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Buildings
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Construction Automation: Current and Future
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Construction Automation: Current and Future

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 (10 November 2023) | Viewed by 17021

Special Issue Editors

Dr. Lei Hou

E-Mail Website
Guest Editor
School of Engineering, STEM College, RMIT University, Melbourne, VIC 3001, Australia
Interests: virtual reality; augmented reality; building information modelling; machine learning; game engine; sensing and tracking; construction management; ergonomics; safety; productivity
Dr. Jun Wang

E-Mail Website
Guest Editor
School of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW, Australia
Interests: BIM; computer vision; construction automation; lean construction; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Sheng Xu

E-Mail Website
Guest Editor
School of Economics and Management, Chang'an University, Xi'an, China
Interests: construction safety management; civil engineering informationization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is becoming increasingly clear that the automation of construction can address various and serious issues related to construction, for example, the low quality of final products, shortages of skilled labour, poor safety, quality, productivity, tight schedules, sustainability, and a circular economy, which are features of building and infrastructure projects in the modern day. The most effective deployments of automated technologies in the construction industry, such as autonomous robotic systems and rapidly maturing artificial intelligence (AI), will be implemented alongside human workers, and they can already assist in performing many physical tasks more efficiently, safely, and productively than humans can. Striking the proper human–automation balance requires a deep understanding of the technological tools we have at present or in the foreseeable future and, as such, we have proposed a Special Issue named ‘Construction Automation: Current and Future’ to gather research works around the cutting-edge automated technologies that have already begun to revolutionise design, construction, operation, maintenance, demolition, and the recycling of roads and runways construction, structures, buildings construction, ports, tunnels, and factories.

All submitted papers to this research topic should focus on state-of-the-art research in various aspects of the adoption of automation technologies from the academic and industry perspectives. Within this, the themes of interest include, but are not limited to:

  • 3D Printing;
  • Automation and Robotics;
  • Computer Vision;
  • Artificial Intelligence;
  • Machine and Deep Learning;
  • Digital Fabrication;
  • BIM, VR, AR, MR;
  • Laser Scan, Reverse Modelling;
  • Digital Twin;
  • Internet of Things;
  • Wearable Sensing and Tracking;
  • Safety, Efficiency, Human Cognition, Ergonomics.

Dr. Lei Hou
Dr. Jun Wang
Dr. Sheng Xu
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 monthly 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

  • 3D printing
  • automation and robotics
  • computer vision
  • artificial intelligence
  • machine and deep learning
  • digital fabrication
  • BIM, VR, AR, MR
  • laser scan, reverse modelling
  • digital twin
  • internet of things
  • wearable sensing and tracking
  • safety, efficiency, human cognition, ergonomics

Published Papers (7 papers)

