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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: closed (31 March 2026) | Viewed by 23541

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Special Issue Editors

Dr. Krishna Kisi

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Guest Editor

Department of Engineering Technology, Texas State University, San Marcos, TX 78666, USA
Interests: construction labor productivity; human performance; ergonomics; construction safety; stem education; technology applications; workforce development and training; dispute resolution in engineering and construction
Special Issues, Collections and Topics in MDPI journals

Dr. Kishor Shrestha

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Guest Editor

Voiland College of Engineering and Architecture, Washington State University, Pullman, WA 99164, USA
Interests: injury prevention; construction engineering; transportation; road safety; traffic safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The construction industry remains one of the most hazardous sectors worldwide, with workers frequently exposed to risks that lead to injuries, illnesses, and fatalities. As construction projects become increasingly complex, ensuring effective safety management and occupational health practices is essential for safeguarding workers' well-being and maintaining project efficiency. This Special Issue focuses on advancing construction safety, occupational health, and risk management research to foster a safer and more sustainable work environment.

This Special Issue will bring together cutting-edge research, innovative methodologies, practical case studies, and practical applications of AI that enhance safety management strategies and occupational health frameworks in construction. We invite academics, researchers, industry professionals, and policymakers to contribute original research articles, reviews, and case studies addressing key challenges and solutions in construction safety. We also invite articles that explore how AI-powered technologies can enhance hazard detection, safety training, and decision-making in construction.

Topics of interest include, but are not limited to, the following:

Safety Management Systems: Development and implementation of safety policies, procedures, and compliance frameworks;
Occupational Health Risks: Identification and mitigation of work-related illnesses, such as musculoskeletal disorders (MSDs) and psychological stress;
Human Factors and Ergonomics: Understanding how physical and cognitive loads impact workers’ safety and performance;
Technology and Safety Innovations: Applications of artificial intelligence (AI), the Internet of Things (IoT), wearable sensors, and virtual/augmented reality (VR/AR) for safety training and hazard prediction;
AI-Enhanced Safety Management Systems: Implementation of AI-driven predictive analytics, machine learning models, and automation for proactive safety management;
Safety Culture and Behavioral Approaches: Exploring how leadership, training, and worker perceptions influence safety outcomes;
Computer Vision and Hazard Detection: Image and video analysis for identifying unsafe behaviors, PPE compliance, and hazardous conditions on-site;
Regulatory Compliance and Policy Impacts: Evaluating the effectiveness of occupational safety laws, standards, and enforcement mechanisms;
Life Cycle Safety Management: Integrating safety considerations from project design to demolition to enhance worker protection throughout the construction process.

We welcome interdisciplinary approaches that combine engineering, AI, safety science, management, and emerging technologies to advance knowledge in construction safety and occupational health. Contributions should provide practical insights, empirical evidence, or case studies that can improve industry practices and inform policy development.

Dr. Krishna Kisi
Dr. Kishor Shrestha
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 250 words) can be sent to the Editorial Office for assessment.

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 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 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
occupational health
human factors
ergonomics
hazard detection
worker well-being
decision making
workplace monitoring
automation in safety management

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Related Special Issue

Safety Management and Occupational Health in Construction—2nd Edition in Buildings

Published Papers (13 papers)

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Research

29 pages, 2292 KB

Open AccessArticle

Exploring the Factors Influencing Construction Workers’ Safety Behavior: An Artificial Intelligence-Based Model Approach

by Mohammed Y. Wahan, Chunyan Yuan and Hafiz Zahoor

Buildings 2026, 16(10), 1965; https://doi.org/10.3390/buildings16101965 - 15 May 2026

Abstract

A substantial proportion of construction accidents is associated with unsafe worker behavior. Identifying their underlying mechanism is vital for designing effective interventions. As prior studies could not capture complex nonlinear interactions among organizational and individual factors, this study leverages machine learning (ML) techniques, which can capture complex relationships by handling large datasets, and can identify patterns in worker behavior. The study proposes an explainable ML model to interpret key determinants of safe behavior. The data were collected from 425 construction workers in Saudi Arabia. Multiple ensemble and benchmark ML algorithms—including random forest (RF), categorical boosting, decision jungle, light gradient boosting machine, support vector machine, and adaptive boosting—were implemented and compared. The results indicate that the RF model achieved the best predictive performance, outperforming several competing models. To enhance the model’s interpretability, explainable artificial intelligence (XAI) techniques were applied to reveal the interaction of key predictors influencing workers’ behaviors. The results demonstrate that safety communication, risk perception, and supportive work environment are the most influential determinants shaping safety behavior. As a key novelty, this study introduces an ML-based approach for predicting construction workers’ safety behavior and applies XAI techniques to systematically interpret the key determinants of safety behavior. The results also provide valuable insights for safety managers and offer data-driven guidance to enhance the effectiveness of safety interventions. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

26 pages, 887 KB

Open AccessArticle

Using Safety Accountability to Enhance Construction Safety Performance: The Mediating Roles of Safety Monitoring and Safety Learning Under Inclusive Leadership

by Mohamed Mohamed and Benard Vetbuje

Buildings 2026, 16(6), 1244; https://doi.org/10.3390/buildings16061244 - 21 Mar 2026

Viewed by 427

Abstract

Safety performance remains a persistent challenge in the construction industry due to hazardous working conditions, dynamic site environments, and complex organizational structures. Despite regulatory advances and technical safety controls, accident rates remain high, suggesting that formal mechanisms alone are insufficient. Addressing this gap, this study examines safety accountability as a central organizational mechanism and investigates how it influences construction workers’ safety performance through behavioral processes and leadership conditions. Drawing on accountability theory and social learning theory, we propose a moderated parallel mediation model in which safety monitoring and safety learning function as mediators, while inclusive leadership behavior serves as a contextual moderator. Data were collected from 629 construction workers employed in large-scale projects in Istanbul and Ankara, Türkiye, using a two-wave survey design to mitigate common method bias. Hypotheses were tested using confirmatory factor analysis and Hayes’ PROCESS macro. The results indicate that safety accountability does not exert a significant direct effect on safety performance; rather, its influence is fully transmitted through safety monitoring and safety learning, with monitoring emerging as the stronger mediating mechanism. Moreover, inclusive leadership behavior significantly strengthens the accountability-driven pathways leading to improved safety outcomes. By integrating accountability structures, behavioral processes, and leadership context, this study advances construction safety research and provides evidence-based guidance for enhancing occupational safety performance in high-risk construction environments. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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16 pages, 3402 KB

Open AccessArticle

A Musculoskeletal Simulation Study to Evaluate the Influence of Postural and Anthropometric Variability on Intervertebral Loads During Manual Lifting in Construction

by Jose Javier Guevara-Torres, Jhon Alexander Quiñones-Preciado, Alexander Paz, Héctor E. Jaramillo Suarez, José Jaime García and Lessby Gómez-Salazar

Buildings 2026, 16(6), 1156; https://doi.org/10.3390/buildings16061156 - 15 Mar 2026

Viewed by 557

Abstract

Computational simulation is a valuable tool for advancing personalized ergonomics. This study evaluated the ability of musculoskeletal simulation to estimate individual lumbar loading during manual lifting tasks representative of construction activities. Fifty-six Colombian adults were recruited to reflect national anthropometric distributions and grouped by BMI and stature. Participants performed two standardized lifting tasks with a 10 kg load: symmetric lifting from the floor to xiphoid height and lateral lifting from a 0.40 m surface to shoulder height with contralateral transfer. Whole-body kinematics and ground reaction forces were processed in OpenSim software using the validated model to estimate L5–S1 compression and shear forces. Results showed a moderate association between lumbar compression and body weight, while shear forces exhibited low correlations with kinematic variables. Subject-specific scaled models revealed substantial inter-individual differences in lumbar loading related to lifting technique and anthropometric characteristics, highlighting the potential of musculoskeletal simulation for personalized risk assessment in construction. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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14 pages, 245 KB

Open AccessArticle

Ergonomic Risk and Musculoskeletal Disorders in Construction: Assessing Job-Related Determinants in the U.S. Workforce

by Krishna Kisi and Omar S. López

Buildings 2026, 16(2), 286; https://doi.org/10.3390/buildings16020286 - 9 Jan 2026

