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Smart and Proactive Construction Safety Combined with AI, IoT, and...
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Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4072

Special Issue Editors

Prof. Dr. Jaewook Jeong

E-Mail Website
Guest Editor
Department of Safety Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul, Republic of Korea
Interests: construction safety; design for safety; risk assessment; off-site construction; prefabrication; smart construction; accident investigation; construction engineering and management
Special Issues, Collections and Topics in MDPI journals
Dr. Jaehyun Lee

E-Mail Website
Guest Editor
Department of Architecture, Honam University, Gwangju 62399, Republic of Korea
Interests: construction safety; design for safety; accident probability; accident loss cost; smart safety management; material engineering and management
Special Issues, Collections and Topics in MDPI journals
Dr. Jaemin Jeong

E-Mail Website
Guest Editor
Department of Safety Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul 01811, Republic of Korea
Interests: construction safety analysis; off-site construction; prefabrication; accident investigation; multi-criteria analysis; thermal comfort; optimization; productivity analysis

Special Issue Information

Dear Colleagues,

We are delighted to extend an invitation for your contribution to our Special Issue titled "Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data". Despite numerous research efforts focused on occupational health and safety, the construction industry continues to exhibit poor safety levels globally, particularly in the analysis of accident root causes and safety management during pre-construction phases.

Our intention is to investigate the root causes of accidents and explore safety management techniques during the pre-construction phase, with a special emphasis on AI, IoT, big data, and other advanced safety technologies. We believe that a comprehensive approach to safety is essential from a life cycle perspective.

This Special Issue aims to consolidate cutting-edge advancements in construction safety and management, encompassing various aspects including systems, policies, organizational structures, and technical innovations. We welcome research papers that contribute to the development of construction safety and management, addressing topics including, but not limited to:

  • Construction safety merged with new technologies (BIM, AI, IoT, big data);
  • Construction safety policy and regulation;
  • Design for safety/prevention through design;
  • Construction safety management;
  • Accident analysis and investigation;
  • Digital and smart technology for safety;
  • Off-site construction for safety;
  • Worker behavior and safety;
  • Risk assessment;
  • Other topics on health and safety in construction.

Your valuable contribution to this endeavor would significantly enhance our understanding of construction safety and contribute to the improvement of safety practices within the industry. We look forward to receiving your research submissions.

Prof. Dr. Jaewook Jeong
Dr. Jaehyun Lee
Dr. Jaemin Jeong
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

  • construction safety
  • design for safety
  • safety management
  • accident analysis
  • digital and smart technology
  • worker behavior
  • risk assessment
  • occupational health and safety

Published Papers (5 papers)

