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Sustainable Construction Management and Computer Simulation

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 21308

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Special Issue Editors

Prof. Dr. JeongWook Son

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Guest Editor

Department of Architectural and Urban Systems Engineering, College of Engineering, Ewha Womans University, Seoul 120-750, Republic of Korea
Interests: smart construction technologies; integrated project delivery; off-site construction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Byungjoo Choi

E-Mail Website
Guest Editor

Department of Architecture, Ajou University, Gyeonggi-do 16499, Korea
Interests: wearable sensors based construction safety and health management; human behaviors in construction projects; personalized smart building system for improving occupants’ well-being

Prof. Dr. Sungjoo Hwang

E-Mail Website
Guest Editor

Department of Architectural and Urban Systems Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
Interests: smart built environment; infrastructure management; urban development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable construction is one of the most critical issues in the construction industry around the world and continuous efforts to enhance the sustainability of construction have been made in both the academic and practical fields. A range of approaches have been explored in the construction supply chain to achieve the goals of sustainable construction, which reduce the industry’s impact on the environment through the use of renewable and recyclable resources, reducing energy consumption and waste, and creating a healthy, environmentally friendly system. Similarly, there are many studies that focus on the implementation of sustainable construction in the construction management field, including life-cycle assessment, lean construction, sustainable supply chain management, sustainable construction methods, and sustainable construction site management. Recently, sustainable construction management has extended its scope to include to the selection of sustainable delivery methods, pre-construction for sustainability, computational design and engineering for sustainability, and off-site construction. Moreover, with the development of computational research methods and emerging technologies, investigations in this field are making remarkable advancements.

The purpose of the Special Issue on “Sustainable Construction Management and Computer Simulation” is to publish state-of-the-art computational research trends and results in the context of sustainable construction management. The Special Issue focuses on, but is not limited to, computer simulations such as discrete-event simulations, system dynamics, agent-based simulations, and hybrid simulations used in computational research methods. Research focused on emerging technologies such as data mining, artificial intelligence, image processing, and big data to enhance performance in combination with simulations is also welcome.

Topics
- Sustainable construction management
- Life-cycle assessment
- Lean construction
- Off-site construction
- Prefabrication
- Sustainable supply chain management
- Sustainable construction method
- Sustainable construction site management
Computational research methods
- Computer simulation
- Discrete-event simulation
- System dynamics
- Agent-based simulation
- Hybrid simulation
- Data mining
- Artificial intelligence
- Image processing
- Big data

Prof. Dr. JeongWook Son
Prof. Dr. Byungjoo Choi
Prof. Dr. Sungjoo Hwang
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 management
life-cycle assessment
lean construction
off-site construction
prefabrication
sustainable supply chain management
sustainable construction method
sustainable construction site management
computer simulation
discrete-event simulation
system dynamics
agent-based simulation
hybrid simulation
data mining
artificial intelligence
image processing
big data

Published Papers (6 papers)

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Research

17 pages, 1862 KiB

Open AccessArticle

Improving the Model for Estimating the Number of Construction Workers for Apartment Construction

by Hyeongjun Mun, Jaewook Jeong and Jaemin Jeong

Sustainability 2023, 15(9), 7150; https://doi.org/10.3390/su15097150 - 25 Apr 2023

Viewed by 1319

Abstract

Because the construction industry is labor-intensive, predicting the number of workers is important for estimating various factors that influence construction, such as the construction worker fatality rate and construction financing plan. In South Korea, the number of full-time workers is estimated based on the total construction cost; however, this estimation method does not reflect the characteristics of specific construction types. This study presents a simple model that uses real data to predict the number of construction workers and calculates correction factors in two ways to improve reliability. This study involved three steps: (1) collecting data, (2) calculating and validating the estimated labor rate, and (3) calculating correction factors. The model predicted the number of workers with an average error rate of 7.60% without correction factors. To improve reliability, this research suggests two-way correction factors, and the results show that correction factor one reduces the average error rate to 0.06% and correction factor two reduces the average error rate to 0.00%. The proposed model can be used for estimating project costs and predicting construction worker fatalities for a project. Full article

(This article belongs to the Special Issue Sustainable Construction Management and Computer Simulation)

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14 pages, 4345 KiB

Open AccessArticle

Transfer and Unsupervised Learning: An Integrated Approach to Concrete Crack Image Analysis

by Luka Gradišar and Matevž Dolenc

Sustainability 2023, 15(4), 3653; https://doi.org/10.3390/su15043653 - 16 Feb 2023

Cited by 2 | Viewed by 1581

Abstract

The detection of cracks in concrete structures is crucial for the assessment of their structural integrity and safety. To this end, detection with deep neural convolutional networks has been extensively researched in recent years. Despite their success, these methods are limited in classifying concrete as cracked or non-cracked and disregard other characteristics, such as the severity of the cracks. Furthermore, the classification process can be affected by various sources of interference and noise in the images. In this paper, an integrated methodology for analysing concrete crack images is proposed using transfer and unsupervised learning. The method extracts image features using pre-trained networks and groups them based on similarity using hierarchical clustering. Three pre-trained networks are used for this purpose, with Inception v3 performing the best. The clustering results show the ability to divide images into different clusters based on image characteristics. In this way, various clusters are identified, such as clusters containing images of obstruction, background debris, edges, surface roughness, as well as cracked and uncracked concrete. In addition, dimensionality reduction is used to further separate and visualise the data, making it easier to analyse clustering results and identify misclassified images. This revealed several mislabelled images in the dataset used in this study. Additionally, a correlation was found between the principal components and the severity of cracks and surface imperfections. The results of this study demonstrate the potential of unsupervised learning for analysing concrete crack image data to distinguish between noisy images and the severity of cracks, which can provide valuable information for building more accurate predictive models. Full article