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Research

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28 pages, 2378 KiB  
Article
Automated PLC Code Generation for the Implementation of Mode-Based Control Algorithms in Buildings
by Xiaoye Cai, Zhijian Jin, Hanyu Li, Alexander Kümpel and Dirk Müller
Buildings 2024, 14(1), 73; https://doi.org/10.3390/buildings14010073 - 26 Dec 2023
Viewed by 1336
Abstract
Faulty programming of control functions in Building Automation and Control Systems (BACS) might result in inefficient building operations. To reduce programming errors, an automated implementation process of control functions might be a promising solution. Recently, Building Information Modeling (BIM) contributes to digitizing building [...] Read more.
Faulty programming of control functions in Building Automation and Control Systems (BACS) might result in inefficient building operations. To reduce programming errors, an automated implementation process of control functions might be a promising solution. Recently, Building Information Modeling (BIM) contributes to digitizing building construction projects but is rarely used in the planning and implementation of control functions in BACS. The control description in BIM also remains unclear. Regarding these problems, a control documentation method for BIM and an automated control implementation approach can simplify control implementation in BACS and hence improve the building operation. In the previous work, we developed the MODI method for a structured planning process of mode-based control algorithms for building energy systems. This method showed the potential to support digitized control planning and implementation in BACS. Based on this, in this paper, we introduce a documentation method to report mode-based control algorithms in the industrial foundation class (IFC), enabling data sharing among BIM, and a software-assisted approach to automatically generate PLC codes for implementing these algorithms. The case study demonstrates the documentation of a desired mode-based control strategy for an energy supply network in IFC and the implementation of this strategy in a PLC program. In the simulation phase, we test the implemented control strategy to verify the functionalities of the PLC program. The results prove that mode-based control strategies can be fully automatically implemented in a PLC program based on IFC data. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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13 pages, 4754 KiB  
Article
Measurement of Water Retention Ratio in Rust Layer by Electrical Resistance
by Rina Hasuike, Ryotaro Yoneyama and Toshihiko Aso
Buildings 2023, 13(12), 2921; https://doi.org/10.3390/buildings13122921 - 23 Nov 2023
Viewed by 501
Abstract
One significant form of deterioration in weathering steel bridges is corrosion, and steel requires water and oxygen to corrode. As a measurement method for the wetness time of the rust layer on weathering steel, measuring electrical resistance has been proposed. In this research, [...] Read more.
One significant form of deterioration in weathering steel bridges is corrosion, and steel requires water and oxygen to corrode. As a measurement method for the wetness time of the rust layer on weathering steel, measuring electrical resistance has been proposed. In this research, the fundamental data have been collected as preliminary considerations to develop this method of measuring water retention in the rust layer. Based on the measurement of specimens, it is revealed that measuring the exact amount of water retention is difficult because electrical resistance depends on the thickness of the rust layer and the supplied amount of NaCl. Thus, the water retention ratio is calculated by dividing the mass of the water-retained specimen by the mass of the full water-retained specimen. These measurement results suggest a potential method for predicting water retention ratio by measuring electrical resistance and rust thickness. The approximate water retention ratio is predicted by plotting electrical resistance and rust thickness in the proposed diagram. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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21 pages, 4880 KiB  
Article
Effective Motion Sensors and Deep Learning Techniques for Unmanned Ground Vehicle (UGV)-Based Automated Pavement Layer Change Detection in Road Construction
by Tirth Patel, Brian H. W. Guo, Jacobus Daniel van der Walt and Yang Zou
Buildings 2023, 13(1), 5; https://doi.org/10.3390/buildings13010005 - 20 Dec 2022
Cited by 3 | Viewed by 2464
Abstract
As-built progress of the constructed pavement should be monitored effectively to provide prompt project control. However, current pavement construction progress monitoring practices (e.g., data collection, processing, and analysis) are typically manual, time-consuming, tedious, and error-prone. To address this, this study proposes sensors mounted [...] Read more.
As-built progress of the constructed pavement should be monitored effectively to provide prompt project control. However, current pavement construction progress monitoring practices (e.g., data collection, processing, and analysis) are typically manual, time-consuming, tedious, and error-prone. To address this, this study proposes sensors mounted using a UGV-based methodology to develop a pavement layer change classifier measuring pavement construction progress automatically. Initially, data were collected using the UGV equipped with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope, and GPS sensor in a controlled environment by constructing various scenarios of pavement layer change. Subsequently, four Long Short-Term Memory network variants (LSTMs) (LSTM, BiLSTM, CNN-LSTM, and ConvLSTM) were implemented on collected sensor data combinations for developing pavement layer change classifiers. The authors conducted the experiment to select the best sensor combinations for feature detection of the layer change classifier model. Subsequently, individual performance measures of each class with learning curves and confusion matrices were generated using sensor combination data to find out the best algorithm among all implemented algorithms. The experimental result demonstrates the (az + gx + D) sensor combination as the best feature detector with high-performance measures (accuracy, precision, recall, and F1 score). The result also confirms the ConvLSTM as the best algorithm with the highest overall accuracy of 97.88% with (az + gx + D) sensor combination data. The high-performance measures with the proposed approach confirm the feasibility of detecting pavement layer changes in real pavement construction projects. This proposed approach can potentially improve the efficiency of road construction progress measurement. This research study is a stepping stone for automated road construction progress monitoring. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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Review

