Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.4
3.1
Journals
Buildings
Special Issues
Digital Twins in the Building Industry
share announcement

Digital Twins in the Building Industry

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 (15 February 2024) | Viewed by 10112

Special Issue Editors

Dr. Issa Ramaji

E-Mail Website
Guest Editor
School of Engineering, Computing and Construction Management, Roger Williams University, Bristol, RI 02809, USA
Interests: digital twin; digital engineering; building operation; building automation systems; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Anne Anderson

E-Mail Website
Guest Editor
School of Engineering, Computing and Construction Management, Roger Williams University, Bristol, RI 02809, USA
Interests: virtual design and construction; building information modeling; global virtual teams; technologies in construction; construction management education
Dr. Hongtao Dang

E-Mail Website
Guest Editor
School of Design and Construction, Washington State University, Pullman, WA 99164, USA
Interests: data analytics and construction automation; building information modeling; virtual design and construction; digital project delivery

Special Issue Information

Dear Colleagues,

A digital twin is a virtual representation of a physical object or system. It is created using data and simulations, allowing users to monitor, analyze, and optimize the performance of the physical object or system in real-time and throughout its lifecycle. Digital twins can have various applications, including in manufacturing, transportation, and construction. For example, digital twins can be used to optimize the design, engineering, construction, and operation of buildings and infrastructure.

For this Special Issue, we are interested in articles that explore the use of digital twins in the building industry, including case studies and technical papers. Potential topics may include, but are not limited to:

  • Applications of digital twins in building design, construction, and operation;
  • Challenges and opportunities in the adoption of digital twins in the building industry;
  • Case studies demonstrating the use of digital twins in the building industry;
  • The roles of digital twins in the design and construction of new buildings;
  • The roles of digital twins in improving energy efficiency, safety, and sustainability in buildings;
  • The interactions and integration of digital twins in the design, construction, operation, and management of buildings;
  • Ethical and privacy considerations surrounding the use of digital twins in the building industry;
  • Future directions for research and development in digital twins.

Dr. Issa Ramaji
Dr. Anne Anderson
Dr. Hongtao Dang
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

  • digital twin
  • Internet of Things
  • BIM
  • building performance simulation
  • real-time assessments
  • sustainability
  • smart buildings
  • artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 1207 KiB  
Article
Towards the Uptake of Digital Technologies for Construction Information Management: A Partial Least Squares Structural Equation Modelling Approach
by Peter Adekunle, Clinton Aigbavboa, Opeoluwa Akinradewo, Matthew Ikuabe and Kenneth Otasowie
Buildings 2024, 14(3), 827; https://doi.org/10.3390/buildings14030827 - 19 Mar 2024
Cited by 7 | Viewed by 2257
Abstract
The primary objective of this study survey is to close knowledge gaps by measuring the responses from construction experts and investigating the significant effects of using digital technologies in construction information management (CIM). This is attributed to the lack of thorough knowledge among [...] Read more.
The primary objective of this study survey is to close knowledge gaps by measuring the responses from construction experts and investigating the significant effects of using digital technologies in construction information management (CIM). This is attributed to the lack of thorough knowledge among construction professionals on the implications and efficacy of incorporating digital tools in construction information management. A thorough analysis of the literature on the use of digital technologies revealed outcomes related to digitized ways of managing construction information, which were then contextually tailored through a pilot study and presented in the form of a postulated model. A total of 257 stakeholders in the building industry were given questionnaire surveys to complete in order to gather primary data. The final model of the result of adopting digital technology was statistically validated using partial least squares structural equation modelling (PLS-SEM). By concentrating on the quantitative contribution of the most important result to the adoption of digital technologies throughout the process of CIM, this study closes this knowledge gap. The three primary benefits that digital technologies have the most influence on are communication, operational efficiency, and market intelligence, according to this paper’s conclusions. The research showed that encouraging relationships that enable the use of digital technologies should be promoted between technology providers and construction companies. In order to adopt and improve digital solutions, construction firms and technology providers will be able to collaborate in an ecosystem. By shedding light on the implementation and impact of digital technologies in the construction sector, the study helps to close this knowledge gap. The study offers valuable information for upcoming initiatives that support digital transformation through construction methods. The results serve as instructions for the government authorities to help them focus their efforts and distribute their resources more effectively. Full article
(This article belongs to the Special Issue Digital Twins in the Building Industry)
►▼ Show Figures

Figure 1

Review

Jump to: Research

32 pages, 4169 KiB  
Review
Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review
by Faeze Hodavand, Issa J. Ramaji and Naimeh Sadeghi
Buildings 2023, 13(6), 1426; https://doi.org/10.3390/buildings13061426 - 31 May 2023
Cited by 40 | Viewed by 6917
Abstract
Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for [...] Read more.
Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation. Full article
(This article belongs to the Special Issue Digital Twins in the Building Industry)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop