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Sustainability
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Digital Twinning Technologies for Sustainable Robotic Automation and Digital Management...
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Digital Twinning Technologies for Sustainable Robotic Automation and Digital Management of Construction

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2085

Special Issue Editors

Dr. Daeho Kim

E-Mail Website
Guest Editor
Department of Civil and Mineral Engineering, University of Toronto, 35 St George St., Toronto, ON, Canada
Interests: construction management; robotic automation; total scene understanding; live digital twinning
Special Issues, Collections and Topics in MDPI journals
Dr. Jongwon Ma

E-Mail Website
Guest Editor
Building, Civil and Environmental Engineering Department, Concordia University, Montreal, QC, Canada
Interests: digital twin; building information modeling; scan-to-BIM; automation; artificial intelligence
Dr. Gaang Lee

E-Mail Website
Guest Editor
Civil & Environmental Engineering Department, University of Alberta, Edmonton, AB, Canada
Interests: human-centered construction and built environment management

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, entitled “Digital Twinning Technologies for Sustainable Robotic Automation and Digital Management of Construction”. Although at a nascent stage, the field of construction robotics and the digitization of fieldwork is central to the future of the construction industry. Robotic automation and digitization will improve the productivity and profitability of a wide spectrum of construction projects while mitigating labor shortages, greatly benefiting project owners and contractors. Nowadays, there is a soaring need for sustainable solutions for robotic automation and digital management of construction practices.

This Special Issue covers advanced technologies for the sustainable digital twinning of in-progress construction projects. The topics include, but are not limited to, the following:

  1. Advanced modalities/methods for real-time 3D reconstruction.
  2. Multimodal digital twinning during the construction phase.
  3. Deep neural network (DNN)-powered computer vision models for total scene understanding.
  4. Data generation or synthetization methods for construction DNN training.
  5. Generative AI-based scene inference.
  6. Scan-to-BIM.

We look forward to receiving your contributions.  

Dr. Daeho Kim
Dr. Jongwon Ma
Dr. Gaang 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 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. Sustainability 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 2400 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 twinning
  • construction robotics
  • robot vision
  • computer vision
  • 3D reconstruction
  • multimodal total scene understanding

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Published Papers (1 paper)

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Research

16 pages, 4944 KiB  
Article
Single-Shot Visual Relationship Detection for the Accurate Identification of Contact-Driven Hazards in Sustainable Digitized Construction
by Daeho Kim, Ankit Goyal, SangHyun Lee, Vineet R. Kamat and Meiyin Liu
Sustainability 2024, 16(12), 5058; https://doi.org/10.3390/su16125058 - 14 Jun 2024
Cited by 2 | Viewed by 1472
Abstract
Deploying construction robots alongside workers presents the risk of unwanted forcible contact—a critical safety concern. To address a semantic digital twin where such contact-driven hazards can be monitored accurately, the authors present a single-shot deep neural network (DNN) model that can perform proximity [...] Read more.
Deploying construction robots alongside workers presents the risk of unwanted forcible contact—a critical safety concern. To address a semantic digital twin where such contact-driven hazards can be monitored accurately, the authors present a single-shot deep neural network (DNN) model that can perform proximity and relationship detections simultaneously. Given that workers and construction robots must sometimes collaborate in close proximity, their relationship must be considered, along with proximity, before concluding an event is a hazard. To address this issue, we leveraged a unique two-in-one DNN architecture called Pixel2Graph (i.e., object + relationship detections). The potential of this DNN architecture for relationship detection was confirmed by follow-up testing using real-site images, achieving 90.63% recall@5 when object bounding boxes and classes were given. When integrated with existing proximity monitoring methods, single-shot visual relationship detection will enable the accurate identification of contact-driven hazards in a digital twin platform, an essential step in realizing sustainable and safe collaboration between workers and robots. Full article
(This article belongs to the Special Issue Digital Twinning Technologies for Sustainable Robotic Automation and Digital Management of Construction)
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