Analyzing the Causes and Safety Barriers of Accidents in Gas Pipeline Excavation and Piping Operations Using Tripod Beta and Bowtie Methods: A Case Study of "Struck By" Accidents

Journal of Occupational Health and Epidemiology, Journal Year: 2024, Volume and Issue: 13(2), P. 76 - 98

Published: June 1, 2024

Language: Английский

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Enhanced identification of equipment failures from descriptive accident reports using language generative model

Engineering Construction & Architectural Management, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Purpose Equipment failure is a critical factor in construction accidents, often leading to severe consequences. Therefore, this study addresses two significant gaps safety research: (1) effectively using historical data investigate equipment and (2) understanding the classification of according Occupational Safety Health Administration (OSHA) standards. Design/methodology/approach Our research utilized multi-stage methodology. We curated from OSHA database, distinguishing accidents involving failures. Then we developed framework generative artificial intelligence (AI) large language models (LLMs) minimize manual processing. This employed two-step prompting strategy: classifying narratives that describe failures analyzing these cases extract specific details (e.g. names, types, categories). To ensure accuracy, conducted analysis subset reports establish ground truth tested different LLMs within our approach, comparing their performance against truth. Findings The demonstrated 95% accuracy determining if 73% extracting enabling automated categorical identifications. These findings highlight LLMs’ promising identification compared methods. Research limitations/implications research’s focus on not only validates but also highlights its potential for broader application across various accident categories beyond construction, extending into any domain with accessible narratives. Given such are essential regulatory bodies like OSHA, framework’s adoption could significantly enhance reporting, contributing more robust protocols industry-wide. Practical implications Using enables us use narratives, reliable source data, analysis. It provides deeper insights than traditional detailed at an unprecedented level. enhanced can inform improve worker training, education policies, applications safety-critical domains. Originality/value presents novel approach AI LLMs, reducing processing time while maintaining high accuracy. By identifying efficiently, work lays groundwork developing targeted protocols, overall improvements practices advancing data-driven processes.

Language: Английский

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Machine Learning Applications in Traffic Safety: Assessing Accident Severity Automatically

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Journal Year: 2023, Volume and Issue: unknown, P. 1197 - 1203

Published: Nov. 22, 2023

The alarming increase in road accidents recent years has elevated them to a significant global issue., making the ninth leading cause of death worldwide. It is unfortunate and completely unacceptable that there have been fatalities because these accidents. As result., it critical this problem be addressed thoroughly. With aid cutting-edge machine learning techniques., proposed study will analyze traffic incidents detail. main goal pinpoint major causes collisions offer insightful suggestions for reducing issue. divide accident severity into three categories: fatal injury., serious minor injury. To do this., makes use variety supervised such as Decision Trees., Support Vector Machines (SVM)., Multinomial Naive Bayes., K-Nearest Neighbors (KNN)., Random Forests., XGBoost., MLPClassifiers., AdaBoost. Notably., Forest model excels with outstanding performance., obtaining stunning 90% accuracy rate successfully forecasting severity.

Language: Английский

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A bibliometric and scientometric analysis-based review of environmental health and safety research in the construction industry

Journal of Engineering Design and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 19, 2024

Purpose The construction business is widely recognised for its inherent complexity and dynamic nature, which stems from the nature of job involved. industry often regarded as one most challenging industries globally in terms implementing environmental, health safety (EHS) practices. However, absence EHS, cannot be considered sustainable. Therefore, this study aims to identify trends, knowledge gaps implications EHS research enhance activities knowledge. Design/methodology/approach adopted a science mapping approach involving bibliometric scientometric analysis 407 publications Scopus database with VOSviewer software. based on journal articles without restriction any time range. Findings main focus identified includes sustainability-related studies, risk-related, environmental issues, management, integrated management systems related process. Some emerging areas also include productivity, design, culture, social sustainability machine learning. influential productive publication sources, countries/regions highest impact were determined. Research limitations/implications Documents published because wider coverage database. Journal written English language represent inclusion criteria, whereas other documents excluded analysis. limited search engineering subject area. Practical findings will enlighten stakeholders practitioners focal domain, are vital enhancing industry. Originality/value To best authors’ knowledge, review-based first attempt internationally conduct extant literature domain through assessments.

Language: Английский

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The Role of Automated Classification in Preserving Indonesian Folk and National Songs

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 288 - 306

Published: Jan. 1, 2024

Language: Английский

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