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Human-Machine Collaboration in Industrialized Construction: Theories, Approaches, Key Technologies, and...
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Human-Machine Collaboration in Industrialized Construction: Theories, Approaches, Key Technologies, and Applications

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 November 2022) | Viewed by 9620

Special Issue Editors

Dr. Xiao Li

E-Mail Website
Guest Editor
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
Interests: industrialized construction; consortium blockchain; work package-based human-robot collaboration; deep learning
Dr. Yue Teng

E-Mail Website
Guest Editor
Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong
Interests: industrialized construction; carbon emissions; low carbon buildings
Special Issues, Collections and Topics in MDPI journals
Dr. Chengke Wu

E-Mail Website
Guest Editor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: work packaging; ontology; knowledge graph; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hengqin Wu

E-Mail Website
Guest Editor
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
Interests: natural language processing; ICT in construction; deep learning
Prof. Dr. Geoffrey Qiping Shen

E-Mail Website
Guest Editor
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
Interests: industrialized construction; collaborative working; value engineering

Special Issue Information

Dear Colleagues,

Construction is a USD 10 trillion industry that employs about 180 million workers worldwide. However, construction suffers from significant occupational injuries and deaths, stagnant productivity, a lack of skilled laborers, and an aging workforce. To address these challenges, the construction industry is gradually gearing up for AI, automatic machines, and robotics, particularly for industrialized construction. There have been massive attempts to implement diverse machine intelligence applications, e.g., computer visions for construction safety, quality and progress monitoring, natural language processing for construction knowledge discovery and sharing, automation, and robotics, to assist repetitive or hazardous construction tasks. While many research and development efforts have been focusing on improving the functionality and capability of machine intelligence, fundamental questions on human–machine collaboration in construction remain unanswered. Thus, this Special Issue features a collection of studies on theories, approaches, key technologies, and applications of human–machine collaboration towards next-generation industrialized construction.

Submissions for this Special Issue can include, but are not limited to, the following topics:

  • Hybrid intelligence in industrialized construction;
  • Effective information and knowledge management;
  • Human–robot collaboration in industrialized construction;
  • Expert systems for decision making;
  • Knowledge-based systems for industrialized construction;
  • Hybrid collaborative working systems;
  • Behavior patterns of human–machine collaboration;
  • The trust model for human–machine collaboration;
  • Smart project delivery of industrialized construction;
  • Sustainability and decarbonization of human–machine collaboration;
  • Intelligent planning, scheduling, quality assurance, safety monitoring;
  • Blockchain-based collaboration;
  • Digital twin, BIM, and IoT for industrialized construction;
  • Deep learning, federated learning, and natural language processing in construction.

Dr. Xiao Li
Dr. Yue Teng
Dr. Chengke Wu
Dr. Hengqin Wu
Prof. Dr. Geoffrey Qiping Shen
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

  • industrialized construction
  • human–machine interaction
  • knowledge graphs
  • hybrid intelligence
  • collaborative working
  • automation and robotics
  • construction management
  • information management

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

Published Papers (3 papers)

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Research

24 pages, 4285 KiB  
Article
Identification of Environmental Pollutants in Construction Site Monitoring Using Association Rule Mining and Ontology-Based Reasoning
by Zhao Xu, Huixiu Huo and Shuhui Pang
Buildings 2022, 12(12), 2111; https://doi.org/10.3390/buildings12122111 - 1 Dec 2022
Cited by 5 | Viewed by 2167
Abstract
Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of [...] Read more.
Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site. Full article
(This article belongs to the Special Issue Human-Machine Collaboration in Industrialized Construction: Theories, Approaches, Key Technologies, and Applications)
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15 pages, 451 KiB  
Article
Investigating the Key Hindering Factors and Mechanism of BIM Applications Based on Social Network Analysis
by Zezhou Wu, Yun Lu, Qiufeng He, Qing Hong, Changhong Chen and Maxwell Fordjour Antwi-Afari
Buildings 2022, 12(8), 1270; https://doi.org/10.3390/buildings12081270 - 19 Aug 2022
Cited by 5 | Viewed by 2623
Abstract
China’s construction industry is an important driving force for the development of society. Nevertheless, with the recent new normal of economic development, traditional construction approaches cannot meet the requirements of socialist modernization and sustainable construction. As such, the development of the construction industry [...] Read more.
China’s construction industry is an important driving force for the development of society. Nevertheless, with the recent new normal of economic development, traditional construction approaches cannot meet the requirements of socialist modernization and sustainable construction. As such, the development of the construction industry needs to match the recent developmental concept of green environmental protection. Therefore, China’s construction industry needs to explore innovative development paths of transformation and upgrading. Recently, the Chinese government has been vigorously promoting building information modeling (BIM) applications. However, in the real-world construction process, BIM applications have not achieved their expected impacts. To satisfy the practical demands, this research uses the social network analysis method to analyze the key hindering factors in order to clarify the significance and influencing mechanism of each factor. The current study identified 12 key hindering factors that impede the development of BIM applications in China’s construction industry. The results show that a lack of policy guidance and the restriction of relevant laws are the most critical hindering factors. This research contributes to the research of the hindering factors of BIM applications in China and can assist decision makers in formulating appropriate strategies to promote the application and development of information BIM technology in China’s construction industry. Full article
(This article belongs to the Special Issue Human-Machine Collaboration in Industrialized Construction: Theories, Approaches, Key Technologies, and Applications)
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15 pages, 10714 KiB  
Article
A Data-Driven Approach to Trace the Development of Lean Construction in Building Projects: Topic Shift and Main Paths
by Hengqin Wu, Xue Lin, Xiao Li, Boyu Zhang, Clyde Zhengdao Li and Huabo Duan
Buildings 2022, 12(5), 616; https://doi.org/10.3390/buildings12050616 - 7 May 2022
Cited by 5 | Viewed by 3513
Abstract
Due to the varied ideas of lean philosophy adopted in the construction industry, it is challenging to trace the development of lean philosophy in terms of how the field evolved by adopting the lean ideas and how the topic shifted. However, it is [...] Read more.
Due to the varied ideas of lean philosophy adopted in the construction industry, it is challenging to trace the development of lean philosophy in terms of how the field evolved by adopting the lean ideas and how the topic shifted. However, it is challenging to extract useful information from the massive body of literature and to trace the development of Lean Construction in Building Projects. Previous studies have conducted longitudinal analyses of scientific areas depending on the authors’ interpretation and explanation, which is time-consuming and labor-intensive. To address this concern, this study proposes a data-driven approach integrating N-gram extraction, citation analysis, and a global key-route algorithm to trace the development. Based on the collected literature of Lean Construction in Building Projects, N-grams were extracted as topics from the raw texts of titles, abstracts, and keywords, and the shifts in topics were measured. Then, the references were extracted from the literature to create a citation network to represent the knowledge flows, and the global key-route algorithm was used to identify the most valuable flows reflecting the main paths of the development. The results illustrate how Lean Construction in Building Projects evolved and how the topics shifted, providing an exciting opportunity to reveal this development by using a data-driven approach rather than personal judgments. The findings can help us to understand that the field of Lean Construction in Building Projects was driven and motivated not only by the “lean theory”, but also by problems in the practice of building projects. Moreover, lean theory leads to flourishing research on informatization, and BIM will be an important tool to better achieve lean thinking in construction. Full article
(This article belongs to the Special Issue Human-Machine Collaboration in Industrialized Construction: Theories, Approaches, Key Technologies, and Applications)
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