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Digital Twins and Robot Sensing for Smart Construction and Facilities Management

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 11823

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Special Issue Editors

Dr. Vincent Gan

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Guest Editor

Department of the Built Environment, National University of Singapore, Singapore 119243, Singapore
Interests: robot-assisted sensing; building information modelling; digital twins; digital construction
Special Issues, Collections and Topics in MDPI journals

Dr. Ali Ghahramani

E-Mail Website
Guest Editor

Department of the Built Environment, National University of Singapore, Singapore 119243, Singapore
Interests: human-centered AI; indoor environmental quality; smart buildings; occupant-centric controls

Dr. Weiwei Chen

E-Mail Website
Guest Editor

Bartlett School of Sustainable Construction, University College London, 1-19 Torrington Pl, London WC1E 7HB, UK
Interests: digital twins; BIM; facility management; 3D reconstruction; IoT; information interoperability
Special Issues, Collections and Topics in MDPI journals

Dr. Mingzhu Wang

E-Mail Website
Guest Editor

School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: intelligent infrastructure monitoring and maintenance; smart construction; BIM; human-centric digital twin; computer vision; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has severely disrupted global economic activities, and has very rapidly changed the way we designed, operated, and maintained the built environment. As previous relevant research on building design has much focused on structure, function and appearance, planners in trying to anticipate the new environmental inventions as related to the building performance faces exceptional difficulties. There is huge unmet needs to transform conventional operating arrays of old buildings towards autonomous built environment for improving its sustainability, resiliency, and well-being. Nowadays, innovative digital technologies such as Digital Twin, Robotics, Artificial Intelligence, and 5G have bring new opportunities for research, and seek to transform the way people design and manage the built environment. This Special Issue aims to collect state-of-the-art research on the latest development of innovative digital solutions for autonomous built environment management. Critical reviews and original research papers which address current research gaps, theoretical frameworks, methodologies, and case studies are welcome.

Potential topics include but not limited to the following:

Critical reviews on state-of-the-art of sensing technologies and their applications in the built environment field such as 3D (BIM) reconstruction, building inspection, and construction process monitoring;
New methods, technologies, and approaches for sensor data collection, including laser scanning, photogrammetry, Simultaneous localization and mapping (SLAM), and Internet of Things (IoT);
Design and development of new sensor and measurement systems, and applications of robot sensing such as 5G mobile robots, multi-sensor automated system, unmanned ground/aerial vehicles, and visual sensing with robots;
New technologies and approaches, i.e. computer vision, machine learning, and deep learning to process the sensing data (such as semantic segmentation, object detection);
Integration of sensing data with model-based or data-driven approaches for predictive operation and maintenance of the built environment; applications of 3D building digital models (such as building information model) to predict, analyse, and optimise the building performance;

Dr. Vincent Gan
Dr. Ali Ghahramani
Dr. Weiwei Chen
Dr. Mingzhu Wang
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. Sensors 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.

Published Papers (3 papers)

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Research

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24 pages, 25817 KiB

Open AccessArticle

Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras

by Jiyao Wang, Qilin Zhang, Bin Yang and Binghan Zhang

Sensors 2023, 23(10), 4851; https://doi.org/10.3390/s23104851 - 17 May 2023

Cited by 5 | Viewed by 2000

Abstract

Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis. Full article

(This article belongs to the Special Issue Digital Twins and Robot Sensing for Smart Construction and Facilities Management)

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Review

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35 pages, 1663 KiB

Open AccessReview

Artificial Intelligence Methods for the Construction and Management of Buildings

by Svetlana Ivanova, Aleksandr Kuznetsov, Roman Zverev and Artem Rada

Sensors 2023, 23(21), 8740; https://doi.org/10.3390/s23218740 - 26 Oct 2023

Cited by 4 | Viewed by 6612

Abstract

Artificial intelligence covers a variety of methods and disciplines including vision, perception, speech and dialogue, decision making and planning, problem solving, robotics and other applications in which self-learning is possible. The aim of this work was to study the possibilities of using AI algorithms at various stages of construction to ensure the safety of the process. The objects of this research were scientific publications about the use of artificial intelligence in construction and ways to optimize this process. To search for information, Scopus and Web of Science databases were used for the period from the early 1990s (the appearance of the first publication on the topic) until the end of 2022. Generalization was the main method. It has been established that artificial intelligence is a set of technologies and methods used to complement traditional human qualities, such as intelligence as well as analytical and other abilities. The use of 3D modeling for the design of buildings, machine learning for the conceptualization of design in 3D, computer vision, planning for the effective use of construction equipment, artificial intelligence and artificial superintelligence have been studied. It is proven that automatic programming for natural language processing, knowledge-based systems, robots, building maintenance, adaptive strategies, adaptive programming, genetic algorithms and the use of unmanned aircraft systems allow an evaluation of the use of artificial intelligence in construction. The prospects of using AI in construction are shown. Full article

(This article belongs to the Special Issue Digital Twins and Robot Sensing for Smart Construction and Facilities Management)

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Other

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32 pages, 11489 KiB

Open AccessSystematic Review

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

by Viktor Drobnyi, Zhiqi Hu, Yasmin Fathy and Ioannis Brilakis

Sensors 2023, 23(9), 4382; https://doi.org/10.3390/s23094382 - 28 Apr 2023

Cited by 10 | Viewed by 2644

Abstract

Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets. Full article

(This article belongs to the Special Issue Digital Twins and Robot Sensing for Smart Construction and Facilities Management)

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