Selected Papers from the “24th International Conference on Construction Applications of Virtual Reality—CONVR2024”

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

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 2095

Special Issue Editors


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Guest Editor
School of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW, Australia
Interests: BIM; immersive technologies; artificial intelligence; augmented reality; virtual reality

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Guest Editor
Urban Transformations Research Centre (UTRC), Western Sydney University, Penrith, NSW, Australia
Interests: smart structures; digital twins; immersive technologies; robotic construction; resilience; reliability; structural health monitoring
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Guest Editor
Director, Urban Transformations Research Centre (UTRC), Western Sydney University, Penrith, NSW, Australia
Interests: sustainable building; sustainable districts; sustainable cities; living labs; circular economy

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Guest Editor
Pro-Vice-Chancellor Research and Business Innovation, De Montfort University, Leicester, UK
Interests: healthcare infrastructure; living labs; location-based project planning; decision making; bim; automated regulation capture

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Guest Editor
Research Director, Net Zero Industry Innovation Centre, Centre for Sustainable Engineering, Teesside University, Middlesabrough, UK
Interests: construction management; BIM; decarbonisation; digital health; smart energy systems and transforming construction
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Special Issue Information

Dear Colleagues,

The 24th International Conference on Construction Applications of Virtual Reality—CONVR2024 will be hosted by Western Sydney University and take place in Sydney (Australia) from 4 to 6 November 2024.

CONVR2024 will bring together AEC researchers and practitioners from around the globe to report on and exchange the latest developments, ideas and applications stemming from innovative and cutting-edge research activities in the area of immersive realities and digital transformations, and their impact on sustainable development and net-zero environments.

The conference focuses on the fields of virtual reality (VR) and augmented reality (AR), which are forward-looking technologies that enable considerable benefits in all stages of the architecture, engineering and construction (AEC) process, from initial planning and conceptual design to facility management and operations. Over the last 23 years, CONVR has been held all around the globe and sponsored by major construction and engineering companies.

Conference Website:
https://convr2024.com/

Dr. Aso Hajirasouli
Dr. Ehsan Noroozinejad
Prof. Dr. Gregory M. Morrison
Prof. Dr. Mike Kagioglou
Prof. Dr. Nashwan Dawood
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • virtual reality
  • augmented reality
  • extended reality
  • digital twin
  • smart construction
  • net-zero infrastructure
  • construction technologies
  • energy-efficient building
  • advanced digital manufacturing
  • digital and circular economy

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Published Papers (4 papers)