(This article belongs to the Special Issue Sustainable Construction Management and Computer Simulation)

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16 pages, 2743 KiB

Open AccessArticle

Evaluation of Accident Risk Level Based on Construction Cost, Size and Facility Type

by Saemi Bang, Jaewook Jeong, Jaehyun Lee, Jaemin Jeong and Jayho Soh

Sustainability 2023, 15(2), 1565; https://doi.org/10.3390/su15021565 - 13 Jan 2023

Cited by 4 | Viewed by 1686

Abstract

Compared with other industries such as manufacturing, the construction industry has a higher danger of fatalities. In Korea, the risk level in the construction industry is managed using the fatality rate per 10,000 construction workers. However, this statistic is lacking in determining the exact risk level because it does not consider the exact number of workers and fails to reflect the specific characteristics of the construction industry. In this study, the fatality rate is deduced by considering the facility type and the project size based on total cost. From the results obtained, considering the facility type, “Assembly” is seen to be the most dangerous facility type. Considering the project size based on total cost, “Less than 0.008 billion dollars” is the most dangerous construction scale. Considering both the facility type and the project size based on total cost, it was confirmed that the overall fatality rate could exceed the fatality rate respective to each facility type and project size. Using the proposed method, it is possible to determine the quantitative risk level considering specific characteristics of the construction industry. Full article

(This article belongs to the Special Issue Sustainable Construction Management and Computer Simulation)

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25 pages, 990 KiB

Open AccessArticle

Design for Manufacturing and Assembly (DfMA) Checklists for Off-Site Construction (OSC) Projects

by Seoyoung Jung and Jungho Yu

Sustainability 2022, 14(19), 11988; https://doi.org/10.3390/su141911988 - 22 Sep 2022

Cited by 5 | Viewed by 3162

Abstract

Off-Site Construction (OSC), which has the advantage of improving construction productivity, is being spotlighted as a solution to the limitations of conventional construction production methods. Despite the need for, and various advantages of, the introduction and utilization of OSC, however, several issues remain, such as design errors and reduction in design completeness, due to the lack of experience and expertise of project participants, as well as improper consideration of production environment and technical constraints. To resolve these issues, it is necessary to develop an optimal design plan that conforms to the OSC manufacturing environment and manufacturing efficiency; thus, there have been ongoing efforts in the construction industry to introduce Design for Manufacturing and Assembly (DfMA), to derive the optimal design plans for OSC projects. Some studies related to the application of DfMA to OSC have been conducted, however they neglected to present a checklist for reviewing the optimality of OSC design plans. This study has therefore developed an OSC–DfMA checklist, to review the optimality of design plans for OSC projects, by listing optimal design goals for OSC projects, the OSC process, and DfMA principles. This study utilized the systematic literature review, structured interview, and content validity analysis methods to develop the OSC–DfMA checklist presented herein. The developed OSC–DfMA checklist will be applicable to reviewing the optimality of the OSC design plans. Full article

(This article belongs to the Special Issue Sustainable Construction Management and Computer Simulation)

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25 pages, 5612 KiB

Open AccessArticle

Crack Detection in Concrete Structures Using Deep Learning

by Vaughn Peter Golding, Zahra Gharineiat, Hafiz Suliman Munawar and Fahim Ullah

Sustainability 2022, 14(13), 8117; https://doi.org/10.3390/su14138117 - 2 Jul 2022

Cited by 38 | Viewed by 10000

Abstract

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information. Full article

(This article belongs to the Special Issue Sustainable Construction Management and Computer Simulation)

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19 pages, 4705 KiB

Open AccessArticle

Analysis on Dynamic Evolution of the Cost Risk of Prefabricated Building Based on DBN

by Mengwei Ye, Junwu Wang, Xiang Si, Shiman Zhao and Qiyun Huang

Sustainability 2022, 14(3), 1864; https://doi.org/10.3390/su14031864 - 6 Feb 2022

Cited by 15 | Viewed by 2613

Abstract

Prefabricated building constitutes the development trend of the construction industry in the future. However, many uncertainties in the construction process will surely lead to a higher cost. Therefore, it is necessary to study the cost risk evolution and transfer mechanism in the implementation process of this project. A dynamic evolution model for the cost risk of prefabricated buildings has been established in this paper. First of all, a matrix for cost risk of prefabricated buildings was established based on the WSR (Wuli-Shili-Renli) model, and all risk factors in the implementation stage were classified in accordance with the WSR principle. Second, a DBN-based dynamic evolution model was established based on the risk matrix, and the structure and node parameters of the Dynamic Bayesian Network were determined with the aid of the K2 structure learning algorithm and parameter learning method. In view of the probability change process of risks over time, the dynamic evolution path of risks was predicted in different cases through causal reasoning and diagnostic reasoning. Eventually, the model was applied into construction projects. The research results show that: because prefabricated components need to be made by prefabricated component factories, the management systems of prefabricated component factories are usually not perfect, and the probability of management risks is higher. The occurrence of management risks not only has an impact on other risks at the current time node, but also causes other risks to occur in the subsequent transportation and construction phases at the next moment, which eventually leads to the occurrence of risk events. Full article

(This article belongs to the Special Issue Sustainable Construction Management and Computer Simulation)

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