Conceptual Framework for Applying Digital Twins in Sustainable Construction and Infrastructure Management

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@article{1710573,
      author = {Olamide Folahanmi Bayeroju, Adepeju Nafisat Sanusi, Zamathula Queen Sikhakhane Nwokediegwu},
      title = {Conceptual Framework for Applying Digital Twins in Sustainable Construction and Infrastructure Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {1},
      pages = {323-338},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710573.pdf},
      abstract = {The increasing complexity of construction projects and the growing imperative for sustainable infrastructure management have intensified the need for advanced digital solutions. Digital twin technology?a virtual representation of physical assets that integrates real-time data, simulations, and predictive analytics?offers transformative potential for sustainable construction and infrastructure management. This proposes a conceptual framework for applying digital twins to enhance environmental performance, operational efficiency, and resilience throughout the lifecycle of construction projects and infrastructure systems. The framework is structured around four interdependent layers. The input layer encompasses real-time sensor data, Internet of Things (IoT) networks, and historical performance records, providing a comprehensive foundation for understanding physical and environmental conditions. The decision layer employs advanced analytics, including machine learning, predictive modeling, and scenario simulation, to optimize design, resource allocation, and operational strategies in line with sustainability targets. The implementation layer integrates digital twin outputs into building management systems, asset operations, and infrastructure governance, facilitating evidence-based decision-making and automated compliance with environmental standards. Finally, the feedback layer ensures continuous learning, enabling adaptive management, performance benchmarking, and knowledge transfer across projects and portfolios. Application scenarios for the framework include monitoring energy efficiency in commercial and residential buildings, optimizing water and resource use in urban infrastructure, predicting maintenance needs for bridges and transportation networks, and supporting lifecycle-based environmental impact assessments. By linking real-time data with predictive simulations, digital twins enable proactive interventions, minimize resource waste, and enhance the resilience of infrastructure systems under changing environmental and operational conditions. This framework bridges technological innovation, sustainability objectives, and strategic management, positioning digital twins as a central tool for advancing sustainable construction practices. Future directions emphasize integration with artificial intelligence for automated decision-making, blockchain-enabled data integrity for transparent reporting, and cross-sector collaborations to scale adoption globally. The conceptual framework thus provides a structured pathway for leveraging digital twin technology to achieve efficient, resilient, and environmentally responsible construction and infrastructure systems.},
      keywords = {Digital Twins, Sustainable Construction, Infrastructure Management, Smart Buildings, Real-Time Monitoring, Predictive Maintenance, Lifecycle Assessment, Energy Efficiency, Resource Optimization, Data Analytics, Building Performance, Simulation Modeling, Iot Integration},
      month = {July},
  }