Viewed by 1485

Abstract

Musculoskeletal disorders (MSDs) remain one of the most persistent occupational health challenges in the U.S. construction industry, where physically demanding tasks such as heavy lifting, kneeling, and working in awkward postures contribute to elevated injury rates. This study aims to identify significant job-related determinants of MSDs in construction-sector occupations. By integrating publicly available datasets from the Survey of Occupational Injuries and Illnesses (SOII) and the Occupational Information Network (O*NET) datasets, a stepwise multiple regression analysis was conducted on 344 occupation-condition observations representing 86 construction occupations, yielding a final model that explained 49% of the variance. Ten significant predictors of MSD events were identified and classified as either risk amplifiers or mitigators. Amplifiers included factors such as exposure to noise, disease, hazardous conditions, and time pressure, all of which heightened MSD risk, while mitigators—such as reduced cramped-space exposure and regulated work environments—were associated with lower risk. MSDs resulting from sprains, strains, or tears accounted for 62.8% of all cases, frequently leading to days away from work (36.3%) or job restrictions (26.5%). The findings underscore that ergonomic risk in construction extends beyond physical strain to include scheduling, equipment design, and work organization. These results provide actionable insights for employers and safety professionals to redesign tools, optimize task rotation, and implement realistic work pacing strategies, ultimately reducing MSD incidence and improving productivity in this high-risk sector. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

23 pages, 700 KB

Open AccessArticle

Hierarchical Modeling of Safety Factors in the Construction Industry Using Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL)

by Mohammed Alamoudi

Buildings 2026, 16(1), 155; https://doi.org/10.3390/buildings16010155 - 29 Dec 2025

Cited by 1 | Viewed by 732

Abstract

Understanding the causal relationships between safety factors is essential for successful intervention in industries with intrinsically high-risk environments such as the construction industry. Therefore, the aim of this study is to employ the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques to analyze and map the interdependencies among various safety-related elements affecting construction safety. According to the results, resource allocation was shown to be the highest-level, most independent element in the analysis, highlighting its function as the primary facilitator of safety initiatives. This strategic commitment directly drives Management Commitment and Competence, which form the core organizational support structure. Mid-level elements that translate management intent into site-level practice include workers’ training, safety motivation, and communication structure. The frequency of safety observations, workers’ involvement in safety decisions, and subcontractor and procurement management—the immediate procedural controls—are then used to assess operational efficacy. Crucially, the most dependent factor was found to be Workers’ Compliance, indicating that frontline safety behavior is the result of efficient management at all higher levels. Therefore, in order to improve overall safety performance in construction, this research emphasizes the importance of improving resource provision and leadership commitment. The outputs of the current study provide an organized, evidence-based roadmap for selecting interventions. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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21 pages, 1771 KB

Open AccessArticle

Transnational Construction Project Risk Factors and Their Impact Pathways

by Qingzhen Yao and Lue Lan

Buildings 2025, 15(24), 4526; https://doi.org/10.3390/buildings15244526 - 15 Dec 2025

Viewed by 637

Abstract

This paper investigates risk factors and their propagation pathways in multinational building projects. An initial set of 30 key risk variables associated with transnational engineering projects was identified. Using Interpretive Structural Modeling (ISM), we constructed a hierarchical framework to elucidate the interrelationships and transmission dynamics among risk factors. The Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) method was then employed to categorize these factors into three distinct layers: root causes, intermediaries, and surface-level outcomes. Our analysis revealed 16 risk transmission pathways. Among the 30 variables, four were identified as root drivers, 22 as propagation factors, and four as surface triggers. Risk typically migrates from the root layer to the surface within three to four steps. Notably, ten factors—most prominently stakeholder demand mismatch, sociocultural conflict, and inefficient information exchange—collectively account for 55% of the total causal influence, forming the “risk core” of the system. This study enhances the theoretical understanding of risk evolution in international construction projects and offers practical guidance for effective risk management. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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39 pages, 14020 KB

Open AccessArticle

LOINSH Information Structure for the Assessment of Occupational Risks in the Execution of Roads Based on the LOIN Standard

by Darío Collado-Mariscal, Juan Pedro Cortés-Pérez, Mario Núñez-Fernández and Alfonso Cortés-Pérez

Buildings 2025, 15(24), 4452; https://doi.org/10.3390/buildings15244452 - 10 Dec 2025

