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Buildings
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Digital Twins in Construction Projects
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Digital Twins in Construction Projects

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 (20 May 2024) | Viewed by 7853

Special Issue Editors

Dr. Qiuchen Lu

E-Mail Website
Guest Editor
The Bartlett School of Sustainable Construction, University College London, London WC1E 6BT, UK
Interests: digital twins; image processing; building information modelling; built asset management
Special Issues, Collections and Topics in MDPI journals
Dr. Zigeng Fang

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Nottingham, Nottingham NG7 2RD, UK
Interests: strategic asset management; digital twins; construction automation; machine learning; facility management; carbon footprint; open data
Dr. Yuting Chen

E-Mail Website
Guest Editor
Construction, Property and Surveying, London South Bank University, London SE1 0AA, UK
Interests: contract management; natural language processing; contract enforcement; project governance; relationship management; trust

Special Issue Information

Dear Colleagues,

We are pleased to inform you that we have launched a new Special Issue of Buildings entitled "Digital Twins in Construction Projects".

The emergence of advanced digitalised technologies has caused the construction industry to undergo an unwavering digital transformation. Digital twins, as the core element of construction industry 4.0, have been utilised in construction projects to improve project management, data visualisation, and construction automation.

Despite the growing number of proposed frameworks and architectures and the potential benefits claimed for digital twins, the construction world demands more innovative attempts to link these frameworks to real practice.

This Special Issue focuses on using digital twins in construction projects with a particular focus on achieving construction industry digitalisation. It encourages the utilisation and integration of digital twins with various digitalisation aspects of the construction projects, such as construction informatics, digital transformation, construction simulation, construction automation, and virtual construction. Various existing technologies (such as building information modelling, internet of things, augmented reality, virtual reality, and machine learning) and social aspects (such as cyber security and data ownership) are also encouraged to be utilised and integrated in this Special Issue.

We look forward to receiving your contributions.

Dr. Qiuchen Lu
Dr. Zigeng Fang
Dr. Yuting Chen
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 twins (DTs)
  • construction informatics
  • digital transformation
  • construction simulation
  • construction automation
  • virtual construction
  • building information modelling (BIM)
  • machine learning (ML)

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

Published Papers (4 papers)

