Emerging Technologies for Digital Transformation of Resilient Built Assets
A Special Issue of Buildings (ISSN 2075-5309) belonging to the section "Construction Management, and Computers & Digitization".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 1191
Editors
Interests: digital twin; asset and facility management; automation and digitisation; data-driven methods; artificial intelligence and machine learning; Internet of Things (IoT); smart built environment; decision support systems
Interests: building and infrastructure facilities and asset management; knowledge graph; machine learning (ML); digital transformation for manufacturing operations and management; building information modelling/management (BIM)/digital twins (DTs)
Special Issue Information
Dear Colleagues,
Built assets, including buildings and infrastructure, are increasingly exposed to complex and interrelated challenges such as climate change, extreme weather events, ageing facilities, operational disruptions, and evolving user demands. Enhancing the resilience of these assets requires not only robust physical design and engineering solutions but also a fundamental digital transformation in how assets are monitored, managed, and operated throughout their lifecycle. Despite growing research on digital twins, automation, and artificial intelligence in the built environment, significant gaps remain in their practical implementation for resilient asset and facility management. The potential of emerging data-driven and knowledge-based technologies to systematically support resilience, such as risk anticipation, vulnerability assessment, operational adaptability, and recovery planning, has not been fully realised.
This Special Issue aims to address these challenges by bringing together cutting-edge research on emerging technologies for the digital transformation of resilient built assets. It seeks contributions that adopt knowledge-driven approaches to enhance asset intelligence and enable resilience-oriented decision-making across the entire asset lifecycle. It welcomes contributions that explore, but are not limited to, image and point-cloud processing for digital twin construction, natural language processing for knowledge integration and fusion, real-time monitoring for asset risk control, and robotic systems enabling advanced built asset operations.
Dr. Qiuchen Lu
Dr. Ya Wen
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- resilient built assets
- digital transformation
- risk and vulnerability assessment asset and facility management
- digital twins
- knowledge-based systems
- artificial intelligence
- real-time monitoring
- decision support systems
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