Digital Twin Integration for Resilient Urban Infrastructure Management

Claire Dubois

Digital Twin Integration for Resilient Urban Infrastructure Management

Author(s)	Dr. Claire Dubois
Country	United States
Abstract	Urban infrastructure systems are increasingly exposed to interconnected physical, climatic, technological, and operational disruptions. Flooding can interrupt road accessibility, power loss can affect pumping systems and communications, structural deterioration can reduce transport capacity, and failures in one infrastructure network may propagate into other urban services. Conventional infrastructure-management systems frequently address roads, bridges, drainage, water, energy, and public-safety assets through separate monitoring and maintenance processes. Digital twins provide an alternative architecture by maintaining dynamically updated virtual representations of physical infrastructure and integrating sensor observations, historical records, simulation models, geospatial information, predictive analytics, and operational decisions. This study develops a simulation-based framework for evaluating the potential contribution of digital-twin integration to resilient urban infrastructure management. Because no primary municipal dataset was provided, no simulated result is presented as an empirical observation from a real city. A reproducible synthetic dataset representing 180 hypothetical urban infrastructure-management units was generated across three management conditions: fragmented conventional monitoring, asset-level digital twins, and integrated urban digital-twin management. Six performance dimensions were assessed: disruption-detection capability, service continuity, recovery coordination, data interoperability, predictive-maintenance performance, and decision latency. These variables were combined into an Urban Infrastructure Resilience Management Index (UIRMI). Mean UIRMI values increased from 54.2 under fragmented monitoring to 73.4 under asset-level digital twins and 87.5 under integrated urban digital-twin management. The integrated scenario also achieved the highest simulated disruption-detection score, service-continuity score, recovery coordination, interoperability, and predictive-maintenance performance, while reducing mean decision latency to 2.3 minutes. Inferential testing confirmed strong internal separation among the deliberately parameterized scenarios. The numerical findings are synthetic and do not establish real-world causal effects. The study instead contributes an integrated resilience framework in which digital twins support the full resilience cycle of anticipation, monitoring, disruption response, recovery, and adaptation. Successful implementation requires interoperable data architectures, validated physical and computational models, cybersecurity, governance, transparent decision support, cross-agency coordination, and retention of human authority for critical infrastructure decisions.
Keywords	digital twin; urban infrastructure; infrastructure resilience; smart cities; predictive maintenance; urban management; critical infrastructure; disaster resilience; infrastructure interoperability; decision support
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2021
Published On	2021-01-21

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Digital Twin Integration for Resilient Urban Infrastructure Management

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