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Intelligent Building Information Modeling (BIM): Advancements in Collaborative Design and...
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Intelligent Building Information Modeling (BIM): Advancements in Collaborative Design and Construction

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

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1772

Editors

Dr. Si Van-Tien Tran

E-Mail
Guest Editor
Department of Architectural Engineering, Catholic Kwandong University, Gangneung 25601, Republic of Korea
Interests: construction management; construction informatics; BIM; digital twin; construction safety
Dr. José António Silva Carvalho Campos Matos

E-Mail Website
Guest Editor
Department of Civil Engineering, ISISE, ARISE, University of Minho, Campus Azurem, 4800-058 Guimaraes, Portugal
Interests: structural assessment; monitoring and inspection; life cycle costs; risk and reliability; resilience and sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Ngoc-Son Dang

E-Mail Website
Guest Editor
Department of Civil Engineering, ISISE, ARISE, University of Minho, Campus Azurem, 4800-058 Guimaraes, Portugal
Interests: BIM; BMS; SHM; digital twin; predictive analytics; AI; ML; eXtended reality and holographic computing; resilience and sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Tomo Cerovšek

E-Mail Website
Guest Editor
Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova cesta 2, 1000 Ljubljana, Slovenia
Interests: BIM; building lifecycle; information management; process improvement; technology transfer; systems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Ung-Kyun Lee

E-Mail Website
Guest Editor
Department of Architectural Engineering, Catholic Kwandong University, Gangneung 25601, Republic of Korea
Interests: construction management; building information modeling; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The construction industry is entering a new era of intelligent collaboration, where Building Information Modelling (BIM) is enhanced by machine learning (ML), artificial intelligence (AI), and digital twin technologies. These advances transform BIM from a static coordination tool into a dynamic, data-driven environment that supports real-time decision-making, automation, predictive management, and lifecycle optimisation. AI-powered BIM facilitates seamless communication, enhances design quality, improves safety and productivity, and supports sustainable and resilient outcomes across the built asset lifecycle.

This Special Issue, therefore, encourages contributions that demonstrate measurable advances in intelligence, automation, data governance, and lifecycle integration. We welcome contributions demonstrating methodological or applied novelties, such as AI-enabled workflows, generative reasoning linked to BIM semantics, predictive performance analytics, model-based automation, and digital twin frameworks with deployment validation. We are seeking original research, reviews, validation studies, and practice-oriented case studies from academia, industry, and policymakers.

Potential topics include, but are not limited to:

  • Intelligent BIM Authoring, Automation and Model Reasoning
  • AI/ML-enhanced modelling and planning;
  • Generative and parametric design linked to BIM semantics;
  • Automated code-compliance and information requirement compliance checking.
  • Digital Twins, Monitoring and Predictive Analytics
  • Lifecycle integration and asset performance modelling;
  • Sensor fusion, SHM and hybrid deep learning;
  • Predictive maintenance of buildings and infrastructure.
  • Interoperability, Standards and Data Governance
  • IFC, IDS, ISO 19650;
  • Digital thread architecture and data quality;
  • Auditability, traceability and trust frameworks.
  • Safety, Risk and Resilience
  • Construction safety intelligence;
  • Reliability of critical infrastructure;
  • Resilience assessment of existing structures.
  • Human–AI Collaboration and XR-Enabled Practices
  • Multi-agent cooperation and co-design;
  • XR for training, coordination and operations.

We welcome original research, comprehensive reviews, and practice-oriented case studies from academia, industry, and policymakers that evidence the transformative potential of intelligent BIM in advancing collaborative, safe, and sustainable construction.