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Research

20 pages, 1282 KiB  
Article
Quantitative Accident Risk Estimation for Infrastructure Facilities Based on Accident Case Analysis
by Jeongung Lee and Jaewook Jeong
Buildings 2024, 14(5), 1283; https://doi.org/10.3390/buildings14051283 - 1 May 2024
Viewed by 382
Abstract
The construction industry records higher accident rates than other industries, and thus, risk estimation is necessary to manage accident rates. Risk levels differ based on facility type and construction project size. In this sense, this study aims to calculate the quantitative accident risk [...] Read more.
The construction industry records higher accident rates than other industries, and thus, risk estimation is necessary to manage accident rates. Risk levels differ based on facility type and construction project size. In this sense, this study aims to calculate the quantitative accident risk level according to the construction project size per infrastructure facility type. To this end, the following five-step risk estimation was performed: (1) data collection and classification; (2) calculation of fatality rate based on construction cost; (3) calculation of fatal construction probability by construction cost classification; (4) reclassification of construction cost considering fatal construction probability; and (5) calculation of risk level by facility type and construction cost classification. As a result, the fatality rate per facility type was the highest in ‘Dam’ at 0.01024 (person/USD million). Additionally, the risk level according to the construction project size per facility type was the highest for ‘Dam’ (0.00403 person/USD million) for a construction of less than USD 0.77 million. The risk level presented in this study can be utilized as basic data in the design stage for safety management. Our results also indicate the necessity of preparing a separate construction cost classification for safety management. Full article
(This article belongs to the Special Issue Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data)
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18 pages, 8220 KiB  
Article
Strategies for Imputing Missing Values and Removing Outliers in the Dataset for Machine Learning-Based Construction Cost Prediction
by Haneul Lee and Seokheon Yun
Buildings 2024, 14(4), 933; https://doi.org/10.3390/buildings14040933 - 28 Mar 2024
Viewed by 471
Abstract
Accurately predicting construction costs during the initial planning stages is crucial for the successful completion of construction projects. Recent advancements have introduced various machine learning-based methods to enhance cost estimation precision. However, the accumulation of authentic construction cost data is not straightforward, and [...] Read more.
Accurately predicting construction costs during the initial planning stages is crucial for the successful completion of construction projects. Recent advancements have introduced various machine learning-based methods to enhance cost estimation precision. However, the accumulation of authentic construction cost data is not straightforward, and existing datasets frequently exhibit a notable presence of missing values, posing challenges to precise cost predictions. This study aims to analyze diverse substitution methods for addressing missing values in construction cost data. Additionally, it seeks to evaluate the performance of machine learning models in cost prediction through the removal of conditional outliers. The primary goal is to identify and propose optimal strategies for handling missing value in construction cost records, ultimately improving the reliability of cost predictions. According to the analysis results, among single imputation methods, median imputation emerges as the most suitable, while among multiple imputation methods, lasso regression imputation produces the most superior outcomes. This research contributes to enhancing the trustworthiness of construction cost predictions by presenting a pragmatic approach to managing missing data in construction cost performance records, thereby facilitating more precise project planning and execution. Full article
(This article belongs to the Special Issue Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data)
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24 pages, 1532 KiB  
Article
Nature of Occupational Incidents among Roofing Contractors: A Data Mining Approach
by Ikechukwu Sylvester Onuchukwu, Pouya Gholizadeh, Gentian Liko and Behzad Esmaeili
Buildings 2024, 14(3), 595; https://doi.org/10.3390/buildings14030595 - 23 Feb 2024
Viewed by 608
Abstract
Given that roofing contractors in the construction industry have the highest fatality rate among specialty contractors, understanding the root cause of incidents among roofers is critical for improving safety outcomes. This study applied frequency analysis and decision tree data-mining techniques to analyze roofers’ [...] Read more.
Given that roofing contractors in the construction industry have the highest fatality rate among specialty contractors, understanding the root cause of incidents among roofers is critical for improving safety outcomes. This study applied frequency analysis and decision tree data-mining techniques to analyze roofers’ fatal and non-fatal accident reports. The frequency analysis yielded insights into the leading cause of accidents, with fall to a lower level (83%) being the highest, followed by incidence sources relating to structures and surfaces (56%). The most common injuries experienced by roofing contractors were fractures (49%) and concussions (15%), especially for events occurring in residential buildings, maintenance and repair works, small projects (i.e., $50,000 or less), and on Mondays. According to the decision tree analysis, the most important factor for determining the nature of the injury is the nonfragile injured body part, followed by injury caused by coating works. The decision tree also produced decision rules that provide an easy interpretation of the underlying association between the factors leading to incidents. The decision tree models developed in this study can be used to predict the nature of potential injuries for strategically selecting the most effective injury-prevention strategies. Full article
(This article belongs to the Special Issue Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data)
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14 pages, 5761 KiB  
Article
Evaluation of Mobile Risk Perception Training System for Improving the Safety Awareness of Construction Workers
by Minji Kim, Dongmyung Jo and Jaewook Jeong
Buildings 2023, 13(12), 3024; https://doi.org/10.3390/buildings13123024 - 5 Dec 2023
Viewed by 1120
Abstract
Toolbox Meeting (TBM) activities conducted before work at construction sites are representative activities with characteristics such as work sharing and improving safety awareness. However, there is a limitation to the conventional TBM approach as it proceeds only formally and is not systematic because [...] Read more.
Toolbox Meeting (TBM) activities conducted before work at construction sites are representative activities with characteristics such as work sharing and improving safety awareness. However, there is a limitation to the conventional TBM approach as it proceeds only formally and is not systematic because it proceeds without the manager’s prior preparation. Therefore, in this study, TBM was conducted using a mobile app by supplementing the limitation of conventional TBMs, and we examined whether mobile TBM is more effective in improving the safety awareness of construction site workers. A survey of 400 people was conducted at two sites implementing existing TBM or mobile TBM. This study included survey development, survey target selection, and statistical analysis. The analysis revealed three main results. First, mobile TBM was more efficient. Second, workers at mobile TBM application sites (M = 4.24) were more positive and satisfied with daily safety activities than those using existing TBMs (M = 3.95). Finally, the impact of TBM education and worker safety awareness was higher in sites using mobile TBM (M = 4.14) than those using existing TBM (M = 3.94). This study provides valuable evidence for construction site safety management decision-makers considering the adoption of smart safety management tools such as mobile TBMs. Full article
(This article belongs to the Special Issue Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data)
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15 pages, 5842 KiB  
Article
A Study of the Effects of Handedness on Portable Angle Grinder Risk
by Sung Bum Choi and Jong Yil Park
Buildings 2023, 13(11), 2811; https://doi.org/10.3390/buildings13112811 - 9 Nov 2023
Viewed by 1001
Abstract
This study investigates the underlying cause of occupational hazards for left-handed construction workers when using portable angle grinders on construction sites. The study was conducted through a survey of 42 participants in South Korean construction companies to gather information on their tasks involving [...] Read more.
This study investigates the underlying cause of occupational hazards for left-handed construction workers when using portable angle grinders on construction sites. The study was conducted through a survey of 42 participants in South Korean construction companies to gather information on their tasks involving portable angle grinders. The survey covered handle preferences, grip strength assessment, and work posture observations. Furthermore, a qualitative comparison of the work risk for left-handed and right-handed construction workers using a handheld angle grinder for cutting was conducted. Results showed that the grip strength of a left-handed worker’s dominant and non-dominant hands did not significantly vary, implying that grip strength does not significantly affect their work performance. However, left-handed workers exhibited a higher likelihood of accidents due to poor work postures. Improvements to workplace safety by ensuring the use of ambidextrous tools and promoting tailored safety measures and training for left-handed workers were recommended. Full article
(This article belongs to the Special Issue Smart and Proactive Construction Safety Combined with AI, IoT, and Big Data)
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