Special Issue "Sustainability and Industry 4.0 in Civil and Infrastructure Engineering"

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

Deadline for manuscript submissions: 10 February 2022.

Special Issue Editors

Prof. Dr. Sanghyo Lee
E-Mail Website
Guest Editor
Division of Smart Convergence Engineering, Hanyang University ERICA, Ansan, Korea
Interests: artificial intelligence; drone; construction management; BIM; financial model for green buildings; advanced construction materials
Prof. Dr. Sungkon Moon
E-Mail Website
Guest Editor
Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, Australia
Interests: construction engineering; automation and control engineering; logistics and supply chain management

Special Issue Information

Dear Colleagues,

The scope of this Special Issue stems covers the advent of Industry 4.0 and current sustainability issues. New technologies are playing a pivotal role in improving the level of being sustainable, although they keep bringing up unprecedented problems and challenges. This Special Issue has the final goal of achieving advanced sustainability, with the advent of Industry 4.0. The field of civil and infrastructure engineering is also experiencing this transit of Industry 4.0, where we need further consideration of how this change and sustainability matters can be harmonized.

The Special Issue will address practical and theoretical issues associated with Sustainable Industry 4.0 and its adoption in civil and infrastructure engineering. Topics of interest include, but are not limited to the following:

  • Sustainability and new technology
  • Innovation in civil and infrastructure engineering
  • Construction information technology
  • Technology adoption and evaluation
  • Advanced and new materials
  • Project management and construction supply chain
  • Practical issues in civil and infrastructure engineering
  • Emerging issues in civil and infrastructure engineering
  • Building maintenance
  • Sustainability in business
  • Risk management

Prof. Dr. Sanghyo Lee
Prof. Dr. Sungkon Moon
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 papers will be 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 1900 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 development
  • Industry 4.0
  • Innovative construction
  • Construction technologies
  • New materials for construction
  • Infrastructure management

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?
Sustainability 2021, 13(11), 5788; https://doi.org/10.3390/su13115788 - 21 May 2021
Cited by 2 | Viewed by 687
Abstract
Artificial intelligence in the fourth industrial revolution is beginning to live up to its promises of delivering real value necessitated by the availability of relevant data, computational ability, and algorithms. Therefore, this study sought to investigate the influence of artificial intelligence on the [...] Read more.
Artificial intelligence in the fourth industrial revolution is beginning to live up to its promises of delivering real value necessitated by the availability of relevant data, computational ability, and algorithms. Therefore, this study sought to investigate the influence of artificial intelligence on the attainment of Sustainable Development Goals with a direct focus on poverty reduction, goal one, industry, innovation, and infrastructure development goal 9, in emerging economies. Using content analysis, the result pointed to the fact that artificial intelligence has a strong influence on the attainment of Sustainable Development Goals particularly on poverty reduction, improvement of the certainty and reliability of infrastructure like transport making economic growth and development possible in emerging economies. The results revealed that Artificial intelligence is making poverty reduction possible through improving the collection of poverty-related data through poverty maps, revolutionizing agriculture education and the finance sector through financial inclusion. The study also discovered that AI is also assisting a lot in education, and the financial sector allowing the previously excluded individuals to be able to participate in the mainstream economy. Therefore, it is important that governments in emerging economies need to invest more in the use of AI and increase the research related to it so that the Sustainable Development Goals (SDGs) related to innovation, infrastructure development, poverty reduction are attained. Full article
Show Figures

