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Sustainability
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Industrialized and Automated Construction in the Context of Sustainability
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Industrialized and Automated Construction in the Context of Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Green Building".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 12313

Special Issue Editors

Dr. Tae Wan Kim

E-Mail
Guest Editor
Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Republic of Korea
Interests: automation in construction; offsite construction; decision support systems; digital twin and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Ahn

E-Mail Website
Guest Editor
University of South Australia, UniSA STEM, SA, 5000, Australia
Interests: construction safety; ergonomics; human factors; automation in construction; human–machine interaction

Special Issue Information

‘Sustainable construction’ has been a buzzword over the last decade, as the materials and methods used in construction processes have a great impact on the ecological, social, and economic sustainability of businesses and societies. During this time, construction production systems have evolved to reduce wastes, reuse and recycle more materials, minimize pollution and adverse impacts on the environment, provide protection against safety incidents and health issues, and enhance building performance while using energy, water, and other resources more efficiently. Industrialization and automation are the two keywords in this direction of evolution in the construction industry. Industrialized construction, also known as offsite construction, transfers some construction production processes from a hazardous, hard-to-organize outdoor work environment to a more controlled, indoor environment where sustainability, health, and safety goals can be achieved more easily. Smart devices and sensing technologies combined with artificial intelligence have also enabled construction project participants to make critical decisions toward the sustainability goals of the project more proactively and accurately. More and more automated systems are being introduced into the entire life cycle of construction projects, including site mobilization, planning, the supply chain, production monitoring and control, and commissioning, contributing to achieving societal, economic, and ecological sustainability goals in construction projects.

Against this background, this Special Issue aims to collect outstanding original research and case study papers on the use and implications of industrialized and automated construction for sustainability on various levels, e.g., projects, businesses, industries, and societies. We hope that such a collection of research outputs will contribute to enhancing our understanding of state-of-the-art methods and practices for sustainable construction in communities of construction researchers and practitioners.

Topics of interest include, but are not limited to:

  • impacts of industrialized construction on sustainability;
  • policies on industrialized construction towards sustainability;
  • assessment of the performance of buildings constructed offsite;
  • decision-making support systems for sustainable construction;
  • societal implications of industrialized construction;
  • automated systems for sustainable and safe construction processes;
  • use of artificial intelligence for sustainable construction;
  • case studies on industrialized and automated construction for sustainability;
  • construction waste management assisted by automated systems;
  • sustainability issues in the production and transportation of construction materials; and
  • application of smart manufacturing theories for industrialized construction. 

Dr. Tae Wan Kim
Dr. Jun Ahn
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

  • sustainable construction
  • industrialized construction
  • offsite construction
  • automation in construction
  • site protection plan
  • site planning
  • worker safety
  • life cycle assessment
  • waste management
  • construction wastes
  • material management
  • building performance assessment
  • decision-making for sustainability

Published Papers (4 papers)

