Digital Workflows and BIM for Prefabricated and Sustainable Construction

A Special Issue of Buildings (ISSN 2075-5309) belonging to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 488

Editor


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Guest Editor
Department of Civil Engineering, Warsaw University of Technology, 16 Armii Ludowej Av., 00-637 Warsaw, Poland
Interests: BIM; prefabrication; artificial intelligence; automation in construction; innovative materials; rehabilitation of buildings/structures; digital twins; facilities management; polymers in/on concrete; machine learning
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Special Issue Information

Dear Colleagues,

I am pleased to invite you to contribute to this Special Issue, which focuses on Digitalization and Building Information Modeling (BIM) in contemporary design and construction.

On construction sites, digital workflows supported by BIM, automation tools, and real-time data collection are transforming planning, coordination, and on-site monitoring. In the operational phase, BIM-based digital twins and facilities management systems enable continuous observation of building performance, predictive maintenance, and informed decision-making over the entire service life of an asset. In this context, machine learning and artificial intelligence open new possibilities for pattern recognition, prediction, optimization, and intelligent assistance in design, scheduling, and inspection processes.

At the same time, the construction sector is under growing pressure to become greener, more efficient, and more transparent. Digital methods play a central role in reducing waste, optimizing material use, and supporting ESG and sustainability objectives at the project, portfolio, and city scale. By integrating BIM with life-cycle thinking, data-driven analytics, and advanced visualization, we can better understand environmental impacts and explore strategies for low-carbon, resource-aware construction.

This Special Issue aims to capture these developments by bringing together contributions on BIM-based prefabrication and automation, digital twins and data-driven facilities management, AI- and ML-supported workflows, and broader aspects of digital transformation in construction. We welcome original research articles, innovative case studies, and comprehensive review papers that illustrate both current achievements and future directions in this rapidly evolving field.

I look forward to receiving your valuable contributions and to advancing this important area of research together.

Best wishes,

Dr. Kostiantyn Protchenko
Guest Editor

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

  • building information modeling
  • precast industry
  • artificial intelligence
  • cost/quality/safety control
  • digital twins
  • facility management
  • automated techologies
  • machine learning

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Published Papers (1 paper)

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Review

37 pages, 8272 KB  
Review
Artificial Intelligence for Structural Condition Assessment and Rehabilitation: Recent Advances and Future Directions
by Shima Zare and Mohammad Najafi
Buildings 2026, 16(17), 3401; https://doi.org/10.3390/buildings16173401 - 26 Aug 2026
Viewed by 317
Abstract
The growing need to ensure the safety, resilience, and sustainability of existing building structures has accelerated the adoption of artificial intelligence (AI) for structural condition assessment and rehabilitation. This critical narrative review synthesizes 82 retained sources, including 33 application-oriented sources, through a transparent, [...] Read more.
The growing need to ensure the safety, resilience, and sustainability of existing building structures has accelerated the adoption of artificial intelligence (AI) for structural condition assessment and rehabilitation. This critical narrative review synthesizes 82 retained sources, including 33 application-oriented sources, through a transparent, structured literature search and study-selection process; it is not a formal systematic review or meta-analysis. To organize this fragmented evidence base, the review introduces the Data-to-Decision (D2D) Continuum, a unifying conceptual framework that traces eight engineering stages from data acquisition through damage detection, localization, quantification, condition and performance assessment, prognosis, reliability and risk assessment, to rehabilitation decision support. Classical machine learning, deep and temporal models, physics-guided and probabilistic approaches, and emerging foundation models are examined according to the engineering output required at each stage. The strongest evidence concerns bounded defect detection and localization, whereas uncertainty-aware prognosis, risk-informed rehabilitation selection, multi-site validation, and governed deployment remain markedly less mature. By integrating existing monitoring, digital-twin, life-cycle risk, and maintenance-decision concepts into an interface-centered evidence chain, the D2D framework clarifies what must be validated before an AI output can responsibly influence an intervention. Full article
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