Generative Artificial Intelligence with Digital Twin in Architecture, Engineering, and Construction
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 47
Special Issue Editors
Interests: machine learning; infrastructure system management; human-machine interaction
Interests: artificial intelligence in construction; advanced infrastructure operation and maintenance
Interests: construction automation; infrastructure management; human systems engineering; machine learning for construction and infrastructure
Interests: machine learning; reinforcement learning; system optimization
Special Issue Information
Dear Colleagues,
The integration of Generative Artificial Intelligence (GAI) with Digital Twin technology is poised to significantly transform the Architecture, Engineering, and Construction (AEC) sector. This Special Issue aims to explore innovative applications of these cutting-edge technologies to address complex challenges in design, construction, and management processes, contributing to the advancement of smart, efficient, and sustainable infrastructure systems.
With the rapid adoption of Digital Twin technology in the AEC industry, enhanced by the capabilities of GAI, we can now simulate, predict, and optimize the life cycle of buildings and infrastructure. The Digital Twin serves as a virtual counterpart to physical systems, providing a dynamic environment for real-time monitoring, predictive analytics, and decision-making. GAI introduces the ability to generate creative design solutions, automate routine tasks, and optimize project outcomes by learning from vast datasets.
This Special Issue invites contributions that demonstrate how GAI and Digital Twin technologies can work together to enhance operational efficiency, resilience, and sustainability in AEC projects. Papers focusing on system-level modeling, optimization techniques, machine learning applications, and human–machine interaction in AEC are particularly welcome. By addressing the integration of these technologies, this Special Issue will highlight advancements in predictive maintenance, real-time decision support systems, and green building initiatives.
Topics of interest include, but are not limited to:
- Digital Twin technologies and applications in AEC;
- Generative AI for design automation and optimization;
- Predictive maintenance and real-time monitoring in construction;
- System-level modeling, multi-domain analysis, and co-simulation;
- Machine learning and AI in infrastructure management;
- Optimization under uncertainty and robust design approaches;
- Human–machine interaction and decision support systems;
- Applications in green construction and sustainable design.
We look forward to receiving your innovative research contributions to this Special Issue.
Technical Program Committee Member:
Mr. Xuhui Lin
Affiliation: The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, UK
Homepage: https://profiles.ucl.ac.uk/96399-xuhui-lin
E-mail: [email protected]
Research Interests: Machine Learning, Urban Computation, Spatial-Temporal Graph Learning
Dr. Pengkun Liu
Dr. Yanyu Wang
Dr. Ruoxin Xiong
Dr. Zemian Ke
Dr. Jinding Xing
Guest Editors
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Keywords
- generative artificial intelligence
- digital twin technology
- architecture, engineering, and construction (AEC)
- machine learning in AEC
- infrastructure system management
- predictive analytics
- real-time data integration
- human–machine interaction
- urban computation
- spatial–temporal graph learning
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