Cited by 2 | Viewed by 586

Abstract

Despite regulatory advances, there continues to be a high accident rate on construction sites, especially on road projects, mainly due to the lack of organization of safety information. Although there is research demonstrating the benefits of the BIM methodology for improving occupational safety, its scope is still limited. This study addresses the integration of occupational health and safety in road projects using the BIM methodology, in line with ISO 19650-1, proposing a standardization framework based on ISO 7817-1:2024. The concept of Level of Information for Safety and Health (LOINSH) is introduced, structured into four categories (100, 200, 300, and 350), which allows risks to be managed progressively throughout the project’s life cycle. The framework defines graphical and alphanumeric requirements for BIM objects, establishing sets of parameters recognized by the open IFC format to ensure interoperability and traceability. It also proposes a system for assessing risks associated with activities and disciplines, facilitating preventive decisions from the design stage onwards. The results indicate that this standardization improves communication and collaboration between agents, reduces workplace accidents, and can be applied to other types of construction works. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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19 pages, 2815 KB

Open AccessArticle

Proposed Safety Control Structure Model for Building Demolition Projects Based on STAMP Model in South Korea

by Hyeon-Ji Jeong and Jeong-Hun Won

Buildings 2025, 15(20), 3680; https://doi.org/10.3390/buildings15203680 - 13 Oct 2025

Viewed by 2395

Abstract

This study developed a safety control structure model to analyze stakeholder interactions to improve safety in Korean building demolition projects. Legal stakeholders and safety measures were systematically incorporated into the System Theoretic Accident Model and Processes (STAMP). The novelty of this safety model is that it provides the first systematic application of STAMP to Korean building demolition, with a specific focus on legal stakeholders and their safety interactions. The results revealed that unsystematic reviews by licensing agencies, the absence of expert reviewers, and the inadequate role of supervisors were key factors contributing to accidents. In particular, the inspection and corrective action of safety measures performed by demolition supervisors directly impacted on-site safety. Furthermore, licensing agencies were identified as key players in determining the overall safety level of demolition projects. The proposed model provides a framework for effectively understanding the roles and responsibilities of stakeholders and supports the identification of non-compliance with safety measures. The use of the proposed model is expected to strengthen the interaction between stakeholders, enhance on-site safety, and contribute to the development of accident prevention strategies for future demolition projects. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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24 pages, 1206 KB

Open AccessArticle

Using Safety-Specific Transformational Leadership to Improve Safety Behavior Among Construction Workers: Exploring the Role of Knowledge Sharing and Psychological Safety

by Mohamed Ali, Kolawole Iyiola, Ahmad Alzubi and Hasan Yousef Aljuhmani

Buildings 2025, 15(18), 3340; https://doi.org/10.3390/buildings15183340 - 15 Sep 2025

Cited by 10 | Viewed by 4136

Abstract

Leaders play a crucial role in shaping employees’ safety behaviors (SBs). However, research on broader leadership styles has yielded inconsistent findings, emphasizing the need for a more tailored leadership approach, especially in high-risk industries, such as construction. Applying the social exchange theory and the positive organizational behavior framework, this study examined the impact of safety-specific transformational leadership (SSTL) on SB. This study uses a quantitative research design to collect data from employees of Turkish construction firms in Ankara and Istanbul. A cross-sectional research design was employed, with purposive sampling of data collected from 706 construction workers in Türkiye. The findings indicate that SSTL positively influences both SB and knowledge sharing, whereas knowledge sharing enhances SB. Knowledge sharing mediates the relationship between SSTL and SB. This study’s findings suggest that implementing safety-specific transformational leadership (SSTL) can significantly improve safety behavior among construction workers by promoting knowledge sharing and psychological safety. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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30 pages, 13771 KB

Open AccessArticle

A High-Performance Hybrid Transformer–LSTM–XGBoost Model for sEMG-Based Fatigue Detection in Simulated Roofing Postures

by Sujan Acharya, Krishna Kisi, Sabrin Raj Gautam, Tarek Mahmud and Rujan Kayastha

Buildings 2025, 15(17), 3005; https://doi.org/10.3390/buildings15173005 - 24 Aug 2025

