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Large Language Models and Multimodal AI for Next-Generation Building Information...
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Large Language Models and Multimodal AI for Next-Generation Building Information Modeling (BIM): From Conversational Agents to Immersive Design Collaboration

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1060

Special Issue Editors

Dr. Muhammad Shoaib Khan

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Kookmin University, Seoul 02707, Republic of Korea
Interests: BIM; digital twin; large language models; knowledge graph; VR/AR; construction informatics
Prof. Dr. Sisi Zlatanova

E-Mail Website
Guest Editor
Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Interests: 3D indoor modelling; 3D GIS; integration of BIM and GIS; 3D spatial analysis; DBMS; emergency response
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jongwon Seo

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: GIS; BIM; construction automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI), particularly large language models (LLMs), is revolutionizing how building information modeling (BIM) data is created, interpreted, shared and utilized throughout the lifecycle of built assets. BIM has become the central hub for information management in the architecture, engineering, and construction (AEC) industry, offering a rich foundation for embedding intelligent automation and human–AI collaboration. LLM-driven platforms are now enabling context-aware information retrieval, natural language interaction with design and construction data, automated compliance checking and immersive decision-making experiences through VR/AR environments. In parallel, the integration of LLMs with knowledge graphs, digital twins, robotics and multimodal interfaces is paving the way for autonomous design assistance, predictive planning and real-time construction monitoring.

This Special Issue focuses on emerging research and advanced applications of LLMs in BIM—ranging from conversational AI agents and reasoning-enhanced data management to multi-agent collaboration and virtual co-design. We welcome high-quality contributions addressing technical innovations, implementation frameworks, case studies and future trends that will define the next generation of intelligent and immersive BIM-enabled construction.

Dr. Muhammad Shoaib Khan
Prof. Dr. Sisi Zlatanova
Prof. Dr. Jongwon Seo
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-blind 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 (BIM)
  • large language models (LLMs)
  • conversational AI
  • VR/AR-enabled collaboration
  • construction informatics
  • multimodal interaction
  • agentic AI for AEC

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Published Papers (1 paper)

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Research

28 pages, 21526 KB  
Article
HCFF-Net: A Hybrid Contextual Feature Fusion Network for Robust Heavy Equipment Classification in Complex Construction Environments
by Hamza Sultan and Jongsoo Choi
Buildings 2026, 16(9), 1764; https://doi.org/10.3390/buildings16091764 - 29 Apr 2026
Viewed by 303
Abstract
Construction equipment plays a crucial role in performing essential tasks such as construction, demolition, and other maintenance. These machines enable workers to accomplish tasks that would otherwise be extremely difficult or impossible manually, significantly enhancing efficiency and productivity. Heavy construction equipment classification is [...] Read more.
Construction equipment plays a crucial role in performing essential tasks such as construction, demolition, and other maintenance. These machines enable workers to accomplish tasks that would otherwise be extremely difficult or impossible manually, significantly enhancing efficiency and productivity. Heavy construction equipment classification is a critical component of intelligent construction monitoring systems; however, existing vision-based methods often struggle under real-world conditions such as occlusion, background clutter, and scale variation. To address these challenges, this study proposes HCFF-Net, a hybrid contextual feature fusion network designed to enhance classification robustness in complex construction environments. The proposed framework integrates a diverse receptive residual fusion (DRRF) block to capture multi-scale local and global features and a global contextual channel recalibration (GCCR) module to adaptively refine channel-wise representations using contextual information. Unlike conventional feature fusion strategies, HCFF-Net effectively combines structural and contextual features to improve discriminative capability under challenging visual conditions. For performance evaluation, experiments were performed on the publicly available Alberta Construction Image Dataset (ACID). The proposed HCFF-Net achieves a classification accuracy of 90.60% and an F1-score of 90.05% across multiple equipment categories, outperforming state-of-the-art methods, validating its effectiveness for intelligent safety monitoring and management in construction environments. Full article
(This article belongs to the Special Issue Large Language Models and Multimodal AI for Next-Generation Building Information Modeling (BIM): From Conversational Agents to Immersive Design Collaboration)
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