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

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

Deadline for manuscript submissions: 21 April 2025 | Viewed by 13113

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Special Issue Editors

Prof. Dr. Jaewook Jeong

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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

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Published Papers (10 papers)

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Research

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29 pages, 31020 KiB

Open AccessArticle

Vision-Based Construction Safety Monitoring Utilizing Temporal Analysis to Reduce False Alarms

by Syed Farhan Alam Zaidi, Jaehun Yang, Muhammad Sibtain Abbas, Rahat Hussain, Doyeop Lee and Chansik Park

Buildings 2024, 14(6), 1878; https://doi.org/10.3390/buildings14061878 - 20 Jun 2024

Cited by 2 | Viewed by 1112

Abstract

Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system’s performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments. 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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31 pages, 16222 KiB

Open AccessArticle

Development of a Site Information Classification Model and a Similar-Site Accident Retrieval Model for Construction Using the KLUE-BERT Model

by Seung-Hyeon Shin, Jeong-Hun Won, Hyeon-Ji Jeong and Min-Guk Kang

Buildings 2024, 14(6), 1797; https://doi.org/10.3390/buildings14061797 - 13 Jun 2024

Viewed by 640

Abstract

Before starting any construction work, providing workers with awareness about past similar accident cases is effective in preventing mishaps. Based on construction accident reports, this study developed two models to identify past accidents at sites with similar site information. The site information includes 16 parameters, such as type of work, type of accident, the work in which the accident occurred, weather conditions, contract conditions, type of work, etc. The first model, the site information classification model, uses named entity recognition tasks to classify site information, which is extracted from accident reports. The second model, the similar-site accident retrieval model, which finds the most similar accidents that occurred in the past from input site information, uses a semantic textual similarity task to match the classified information with it. A total of 17,707 accident reports from South Korean construction sites were found; these models were trained to use Korean Language Understanding Evaluation–Bidirectional Encoder Representations from Transformers (KLUE-BERT) for processing. The first model achieved an average accuracy of 0.928, and the second model was precisely matched, with a mean cosine similarity score exceeding 0.90. These models could identify and provide workers with similar past accidents, enabling proactive safety measures, such as site-specific hazard identification and worker education, thereby allowing recognition of construction safety risks before starting work. By integrating site information with historical data, the models offer an effective approach to improving construction safety. 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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22 pages, 5028 KiB

Open AccessArticle

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

by Yunfei Hou and Qi Zhao

Buildings 2024, 14(6), 1719; https://doi.org/10.3390/buildings14061719 - 8 Jun 2024

Viewed by 890

Abstract

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers. 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, 8845 KiB

Open AccessArticle

Experimental Study on Using Synthetic Images as a Portion of Training Dataset for Object Recognition in Construction Site

by Jaemin Kim, Ingook Wang and Jungho Yu

Buildings 2024, 14(5), 1454; https://doi.org/10.3390/buildings14051454 - 17 May 2024

Viewed by 954

Abstract

The application of Artificial Intelligence (AI) across various industries necessitates the acquisition of relevant environmental data and the implementation of AI recognition learning based on this data. However, the data available in real-world environments are limited and difficult to obtain. Construction sites represent dynamic and hazardous environments with a significant workforce, making data acquisition challenging and labor-intensive. To address these issues, this experimental study explored the potential of generating synthetic data to overcome the challenges of obtaining data from hazardous construction sites. Additionally, this research investigated the feasibility of hybrid dataset in securing construction-site data by creating synthetic data for scaffolding, which has a high incidence of falls but low object recognition rates due to its linear object characteristics. We generated a dataset by superimposing scaffolding objects, from which the backgrounds were removed, onto various construction site background images. Using this dataset, we produced a hybrid dataset to assess the feasibility of synthetic data for construction sites and to evaluate improvements in object recognition performance. By finding the optimal composition ratio with real data and conducting model training, the highest accuracy was achieved at an 8:2 ratio, with a construction object recognition accuracy of 0.886. Therefore, this study aims to reduce the risk and labor associated with direct data collection at construction sites through a hybrid dataset, achieving data generation at a low cost and high efficiency. By generating synthetic data to find the optimal ratio and constructing a hybrid dataset, this research demonstrates the potential to address the problems of data scarcity and data quality on construction sites. The improvement in recognition accuracy of the construction safety management system is anticipated, suggesting that the creation of synthetic data for constructing a hybrid dataset can reduce construction safety-accident issues. 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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20 pages, 1282 KiB

Open AccessArticle

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 1081

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1399

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 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

Open AccessArticle

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 1374

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’ 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

Open AccessArticle

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

Cited by 2 | Viewed by 1674

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 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

Open AccessArticle

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 1672

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 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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Review

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20 pages, 3474 KiB

Open AccessReview

Systematic Review of Quantitative Risk Quantification Methods in Construction Accidents

by Louis Kumi, Jaewook Jeong and Jaemin Jeong

Buildings 2024, 14(10), 3306; https://doi.org/10.3390/buildings14103306 - 19 Oct 2024

Viewed by 1162

Abstract

Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies. 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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