data limitations, and the need for contextual interpretation. In practice, engineering judgment continues to play a central role in infrastructure management decisions. This study proposes a human-in-the-loop digital twin framework for urban infrastructure decision-making, in which structural health monitoring data and physics-informed digital models are combined with expert judgment in a structured and transparent manner. Rather than replacing human decision-makers, the digital twin functions as a decision-support environment that presents interpretable performance indicators, predicted trends, and uncertainty information to engineers and managers. The proposed framework is demonstrated through a case study of representative urban infrastructure assets in Tehran. The results show that incorporating human feedback into digital twin updating and decision processes improves robustness, reduces misinterpretation of automated outputs, and enhances trust in data-driven recommendations. The findings highlight the importance of human-centered digital twin design for effective and accountable urban infrastructure management.

کلیدواژه ها:

Human-in-the-loop ، Digital twin ، Structural health monitoring ، Engineering judgment ، Decision support systems ، Urban infrastructure ، Case study of Tehran