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29 pages, 4888 KiB  
Review
A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management
by Ruijie Cheng, Lei Hou and Sheng Xu
Buildings 2023, 13(5), 1143; https://doi.org/10.3390/buildings13051143 - 25 Apr 2023
Cited by 5 | Viewed by 3246
Abstract
Natural disasters can cause severe damages to civil infrastructure and lead to extensive economic losses and casualties. To improve the emergency response capability of civil infrastructure under extreme circumstances such as natural disasters and human-caused hazards, intelligent technology for infrastructure emergency management has [...] Read more.
Natural disasters can cause severe damages to civil infrastructure and lead to extensive economic losses and casualties. To improve the emergency response capability of civil infrastructure under extreme circumstances such as natural disasters and human-caused hazards, intelligent technology for infrastructure emergency management has been extensively studied. As an emerging paradigm of interdisciplinary convergence, digital twins (DTs) can integrate intelligent technology into different stages of emergency management and provide a new solution for the emergency management of civil infrastructure (EMCI). However, applications of DT in EMCI have several limitations and are mostly case by case. However, the sector needs more generalisable lessons to address the greater value of DT in the context of EMCI. To address this gap, we first carry out a systematic literature review and analyse the latest progress and previous research deficiencies of DT by taking the scientometrical approach. Next, a framework is proposed to explain how DT can be applied to the mitigation, preparation, response, and recovery stages of EMCI. Lastly, the trends and prospects of DT applications in EMCI are discussed. Overall, the knowledge gained from this study will promote the research and development of more-viable DTs to address the sector’s demand for emergency management. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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22 pages, 5875 KiB  
Review
Extended Reality for Safe and Effective Construction Management: State-of-the-Art, Challenges, and Future Directions
by Xuefeng Zhao, Meng Zhang, Xiongtao Fan, Zhe Sun, Mengxuan Li, Wangbing Li and Lingli Huang
Buildings 2023, 13(1), 155; https://doi.org/10.3390/buildings13010155 - 07 Jan 2023
Cited by 3 | Viewed by 2725
Abstract
Safe and effective construction management requires tools for reducing delays, eliminating reworks, and avoiding accidents. Unfortunately, challenges still exist in current construction practices for enabling real-time interactions among project participants, field discoveries, and massive data. Extended reality (i.e., XR) could help to establish [...] Read more.
Safe and effective construction management requires tools for reducing delays, eliminating reworks, and avoiding accidents. Unfortunately, challenges still exist in current construction practices for enabling real-time interactions among project participants, field discoveries, and massive data. Extended reality (i.e., XR) could help to establish immersive and interactive virtual environments that enable real-time information exchange among humans, cyber processes, and physical environments during construction. However, limited studies have synthesized potentials, challenges, and scenarios of XR for ensuring construction safety and efficiency. This study provides a critical review that synthesizes XR in construction management. First, the authors used the PRISMA method to screen studies related to XR in construction management. Seventy-nine studies were selected and comprehensively analyzed. The authors conducted a bibliometric analysis to comprehend the spatiotemporal distributions of the selected studies. Then, the selected studies were classified into three categories: (1) progress control, (2) quality control, and (3) safety management. The authors also synthesized information for XR applications in various construction management scenarios and summarized the challenges related to XR applications. Finally, this review shed light on future research directions of XR for safe and effective construction management. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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21 pages, 2365 KiB  
Review
Exploring the Knowledge Domain of Risk Management in Prefabricated Construction
by Tianxin Li, Zhongfu Li, Long Li and Peng Jiang
Buildings 2022, 12(11), 1784; https://doi.org/10.3390/buildings12111784 - 24 Oct 2022
Cited by 4 | Viewed by 2139
Abstract
Risks hinder the role of prefabricated construction (PC) in promoting construction automation. Although existing research has focused on risk management (RM) in PC, the lack of a global perspective has affected the effectiveness of RM. Accordingly, this paper adopts a scientometric analysis to [...] Read more.
Risks hinder the role of prefabricated construction (PC) in promoting construction automation. Although existing research has focused on risk management (RM) in PC, the lack of a global perspective has affected the effectiveness of RM. Accordingly, this paper adopts a scientometric analysis to review the knowledge domain of RM in PC. A total of 144 articles were selected from the Scopus database for journal citation analysis, document co-citation analysis, and keyword co-occurrence analysis. The results show that since 2011, the annual publications show an overall upward trend. International Journal of Construction Management, Journal of Building Engineering, and Buildings have been cited more frequently recently. PC research, RM research, environmental sustainability research, and ergonomic research provide a solid foundation for the research on RM in PC. Existing studies are conducted from five knowledge themes, namely, Supply chain and industry, Decision and optimization, Safety and health, Environment and overheating, and Investment and cost. Further, current research content, future research needs, and RM strategies for PC practices are discussed. This study helps stimulate further promising research and enhance the effectiveness of RM in PC in practice. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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33 pages, 19374 KiB  
Review
Towards Sustainable Development through the Perspective of Construction 4.0: Systematic Literature Review and Bibliometric Analysis
by Kaiyang Wang and Fangyu Guo
Buildings 2022, 12(10), 1708; https://doi.org/10.3390/buildings12101708 - 17 Oct 2022
Cited by 14 | Viewed by 3001
Abstract
The construction industry utilizes a substantial number of resources, which has negative impacts on both environmental and socioeconomic aspects. Therefore, it is important to reduce these negative impacts and maintain sustainable development (SD). Recent studies suggest that integrating Industry 4.0 (also called Construction [...] Read more.
The construction industry utilizes a substantial number of resources, which has negative impacts on both environmental and socioeconomic aspects. Therefore, it is important to reduce these negative impacts and maintain sustainable development (SD). Recent studies suggest that integrating Industry 4.0 (also called Construction 4.0 (C4.0) in the construction industry) and SD may help address these concerns, which is a new and ever-evolving field. In order to fully understand SD in the C4.0 context, this paper offers a verifiable and reproducible systematic literature review and bibliometric analysis of associated topics. Through a review of 229 works, this article presents the publication trend, the most prolific journals, countries, institutions, researchers, and keywords analysis, as well as the content analysis of C4.0 impacts on SD based on triple-bottom-line (TBL) dimensions. The authors also identify and summarize the critical success factors (CSFs) of C4.0 toward SD. Overall, findings reveal the potential benefits of C4.0 on SD and contribute to the evaluation of sustainable C4.0 innovations. The key topics and CSFs identified in this work could potentially serve as the basis for future investigations, encouraging and directing interested researchers, and thus supporting both theoretical and practical progress in this evolving research area. Full article
(This article belongs to the Special Issue Construction Automation: Current and Future)
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