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Research

24 pages, 1639 KiB  
Article
Exploring the Impact of Construction 4.0 on Industrial Relations: A Comprehensive Thematic Synthesis of Workforce Transformation in the Digital Era of Construction
by Aso Hajirasouli, Ayrin Assadimoghadam, Muhammad Atif Bashir and Saeed Banihashemi
Buildings 2025, 15(9), 1428; https://doi.org/10.3390/buildings15091428 - 24 Apr 2025
Viewed by 315
Abstract
The rise of Construction 4.0—driven by digitalisation, automation, and data-intensive technologies—is radically reshaping the construction industry. While its technological innovations are widely acknowledged, their implications for industrial relations remain underexplored. In this study, we conduct a systematic literature review (SLR) of 91 peer-reviewed [...] Read more.
The rise of Construction 4.0—driven by digitalisation, automation, and data-intensive technologies—is radically reshaping the construction industry. While its technological innovations are widely acknowledged, their implications for industrial relations remain underexplored. In this study, we conduct a systematic literature review (SLR) of 91 peer-reviewed articles published between 2010 and 2024, aiming to synthesise emerging knowledge on how Construction 4.0 is transforming workforce dynamics, employment models, and labour relations. Using NVivo software and an inductive thematic approach, we identify seven key themes: workforce transformation, the attraction of new generations and women, skill requirements and workforce development, supply chain and logistics optimisation, digital twin technology in project management, the emergence of new business models, and safety and risk assessment. Our findings highlight both opportunities—such as improved collaboration, skill diversification, and enhanced productivity—and challenges, including job displacement, digital ethics, and widening disparities between developed and developing countries. Recent studies from 2023 and 2024 underscore routine-biased changes in workforce structure, evolving project management practices through digital twins, and critical skill shortages within the sector. Furthermore, contemporary policy shifts and increasing labour tensions in some regions reveal deeper socio-economic implications of digital construction. This review contributes to a more holistic understanding of how technological innovation intersects with social systems in the built environment. The insights presented offer valuable guidance for policymakers, educators, and industry leaders seeking to navigate the evolving landscape of Construction 4.0. Full article
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27 pages, 11200 KiB  
Article
An Automatic Registration System Based on Augmented Reality to Enhance Civil Infrastructure Inspections
by Leonardo Binni, Massimo Vaccarini, Francesco Spegni, Leonardo Messi and Berardo Naticchia
Buildings 2025, 15(7), 1146; https://doi.org/10.3390/buildings15071146 - 31 Mar 2025
Viewed by 305
Abstract
Manual geometric and semantic alignment of inspection data with existing digital models (field-to-model data registration) and on-site access to relevant information (model-to-field data registration) represent cumbersome procedures that cause significant loss of information and fragmentation, hindering the efficiency of civil infrastructure inspections. To [...] Read more.
Manual geometric and semantic alignment of inspection data with existing digital models (field-to-model data registration) and on-site access to relevant information (model-to-field data registration) represent cumbersome procedures that cause significant loss of information and fragmentation, hindering the efficiency of civil infrastructure inspections. To address the bidirectional registration challenge, this study introduces a high-accuracy automatic registration method and system based on Augmented Reality (AR) that streamlines data exchange between the field and a knowledge graph-based Digital Twin (DT) platform for infrastructure management, and vice versa. A centimeter-level 6-DoF pose estimation of the AR device in large-scale, open unprepared environments is achieved by implementing a hybrid approach based on Real-Time Kinematic and Visual Inertial Odometry to cope with urban-canyon scenarios. For this purpose, a low-cost and non-invasive RTK receiver was prototyped and firmly attached to an AR device (i.e., Microsoft HoloLens 2). Multiple filters and latency compensation techniques were implemented to enhance registration accuracy. The system was tested in a real-world scenario involving the inspection of a highway viaduct. Throughout the use case inspection, the system seamlessly and automatically provided field operators with on-field access to existing DT information (i.e., open BIM models) such as georeferenced holograms and facilitated the enrichment of the asset’s DT through the automatic registration of inspection data (i.e., images) with the open BIM models included in the DT. This study contributes to DT-based civil infrastructure management by establishing a bidirectional and seamless integration between virtual and physical entities. Full article
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33 pages, 18018 KiB  
Article
Automatic Scan-to-BIM—The Impact of Semantic Segmentation Accuracy
by Jidnyasa Patil and Mohsen Kalantari
Buildings 2025, 15(7), 1126; https://doi.org/10.3390/buildings15071126 - 30 Mar 2025
Viewed by 376
Abstract
Scan-to-BIM is the process of converting point cloud data into a Building Information Model (BIM) that has proven essential for the AEC industry. Scan-to-BIM consists of two fundamental tasks—semantic segmentation and 3D reconstruction. Deep learning has proven useful for semantic segmentation, and its [...] Read more.
Scan-to-BIM is the process of converting point cloud data into a Building Information Model (BIM) that has proven essential for the AEC industry. Scan-to-BIM consists of two fundamental tasks—semantic segmentation and 3D reconstruction. Deep learning has proven useful for semantic segmentation, and its integration into the Scan-to-BIM workflow can benefit the automation of BIM reconstruction. Given the rapid advancement of deep learning algorithms in recent years, it is crucial to analyze how their accuracy impacts reconstruction quality. In this study, we compare the performance of five deep learning models—PointNeXt, PointMetaBase, PointTransformer V1, PointTransformer V3, and Swin3D—and examine their influence on wall reconstruction. We propose a novel yet simple workflow that integrates deep learning and RANSAC for reconstructing walls, a fundamental architectural element. Interestingly, our findings reveal that even when semantic segmentation accuracy is lower, reconstruction accuracy may still be high. Swin3D consistently outperformed the other models in both tasks, while PointNeXt, despite weaker segmentation, demonstrated high reconstruction accuracy. PTV3, with its faster performance, is a viable option, whereas PTV1 and PointMetaBase delivered subpar results. We provide insights into why this occurred based on the architectural differences among the deep learning models evaluated. To ensure reproducibility, our study exclusively utilizes open-source software and Python 3.11 for processing, allowing future researchers to replicate and build upon our workflow. Full article
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28 pages, 10942 KiB  
Article
Physics-Based and Data-Driven Retrofitting Solutions for Energy Efficiency and Thermal Comfort in the UK: IoT-Validated Analysis
by Elena Imani, Huda Dawood, Sean Williams and Nashwan Dawood
Buildings 2025, 15(7), 1050; https://doi.org/10.3390/buildings15071050 - 25 Mar 2025
Viewed by 298
Abstract
The application of building retrofitting solutions targeting improved energy efficiency and thermal comfort is significantly influenced by environmental and climate conditions. This study aims to automate a reliable dataset and enhance the predictability of the post-retrofit performance of the buildings. The proposed hybrid [...] Read more.
The application of building retrofitting solutions targeting improved energy efficiency and thermal comfort is significantly influenced by environmental and climate conditions. This study aims to automate a reliable dataset and enhance the predictability of the post-retrofit performance of the buildings. The proposed hybrid methodology utilises physics-based and data-driven methods to evaluate a range of retrofitting scenarios across diverse UK climate zones and validates an automated dataset with real-time data collected via IoT (Internet of things)-based sensors. This hybrid method enables a comprehensive assessment of retrofitting solutions’ impacts on building performance. The collected data create a reliable dataset and serve as the foundation for training machine learning (ML) prediction models and support decisions in retrofit strategies. The findings reveal that in cool–humid climates, the air source heat pumps significantly perform better when compared to 58 heating systems in terms of the balance of energy efficiency and thermal comfort. Moreover, Water Source Heat Pumps (WSHPs) are recommended for colder regions. As a result, zone-specific retrofitting strategies with seasonal adjustments are recommended for achieving optimum energy efficiency and thermal comfort. Full article
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