Cause analysis of hot work accidents based on text mining and deep learning

Xu, Hui; Liu, Yi; Shu, Chi‐Min; Bai, Mingqi; Motalifu, Mailidan; He, Zhongxu; Wu, Shuncheng; Zhou, Penggang; Li, Bing

doi:10.1016/j.jlp.2022.104747

Cited by 41 publications

(19 citation statements)

References 40 publications

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“…Ensemble learning involves combining multiple classifiers or models to solve a single predictive issue. Random factor's prediction performance is getting better than that of a single decision tree because of the logic that the class is selected based on the maximum votes all trees in the forest have given 43 . The random forests can handle both categorical and continuous data.…”

Section: Methodsmentioning

confidence: 99%

“…Balasubramanian and Thangamani 41 ; Kurian et al 21,42 ; Xu et al 43 ; Cakir et al 44 ; Tamascelli et al 5 ). According to the findings, accident prediction models in the oil and gas industry are still in their early stages.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

“…Random factor's prediction performance is getting better than that of a single decision tree because of the logic that the class is selected based on the maximum votes all trees in the forest have given. 43 The random forests can handle both categorical and continuous data. Several studies in injury prediction have used RF and it has also been useful in computing missing values and feature engineering task.…”

Section: Random Forestmentioning

confidence: 99%

“…Several authors have used ML to predict occupational accidents analysis in different sectors, in the aviation industry (Herrema et al 26 ; Burnett and Si 27 ), the mining industry (Rivas et al 22 ; Matias et al 28 ), construction industry (Tixier et al 4 ; Goh and Ubeynarayana 6 ; Chokor et al 29 ; Mistikoglu et al 30 ; Liao et al 31 ; Zhu et al 32 ; Choi et al 33 ), in steel and other mineral extraction industry (Sarkar et al 8,11,16,23 ; Shirali et al 34 ; Yang et al 35 ; Dumakor‐Dupey et al 36 ), railways (Alawad et al 37 ; Gao et al 38 ), autonomous vehicle safety (Wang et al 39 ), petrochemical industry (Mugunthan 40 ; Balasubramanian and Thangamani 41 ; Kurian et al 21,42 ; Xu et al 43 ; Cakir et al 44 ; Tamascelli et al 5 ). According to the findings, accident prediction models in the oil and gas industry are still in their early stages.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

“…In the petroleum sector, a few studies discussed the significance of ML techniques in predicting accident outcomes. Mugunthan 40 and Xu et al 43 have automated the risk assessment and accident data analysis systems by using state of art neural networks and deep learning architectures. These systems are capable of issuing a warning message when irregular or unsafe activity at the site is observed.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

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Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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Summary Recent advancements in machine learning techniques are helping researchers to develop predictive models that assist decision‐makers to get a quick, unbiased overview of the processes. But studies using machine learning approaches in analyzing and classifying the injury narratives of the petroleum industries are still in their early stages due to data unavailability and lack of trust in these models. Comparatively, other industries such as construction, manufacturing, aviation and so forth are using the findings from the predictive models but the use of machine learning techniques in analyzing petroleum industry accident data is not gaining much importance. This study aims to use available accident data from the Indian petroleum industry to develop a classification model for predicting possible outcomes of an accident. The data used in this study comprises 194 accident reports with 20 information attributes collected during the 2016–20 period. Six different machine learning algorithms are used to analyze and classify the possible outcome of the accident. It has been found that the Xgboost algorithm has achieved 95% accuracy following multilayer perceptron with 94% accuracy. The rough set theory is also used to extract the indiscernibility relationship between the given attributes causing accident occurrence. The results indicate that “skill‐based error, supervisory violation, no personal protective equipment usage, and lack of standard operating procedure compliance” have contributed to the majority of the accidents. The findings of this study can be used to assist safety professionals in decision‐making, mitigating the root causes of contributing factors.

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Section: Methodsmentioning

confidence: 99%

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

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Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting

Shao,

Shi,

Zhang

et al. 2024

Engineering Applications of Artificial Intelligence

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Machine learning-based models to prioritize scenarios in a Quantitative Risk Analysis: An application to an actual atmospheric distillation unit

Macêdo

Moura

Ramos

et al. 2022

Journal of Loss Prevention in the Process Industries

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Cause analysis of hot work accidents based on text mining and deep learning

Cited by 41 publications

References 40 publications

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting

Machine learning-based models to prioritize scenarios in a Quantitative Risk Analysis: An application to an actual atmospheric distillation unit

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