Figure 1

Article
Quantitative Analysis of Waiting Length and Waiting Time for Frame Construction Work Activities Using a Queue Model; Focusing on Korean Apartment Construction
Sustainability 2021, 13(7), 3778; https://doi.org/10.3390/su13073778 - 29 Mar 2021
Viewed by 456
Abstract
The frame construction of an apartment complex that consists of multiple buildings encounters various uncertainties, owing to the complex relationships between units of work. Currently, the period of such a construction is calculated based on the number of floors of the highest building [...] Read more.
The frame construction of an apartment complex that consists of multiple buildings encounters various uncertainties, owing to the complex relationships between units of work. Currently, the period of such a construction is calculated based on the number of floors of the highest building in the complex. This study quantitatively analyzes an apartment frame construction period using a queue model and evaluates the validity of the estimated period. In this regard, a methodology is proposed for analyzing the construction period by applying the concept of a customer and a server. A case study on the duration of an apartment frame construction period is conducted with the Korea Land and Housing Corporation, which has supplied the largest number of apartments in South Korea. It was found that the stable state of a queue system was observed when the rate of server utilization was applied to the basement and above-ground floors. However, a stable state was not reached on the ground floor. This study includes non-working days in its calculation and quantitatively analyzes uncertainty factors during construction. Therefore, the findings can be practically utilized to quantitatively plan the durations of work units in an apartment frame construction. Full article
Show Figures

Figure 1

Article
Total Repair Cost Simulation Considering Multiple Probabilistic Measures and Service Life
Sustainability 2021, 13(4), 2350; https://doi.org/10.3390/su13042350 - 22 Feb 2021
Viewed by 412
Abstract
In this study, the total maintenance cost for public houses in South Korea was analyzed, and the effect of each repair process on the total maintenance cost was evaluated with probabilistic and deterministic methods. In the probabilistic method, quality of repair materials and [...] Read more.
In this study, the total maintenance cost for public houses in South Korea was analyzed, and the effect of each repair process on the total maintenance cost was evaluated with probabilistic and deterministic methods. In the probabilistic method, quality of repair materials and construction skills were considered in the variability of extended service life through repair, while the deterministic method considered it by simple summation of repair step. The repair cost was analyzed considering the coefficient of variation (COV) of extended service life, so the reasonable total maintenance cost was able to be evaluated. Since the results through the probabilistic method provided a continuous cost line, a reasonable repair strategy was carried out by simply changing the intended service life of the structure. The repair cost was additionally analyzed with constant COV (0.15) of each repair process for considering various situations. The analysis results with a COV of 0.15 exhibited a slightly higher maintenance cost than those with current COV. The total maintenance costs can be adjusted if the initial repair timing is extended to the largest possible extent for the highest-repair-cost process since the total repair cost is dominated by the process with the highest repair cost. Full article
Show Figures

Figure 1

Article
MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network
Sustainability 2020, 12(22), 9785; https://doi.org/10.3390/su12229785 - 23 Nov 2020
Cited by 2 | Viewed by 1033
Abstract
Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address [...] Read more.
Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades. Full article
Show Figures

Figure 1

Article
Analysis of Major Environmental Impact Categories of Road Construction Materials
Sustainability 2020, 12(17), 6951; https://doi.org/10.3390/su12176951 - 02 Sep 2020
Cited by 2 | Viewed by 967
Abstract
To address the environmental problems associated with construction materials, the construction industry has made considerable efforts to reduce carbon emissions. However, construction materials cause several other environmental problems in addition to carbon emissions and thus, a comprehensive analysis of environmental impact categories is [...] Read more.
To address the environmental problems associated with construction materials, the construction industry has made considerable efforts to reduce carbon emissions. However, construction materials cause several other environmental problems in addition to carbon emissions and thus, a comprehensive analysis of environmental impact categories is required. This study aims to determine the major environmental impact categories for each construction material in production stage using the life cycle assessment (LCA) technique on road projects. Through the review of life cycle impact assessment (LCIA) methodologies, the abiotic depletion potential (ADP), ozone depletion potential, photochemical oxidant creation potential, acidification potential, eutrophication potential, eco-toxicity potential, human toxicity potential, as well as the global warming potential (GWP) were defined as impact categories. To define the impact categories for road construction materials, major environmental pollutants were analyzed for a number of road projects, and impact categories for 13 major construction materials were selected as mandatory impact categories. These materials contributed more than 80% to the impact categories from an LCA perspective. The impact categories to which each material contributed more than 99% were proposed as specialization impact categories to provide basic data for use in the LCIA of future road projects. Full article
Show Figures

Figure 1

Back to TopTop