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Research

15 pages, 3315 KiB  
Article
MR-Based Human–Robot Collaboration for Iterative Design and Construction Method
by Yang-Ting Shen
Buildings 2024, 14(8), 2436; https://doi.org/10.3390/buildings14082436 - 7 Aug 2024
Cited by 1 | Viewed by 1380
Abstract
The current building industry is facing challenges of labor shortages and labor-intensive practices. Effectively collaborating with robots will be crucial for industry upgrading. This research introduces a MR iterative design and robot-assisted construction mode based on human–robot collaboration, facilitating an integrated process innovation [...] Read more.
The current building industry is facing challenges of labor shortages and labor-intensive practices. Effectively collaborating with robots will be crucial for industry upgrading. This research introduces a MR iterative design and robot-assisted construction mode based on human–robot collaboration, facilitating an integrated process innovation from design to construction. The development of the ROCOS (Robot Collaboration System) comprises three key aspects: (1) Layout Stage: using MR technology to layout the site, forming a full-scale integrated virtual and physical digital twin design environment. (2) Design Stage: conducting virtual iterative design in the digital twin environment and automatically simulating assembly processes. (3) Assembly Stage: translating simulated results into assembly path commands and driving a robotic arm to perform actual assembly. In the end, this research setup two experiments to examine the feasibility of this iterative design–construction loop script. The results showed that although the presence of obstacles reduced the designer’s freedom and increased the number of steps, the designer could still finish both tasks. This means that the ROCOS has value in the prototype of human–robot collaboration. In addition, some valuable findings from users’ feedback showed that potential improvements can be addressed in operability, customization, and real construction scenarios. Full article
(This article belongs to the Special Issue Digital Twins in Construction Projects)
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21 pages, 1044 KiB  
Article
Big Data Adoption in the Chinese Construction Industry: Status Quo, Drivers, Challenges, and Strategies
by Dandan Gong, Xiaojing Zhao and Bohan Yang
Buildings 2024, 14(7), 1891; https://doi.org/10.3390/buildings14071891 - 21 Jun 2024
Viewed by 1590
Abstract
Under the influence of pervasive digital revolution, the accessibility and analysis of ‘big data’ can provide useful insights and help various industries evolve. Despite the popularity of big data, the construction industry is lagging behind other industries in adopting big data technologies. This [...] Read more.
Under the influence of pervasive digital revolution, the accessibility and analysis of ‘big data’ can provide useful insights and help various industries evolve. Despite the popularity of big data, the construction industry is lagging behind other industries in adopting big data technologies. This paper fills the knowledge gap by examining the status quo of big data adoption in companies with different sizes and roles, as well as that in projects with different types, and ascertaining the drivers for and challenges in adopting big data. This paper employed a structured questionnaire survey and statistical analyses to investigate the significance of factors influencing the drivers, challenges, and enhancement strategies of big data adoption, and validated the results with post-study interviews with construction professionals. The results show that big data adoption in the construction industry is affected by the size of companies and the work experience of their employees. Technology advancement, competitiveness, and government plan and policy initiatives are identified as the top three drivers of big data adoption in the construction sector. Moreover, a lack of appropriate supporting systems, difficulties in data collection, and the shortage of knowledge and experience are found to be the major challenges in big data adoption. Finally, the identified top three strategies for overcoming these challenges and promoting big data adoption are ‘clear organization structure’, ‘government incentives’, and ‘the training of information technology (IT) personnel’. The paper suggests the necessity of creating differentiated strategies for big data adoption for companies with different scales and roles, and helps provide useful insights for policy-makers in promoting big data applications. Full article
(This article belongs to the Special Issue Digital Twins in Construction Projects)
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22 pages, 7604 KiB  
Article
An Effective Digital Twin Modeling Method for Infrastructure: Application to Smart Pumping Stations
by Fan Feng, Zhansheng Liu, Guoliang Shi and Yanchi Mo
Buildings 2024, 14(4), 863; https://doi.org/10.3390/buildings14040863 - 22 Mar 2024
Cited by 5 | Viewed by 2085
Abstract
Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is [...] Read more.
Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is still in the first phases of digital twin advancement. A significant obstacle in this field is the absence of established definitions and modeling standards, which impede the precise depiction of infrastructure systems. To address these challenges, this paper proposes a high-precision digital twin modeling method tailored for pumping stations. The method focuses on two key scenarios: first, we construct an overall digital twin model that contains both physical entities and operational processes of pumping stations; second, we design a modeling process applicable to pumping stations by analyzing the deficiencies of the existing standard system. Additionally, we selected the East–West Water Transfer Project in China as a case study to demonstrate the high-precision digital twin model of a pumping station. This model will include essential components, such as the modeling of pumping stations, the operational processes of pumping stations, and the modeling of system operation analysis. Serving as the database for the digital twin, it can complete the automatic inspection of the pumping station, optimization of scheduling, prediction and regulation of energy and carbon emissions, and visualization of results for display and other applications. The model realized the benefits of 100% automatic inspection rate, reduction of eight corresponding operating personnel, and comprehensive cost saving of RMB 2.25 million. The objective of this research is to narrow the divide between theoretical concepts and real-world implementations by pushing the boundaries of digital twin modeling and offering valuable insights for its utilization in the infrastructure industry. It establishes the foundation for progress in the field of digital twin technology in the specific context of intricate infrastructure projects. This project aims to improve the practicality of digital twin technology in real-world situations, namely in the infrastructure industry. Full article
(This article belongs to the Special Issue Digital Twins in Construction Projects)
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19 pages, 10889 KiB  
Article
A Web3D Rendering Optimization Algorithm for Pipeline BIM Models
by Xiaoyu Wang, Liang Huo, Tao Shen, Xincheng Yang and Haoyuan Bai
Buildings 2023, 13(9), 2309; https://doi.org/10.3390/buildings13092309 - 11 Sep 2023
Cited by 2 | Viewed by 2002
Abstract
BIM (building information modeling) plays a pivotal role in the construction industry. BIM technology tailored for pipelines offers in-depth semantic information and spatial data, bolstering the utility and implementation of digital twin-associated technologies in both architecture and urban planning. This paper introduces a [...] Read more.
BIM (building information modeling) plays a pivotal role in the construction industry. BIM technology tailored for pipelines offers in-depth semantic information and spatial data, bolstering the utility and implementation of digital twin-associated technologies in both architecture and urban planning. This paper introduces a rendering optimization algorithm rooted in the BSP Tree (Binary Space Partitioning Tree). The algorithm is used to address the challenges of slow loading and poor rendering quality of pipeline BIM models when displayed on the web, which stem from large amounts of model data and complex geometric configurations. Initially, the algorithm delves into the geometric distribution traits of the pipeline BIM model from multiple perspectives, pinpointing the spatial division dimension. Subsequently, it employs an adaptive step size technique for spatial segmentation, harmonizing it with real-world application contexts. Concurrently, any superfluous data that emerge are refined to uphold the structural wholeness of the BIM model. This algorithm is adept at systematically arranging and overseeing the BIM model data. Trial outcomes reveal that the AKDT (Adaptive K-Dimensional Tree) algorithm significantly trims the browser’s initial rendering duration while maintaining the model’s accuracy and semantic uniformity. Moreover, it excels in areas such as rendering frame rate, user interaction responsiveness, and data transmission duration. In essence, the algorithm stands out for its efficiency and precision in rendering pipeline BIM models on web platforms, achieving the desired optimization results. Full article
(This article belongs to the Special Issue Digital Twins in Construction Projects)
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