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Research

16 pages, 2506 KiB  
Article
Expediting the Cost Estimation Process for Aged-Housing Renovation Projects Using a Probabilistic Deep Learning Approach
by Jun Kim and Hee Sung Cha
Sustainability 2022, 14(1), 564; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010564 - 05 Jan 2022
Cited by 4 | Viewed by 2202
Abstract
Since the early 1980s, the Korean government has rapidly boosted residential buildings to cope with substantial housing shortages. However, as buildings have been aging simultaneously, the performance of a large number of residential buildings has deteriorated. A government plan to upgrade poor housing [...] Read more.
Since the early 1980s, the Korean government has rapidly boosted residential buildings to cope with substantial housing shortages. However, as buildings have been aging simultaneously, the performance of a large number of residential buildings has deteriorated. A government plan to upgrade poor housing performance through renovation is being adopted. However, the difficulty of accurate construction cost prediction in the early stages has a negative effect on the renovation process. Specifically, the relationship between renovation design elements and construction work items has not been clearly revealed. Thus, construction experts use premature intuition to predict renovation costs, giving rise to a large difference between planned and actual costs. In this study, a new approach links the renovation design elements with construction work items. Specifically, it effectively quantifies design factors and applies data-driven estimation using the simulation-based deep learning (DL) approach. This research contributes the following. First, it improves the reliability of cost prediction for a data-scarce renovation project. Moreover, applying this novel approach greatly reduces the time and effort required for cost estimation. Second, several design alternatives were effectively examined in an earlier stage of construction, leading to prompt decision-making for homeowners. Third, rapid decision-making can provide a more sustainable living environment for residents. With this novel approach, stakeholders can avoid a prolonged economic evaluation by selecting a better design alternative, and thus can maintain their property holdings in a smarter way. Full article
(This article belongs to the Special Issue Industrialized and Automated Construction in the Context of Sustainability)
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21 pages, 1687 KiB  
Article
Identification and Prioritization of Critical Success Factors for Off-Site Construction Using ISM and MICMAC Analysis
by Seoyoung Jung, Seulki Lee and Jungho Yu
Sustainability 2021, 13(16), 8911; https://0-doi-org.brum.beds.ac.uk/10.3390/su13168911 - 09 Aug 2021
Cited by 13 | Viewed by 2826
Abstract
Many studies have been conducted to define the critical success factors (CSFs) for off-site construction (OSC) activation, but there has been a lack of identification of the relationship with the identified CSFs. However, it is necessary to clearly identify the hierarchy and relationships [...] Read more.
Many studies have been conducted to define the critical success factors (CSFs) for off-site construction (OSC) activation, but there has been a lack of identification of the relationship with the identified CSFs. However, it is necessary to clearly identify the hierarchy and relationships with the success factors in order to develop specific strategies for OSC activation. This work presents a study that was conducted to identify the CSFs for OSCs and establish the relationships of the identified CSFs for OSC. First, 20 CSFs for OSCs were identified through prior study reviews related to CSFs for OSC. Next, the interpretive structural modeling (ISM), which has advantages in developing an understanding of complex relationships, was leveraged in order to analyze the relationships between 20 CSFs for OSC to derive a hierarchical model consisting of seven levels. The CSFs for OSC were classified into four groups using MICMAC analysis, which is useful for classifying factors by the strength of the relationship with factors based on driving power and dependence power. This proposed model can be used as a basis for developing management measures for OSC project success. Full article
(This article belongs to the Special Issue Industrialized and Automated Construction in the Context of Sustainability)
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22 pages, 2243 KiB  
Article
Optimization of Prefabricated Components in Housing Modular Construction
by Sunghoon Nam, Jongsik Yoon, Kyungrai Kim and Byungjoo Choi
Sustainability 2020, 12(24), 10269; https://0-doi-org.brum.beds.ac.uk/10.3390/su122410269 - 09 Dec 2020
Cited by 18 | Viewed by 4068
Abstract
In modular construction—a type of industrialized construction—production planning is very important, as it is closely related to the project’s duration, quality, and sustainability. The constraints (production area, delivery due date) often differ for each project, yet production planning in modular construction has failed [...] Read more.
In modular construction—a type of industrialized construction—production planning is very important, as it is closely related to the project’s duration, quality, and sustainability. The constraints (production area, delivery due date) often differ for each project, yet production planning in modular construction has failed to change with the project characteristics. As a result, bottlenecks and construction delays are common problems seen in modular construction, which, in turn, decreases the production ratio, causing the production to be inefficient. To this end, this paper applied a prefabricated component in the modular production process. The paper developed a process analysis model considering constraint factors (production period, production area) to derive the optimal configuration of the prefabricated components in various alternatives. The developed analysis model was then applied to a virtual case to analyze the productivity improvement and select the optimal process. The optimal production process was derived by simulating the possible production planning within a limited production area and production timeline. The result of a simulation indicates that the production period has been halved by optimizing the process. Furthermore, by applying prefabricated components, the production efficiency was further increased because the existing linear production process’s bottleneck disappeared. The model is deemed to have the potential to optimize various production methods across production facilities or modular factories that simultaneously perform multiple projects. Full article
(This article belongs to the Special Issue Industrialized and Automated Construction in the Context of Sustainability)
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17 pages, 10069 KiB  
Article
Automatic Space Analysis Using Laser Scanning and a 3D Grid: Applications to Industrial Plant Facilities
by Donghyun Kim, Soonwook Kwon, Chung-Suk Cho, Borja García de Soto and Daeyoon Moon
Sustainability 2020, 12(21), 9087; https://0-doi-org.brum.beds.ac.uk/10.3390/su12219087 - 31 Oct 2020
Cited by 4 | Viewed by 2335
Abstract
While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, [...] Read more.
While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, etc.,). Furthermore, problems during installation, operation, and maintenance stages caused by inconsistencies between floor plans and actual layout are on the rise. Although some of these conflicts can be addressed through clash detection using BIM (building information modeling), quality BIM models are scarce, especially for existing industrial plants. This study proposes a way to address the complexities caused by changes during plant construction and securing space for the installation of equipment during the construction and lifecycle of built facilities. 3D cloud point data of space and equipment were collected using 3D laser scanning to conduct space matching. In processing the space matching, data were simplified by applying the 3D grid and by comparing the data, easier identification of the space for target equipment was accomplished. This study also proposed a pre-processing method based on sub-sampling that optimizes the point cloud data and verifies the processing speed and accuracy. Lastly, it finds free space for various equipment layouts required in industrial plant projects by space analysis, proposed algorithms, and processes for obtaining the coordinates of valid space for equipment arrangement. The proposed method of this study is expected to help solve the problems derived from arrangement and installation of new equipment in a complex plant site. Full article
(This article belongs to the Special Issue Industrialized and Automated Construction in the Context of Sustainability)
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