Design for safety training for construction professionals: A digital game-based learning approach DOI
Juliana Tay, Sufiana Safiena, Tianxiang Lan

и другие.

Safety Science, Год журнала: 2024, Номер 177, С. 106588 - 106588

Опубликована: Июнь 18, 2024

Язык: Английский

Modification of HFACS model for path identification of causal factors of collapse accidents in the construction industry DOI
Haonan Qi, Zhipeng Zhou, Javier Irizarry

и другие.

Engineering Construction & Architectural Management, Год журнала: 2024, Номер unknown

Опубликована: Май 5, 2024

Purpose This study aims to modify the human factors analysis and classification system (HFACS) make it suitable for collapse accident in construction. Based upon modified HFACS, distribution patterns of causal across multiple levels were discerned among various stakeholders at construction sites. It explored correlations between two from different further determined causation paths perspectives level stakeholder. Design/methodology/approach The main research framework consisted data collection, coding analysis. Collapse reports collected with adequate information. HFACS was utilized all five each case. A hybrid approach stakeholder proposed frequency analysis, correlation path identification factors. Findings Eight external organizations fifth added original HFACS. Level-based analyses provided safety managers a holistic view inter-connected levels. Stakeholder-based its non-adjacent implemented based on client, government third parties. These identified useful develop specific plans avoiding accidents. Originality/value paper fulfils an need utilize model resulting accidents, which can provide opportunities tailoring preventive protective measures

Язык: Английский

Процитировано

17

Study on construction safety management in megaprojects from the perspective of resilient governance DOI
Kai Liu, Yuming Liu, Yuanyuan Kou

и другие.

Safety Science, Год журнала: 2024, Номер 173, С. 106442 - 106442

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

16

Development and testing of immersive virtual reality environment for safe unmanned aerial vehicle usage in construction scenarios DOI
Mariusz Szóstak, Abdul‐Majeed Mahamadu, Abhinesh Prabhakaran

и другие.

Safety Science, Год журнала: 2024, Номер 176, С. 106547 - 106547

Опубликована: Апрель 27, 2024

Язык: Английский

Процитировано

9

Analyzing Research Trends in Smart Construction Safety: A Topic Modeling Approach DOI Creative Commons
Hyun Jeong Seo,

Young-Geun Yoon

Buildings, Год журнала: 2025, Номер 15(4), С. 520 - 520

Опубликована: Фев. 8, 2025

The construction industry is increasingly embracing smart technologies to enhance safety, efficiency, and sustainability. Despite their potential, the practical integration of such as digital twins, Internet Things (IoT), big data into safety management systems remains insufficiently explored. This study investigates how these can be effectively implemented improve outcomes. A systematic review literature conducted, culminating in development a conceptual framework for integrating systems. highlights application IoT, real-time monitoring, predictive risk management, resource optimization. findings reveal that significantly site by proactively identifying hazards, reducing accidents, improving allocation. Moreover, contribute environmental sustainability optimizing energy use lowering carbon emissions. research underscores dual benefits technological integration, advancing both objectives. While provides theoretical insights implications, further empirical across diverse environments necessary validate refine proposed framework.

Язык: Английский

Процитировано

1

Automatic Identification of Causal Factors from Fall-Related Accident Investigation Reports Using Machine Learning and Ensemble Learning Approaches DOI
Haonan Qi, Zhipeng Zhou, Javier Irizarry

и другие.

Journal of Management in Engineering, Год журнала: 2023, Номер 40(1)

Опубликована: Окт. 5, 2023

To enhance the performance of learning from past fall-related accidents, this study developed an innovative framework for automatically extracting every individual causal factor accident investigation reports based upon modified human factors analysis and classification system. Multiple techniques including synthetic minority oversampling technique (SMOTE) algorithm handling imbalanced data, soft voting with unequal weights ensemble learning, hyperparameter optimization were adopted to improve automatic identification unstructured text data. Experimental results denoted there no classifiers best accuracy F1 score unanimously any 19 subcategories factors. Therefore, one or more specific preferred predicting performance. Further comparative analyses between seven demonstrated that model by (ELSV) could provide stable predictions low variance across different compared machine models. It was suggested ELSV ought be prioritized collectively identifying all These findings are beneficial substantial accidents high efficiency reliability, valuable insights can discerned utilized controlling risk fall-from-height at construction sites.

Язык: Английский

Процитировано

21

Are virtual reality applications effective for construction safety training and education? A systematic review and meta-analysis DOI
Siu Shing Man, Huiying Wen, Billy C. L. So

и другие.

Journal of Safety Research, Год журнала: 2023, Номер 88, С. 230 - 243

Опубликована: Дек. 2, 2023

Язык: Английский

Процитировано

21

EMERGING TRENDS IN CYBERSECURITY FOR CRITICAL INFRASTRUCTURE PROTECTION: A COMPREHENSIVE REVIEW DOI Creative Commons

Sontan Adewale Daniel,

Samuel Segun Victor

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(3), С. 576 - 593

Опубликована: Март 10, 2024

As critical infrastructure becomes increasingly interconnected and digitized, the need for robust cybersecurity measures to safeguard essential systems is more pressing than ever. This review article explores dynamic landscape of infrastructure, focusing on emerging trends, current challenges, future prospects. The historical overview delves into evolution cyber threats, emphasizing adaptive security measures. Key components are examined, elucidating specific challenges each sector faces. state analyzed, with a spotlight frameworks that guide organizations in bolstering their defenses. heart trends cybersecurity, covering artificial intelligence machine learning threat detection, IoT security, blockchain applications, advancements cloud computing security. Challenges threats horizon, including advanced persistent quantum implications, scrutinized provide insights potential vulnerabilities. Keywords: Cybersecurity; Critical Infrastructure; Artificial Intelligence; Internet-of-Things; Blockchain.

Язык: Английский

Процитировано

6

The Concept of Creating Digital Twins of Bridges Using Load Tests DOI Creative Commons
Marcin Jasiński, Piotr Łaziński, Dawid Piotrowski

и другие.

Sensors, Год журнала: 2023, Номер 23(17), С. 7349 - 7349

Опубликована: Авг. 23, 2023

The paper sheds light on the process of creating and validating digital twin bridges, emphasizing crucial role load testing, BIM models, FEM models. At first, presents a comprehensive definition concept, outlining its core principles features. Then, framework for implementing concept in bridge facilities is discussed, highlighting potential applications benefits. One components highlighted testing validation updating model further use framework. Load emphasized as key step ensuring accuracy reliability twin, it allows refinement To illustrate practical application issues during tuning model, provides an example real bridge. It shows how utilized to generate computational model. results tests carried out are demonstrating importance data obtained from these calibrating which forms critical part

Язык: Английский

Процитировано

13

Navigating leadership styles through qualitative exploration for enhanced safety in the construction sector DOI

S. Senthamizh Sankar,

K. S. Anandh

Safety Science, Год журнала: 2024, Номер 175, С. 106495 - 106495

Опубликована: Март 16, 2024

Язык: Английский

Процитировано

5

Hazard identification performance comparison between virtual reality and traditional construction safety training modes for different learning style individuals DOI
Xiaotong Guo, Yujie Liu,

Yubing Tan

и другие.

Safety Science, Год журнала: 2024, Номер 180, С. 106644 - 106644

Опубликована: Авг. 19, 2024

Язык: Английский

Процитировано

5