Dr. Si Van-Tien Tran
Dr. José Campos Matos
Dr. Ngoc-Son Dang
Dr. Tomo Cerovšek
Dr. Ung-Kyun Lee
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

  • intelligent BIM
  • digital twins
  • AI/ML in construction
  • interoperability and ISO 19650
  • information requirements and automation
  • predictive analytics and SHM
  • generative and rule-based design
  • XR and human–AI collaboration
  • lifecycle asset management
  • resilient and sustainable built environment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

32 pages, 18872 KB  
Article
A Lightweight CNN Framework for UAV-Based Missing-Bolt Patch Classification in Structural Health Monitoring
by Omoniyi Tope Moses, Abba-Gana Mohammed, Umar Sa’eed Yusuf, Nguyen Thi Thu Nga, Omoebamije Oluwaseun, Aliyu Abubakar, Jose C. Matos, Duna Samson and Son N. Dang
Buildings 2026, 16(16), 3261; https://doi.org/10.3390/buildings16163261 - 17 Aug 2026
Viewed by 444
Abstract
Missing bolts compromise the structural integrity of bolted connections in steel bridges and industrial infrastructure. Manual visual inspection remains labour-intensive, subjective, and hazardous in hard-to-reach locations. This study presents a comparative benchmarking framework for unmanned aerial vehicles (UAVs) missing-bolt patch classification using convolutional [...] Read more.
Missing bolts compromise the structural integrity of bolted connections in steel bridges and industrial infrastructure. Manual visual inspection remains labour-intensive, subjective, and hazardous in hard-to-reach locations. This study presents a comparative benchmarking framework for unmanned aerial vehicles (UAVs) missing-bolt patch classification using convolutional neural network (CNN), focusing on balancing accuracy and computational efficiency. A UAV-acquired dataset of bolt-centric image patches was developed to evaluate four systematic experimental schemes: (i) a custom lightweight CNN trained from scratch, (ii) the lightweight CNN integrated with Squeeze-and-Excitation (SE) attention blocks across multiple positions, (iii) nine fine-tuned state-of-the-art (SOTA) pretrained CNN backbones, and (iv) SE-enhanced versions of these pretrained models. All architectures were evaluated under a standardised experimental protocol. Results show that the proposed lightweight CNN achieves classification performance comparable to heavyweight pretrained models while requiring significantly lower computational resources. Integrating SE blocks did not improve classification performance for this localised task and, in several configurations, reduced accuracy and training stability. Pretrained transfer learning models achieved high accuracy overall, but their computational complexity limits direct deployment on edge devices and UAV platforms. Grad-CAM visual explanations confirmed that the lightweight CNN consistently focuses on relevant bolt and hole regions. The findings demonstrate that a task-specific lightweight CNN offers a practical balance between inspection reliability and deployment efficiency for automated structural monitoring. Full article
(This article belongs to the Special Issue Intelligent Building Information Modeling (BIM): Advancements in Collaborative Design and Construction)
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24 pages, 447 KB  
Article
Structuring Cost Information in BIM: A Property-Based Mapping Between Regional Price Lists and IFC
by Giorgia Marcellino, Pedro Mêda Magalhães and Carlo Zanchetta
Buildings 2026, 16(13), 2677; https://doi.org/10.3390/buildings16132677 - 6 Jul 2026
Viewed by 546
Abstract
Construction cost estimation often relies on subjective expert judgment, which introduces variability and inconsistency. Standardizing data and procedures can improve reliability and enable repeatable workflows. This research investigates how price lists used for public construction can be semantically linked to Building Information Modeling [...] Read more.
Construction cost estimation often relies on subjective expert judgment, which introduces variability and inconsistency. Standardizing data and procedures can improve reliability and enable repeatable workflows. This research investigates how price lists used for public construction can be semantically linked to Building Information Modeling (BIM) via the Industry Foundation Classes (IFC) standard to support objective, repeatable, semi-automated model-to-cost estimation. By an inductive case-based design, the work uses Veneto Region price list and maps selected cost items to IFC properties. Six representative price list items (slabs, partition walls, plasterboards, plasters, doors, and windows) are examined to identify discriminating parameters (e.g., material, thickness, dimensions, fire rating) that are mappable to IFC entities and property sets. The methodology distinguishes primary charges from surcharges, then assesses the model-ability of parameters and their semantic coherence within BIM’s object-based paradigm. Findings show that through formalization and standardization of cost item characteristics via IFC properties, the approach reduces subjectivity, enabling structured and objective matching and laying the groundwork for future automated workflows. Limitations are discussed, including incomplete representation of some cost-driving attributes, reliance on naming conventions, and opportunities associated with Digital Product Passport implementation (DPP). Full article
(This article belongs to the Special Issue Intelligent Building Information Modeling (BIM): Advancements in Collaborative Design and Construction)
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