Cited by 3 | Viewed by 2832

Abstract

Within the hazardous construction industry, roofers represent one of the most at-risk workforces, with high fatalities and injury rates largely driven by Work-Related Musculoskeletal Disorders (WMSDs). The primary precursor to these disorders is muscle fatigue, yet its objective assessment remains a significant challenge for implementing proactive safety management. To address this gap, this study details the implementation and validation of an AI-driven predictive analytics framework for automated fatigue detection using surface electromyography (sEMG) signals. Data was collected as participants (novice roofers) performed strenuous, simulated roofing tasks involving sustained standing, stooping, and kneeling postures. A key innovation is a data-driven labeling methodology using Weak Monotonicity (WM) trend analysis to automate the generation of objective labels. After a feature selection process yielded seven significant features, an evaluation of standard models confirmed that their classification performance was highly posture-dependent, motivating a more robust, hybrid solution. The framework culminates in a high-performance hybrid machine learning model. This architecture synergistically combines a Transformer–LSTM network for deep feature extraction with an XGBoost classifier. The model outperformed all standalone approaches, achieving over 82% accuracy across all postures with consistently strong fatigue F1-scores (0.77–0.78). The entire framework was validated using a stringent Leave-One-Subject-Out (LOSO) cross-validation protocol to ensure subject-independent generalizability. This research provides a validated component for AI-enhanced safety management systems. Future work should prioritize field validation with professional workers to translate this framework into practical, real-world ergonomic monitoring systems. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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15 pages, 1247 KB

Open AccessArticle

Prioritizing Critical Factors Affecting Occupational Safety in High-Rise Construction: A Hybrid EFA-AHP Approach

by Hai Chien Pham, Si Van-Tien Tran and Ung-Kyun Lee

Buildings 2025, 15(15), 2677; https://doi.org/10.3390/buildings15152677 - 29 Jul 2025

Cited by 1 | Viewed by 1631

Abstract

High-rise construction presents heightened safety risks due to vertical complexity, spatial constraints, and workforce variability. Conventional safety management often proves insufficient, especially in rapidly urbanizing or resource-limited settings. This study proposes a hybrid methodological framework to systematically identify and prioritize the critical factors influencing occupational safety in Vietnamese high-rise construction projects. Based on 181 valid survey responses from construction professionals, 23 observed variables were developed through extensive literature review and expert consultation. Exploratory Factor Analysis (EFA) was employed to empirically group 23 validated indicators into five key latent dimensions: (1) Safety Training and Inspection, (2) Employer’s Knowledge and Responsibility, (3) Worker’s Competence and Compliance, (4) Working Conditions and Environment, and (5) Safety Equipment and Signage. These dimensions were then structured into an Analytic Hierarchy Process (AHP) model, with pairwise comparisons conducted by industry experts to calculate consistency ratios and derive factor weights across three high-rise project case studies. The findings provide actionable insights for construction managers, safety professionals, and policymakers in developing and underdeveloped countries, supporting data-driven decision-making for safer and more sustainable urban development. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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18 pages, 242 KB

Open AccessArticle

Exploring Factors Impeding the Implementation of Health and Safety Control Measures in the South African Construction Industry

by Ndaleni Phinias Rantsatsi

Buildings 2025, 15(14), 2439; https://doi.org/10.3390/buildings15142439 - 11 Jul 2025

Viewed by 2580

Abstract

Organisations have provided health and safety (H&S) control measures for construction activities, but the literature suggests that implementing these measures in the construction industry remains a challenge. This study aims to explore the factors impeding the implementation of H&S control measures (barriers). The study followed a qualitative research approach using interview form as a data collection tool designed to collect qualitative data on the factors impeding the implementation of H&S control measures. Purposive sampling method was adopted. The content analysis method was used to analyse the collected data. The findings reveal that the implementation of H&S control measures is affected by different barriers. The study uncovered eight main barriers (lack of management support and commitment, implementation costs, lack of training and education, language and cultural differences, time pressure, prioritisation of production over H&S issues, lack of worker involvement and participation and lack of communication) to the implementation of H&S control measures. Respondents were mainly from H&S background; it would be interesting to explore the perceptions of site managers, engineers, designers, supervisors and field workers through the use of a quantitative approach involving a larger sample. By identifying and understanding these barriers to the implementation of H&S control measures, construction organisations could be in a better position to control construction hazards. This paper adds value to construction organisations and professionals’ understanding of barriers to the implementation of H&S control measures on construction sites. The study also recommends measures to remove barriers or facilitate better implementation of H&S control measures on construction sites. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

44 pages, 1470 KB

Open AccessArticle

GPT Applications for Construction Safety: A Use Case Analysis

by Ali Katooziani, Idris Jeelani and Masoud Gheisari

Buildings 2025, 15(14), 2410; https://doi.org/10.3390/buildings15142410 - 9 Jul 2025

Cited by 1 | Viewed by 3943

Abstract

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management. Full article

(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)

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