A Correlation Analysis of Construction Site Fall Accidents Based on Text Mining

Luo, Xixi; Liu, Quanlong; Qiu, Zunxiang

doi:10.3389/fbuil.2021.690071

Cited by 11 publications

(12 citation statements)

References 36 publications

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“…Next, the focus of the papers was to evaluate the utilization of text-mining and NLP-based techniques in classifying occupational accidents ( 26 , 33 , 35 , 38 , 47 ). Also, these techniques were used to identify anomalous accidents from the narratives ( 29 ).…”

Section: Resultsmentioning

confidence: 99%

“…Suh et al used the sectoral pattern to categorize the type of occupational accident ( 27 ). Another important objective discovered from the papers is the ability of these techniques in determining the causes of occupational injury ( 26 , 27 , 34 , 52 ). Interestingly, a paper by Zhong et al enhanced the findings by providing a visual representation of the accident causal factors ( 44 ).…”

Section: Resultsmentioning

confidence: 99%

“…It has revealed that the R system can examine the unstructured information with a high precision of 0.95. Additionally, Luo et al ( 26 ) utilized R and its software packages to propose a text mining-based method for detecting the occupational accident causal factors and types of the occupational accident based on “term frequency-inverse document frequency” (TF-IDF). Also, Suh used R in constructing the document-term matrix as the input for the “latent-Dirichlet allocation” (LDA) algorithm in the text mining approach to identify the sectoral patterns of occupational accidents from the narratives ( 27 ).…”

Section: Resultsmentioning

confidence: 99%

“…About the databases gathered in the studies, some of the narratives are publicly accessed such as the “Occupational Safety and Health Administration (OSHA)” ( 27 , 33 – 35 , 44 , 45 ), “Mine Safety and Health Administration” ( 19 , 32 ), and “Survey of Occupational Injury and Illness” ( 40 ), beside the databases from the relevant government agencies ( 25 , 26 , 28 ). However, most of the studies represent the internal databases from the respective sector and industry, mainly the occupational injury reports including the records from preliminary hazard analysis ( 53 ) and worker's compensation claims ( 38 , 41 , 50 ).…”

Section: Discussionmentioning

confidence: 99%

“…Researchers may explore the analysis of more occupational injury characteristics, for example, the “activity-based factor,” “causal-and-effect relationship,” and “factor-and-severity relationship” to reveal more mechanisms contributed to workplace injury ( 28 , 40 , 68 ). Additionally, the documents on engineering and technical aspects, as well as, the environmental factors are encouraged to be incorporated in producing comprehensive information for results improvement ( 26 , 53 ).…”

Section: Discussionmentioning

confidence: 99%

See 4 more Smart Citations

Predicting occupational injury causal factors using text-based analytics: A systematic review

Khairuddin

Hasikin‬

Razak

et al. 2022

Front. Public Health

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Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learningbased Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of , out of articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting occupational injury causal factors using text-based analytics: A systematic review

Khairuddin

Hasikin‬

Razak

et al. 2022

Front. Public Health

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Comprehensive factor analysis and risk quantification study of fall from height accidents

Peng,

Liu,

Peng

et al. 2023

Heliyon

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Examining the relationship between construction fatalities and the construction economy in China: a three-decade analysis

Kang

2024

ECAM

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PurposeThe fluctuation of construction fatalities is influenced by both urbanization and economic levels. This study aims to understand the impact of Chinese construction economy development on construction accidents, providing valuable insights for enhancing construction safety and promoting sustainable development in construction.Design/methodology/approachThe Kuznets curve model, multiple linear regression model, and data envelopment analysis (DEA) model are employed to process data sets spanning from 1992 to 2021 for examining the relationship between construction fatalities and the construction economy in China.FindingsSignificant correlations have been found between construction fatalities and the construction economy in China. Over the past three decades, as the total output value of construction increased, there have been upward, downward, and downward trends in per capita construction area, the mortality rate per million square meters, and the mortality rate per ten thousand persons respectively. However, it is worth noting that since 2015, there has been a slight upward trend in the fitted U-shaped curve depicting the relationship between the mortality rate per ten thousand persons and the construction economy. This specific trend necessitates the attention of construction safety policymakers. The growth of the construction economy is found to exhibit negative, positive, and positive correlations with the number of construction fatalities, construction area, and the number of employed persons respectively. The changing trends observed in the Kuznets curve model analysis align with the evaluation results obtained from the DEA-based model.Originality/valueThe research offers effective means to identify superior and inferior performance in macro construction safety, providing valuable references for construction safety policymakers to design effective safety strategies and enhance work safety conditions.

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A Correlation Analysis of Construction Site Fall Accidents Based on Text Mining

Cited by 11 publications

References 36 publications

Predicting occupational injury causal factors using text-based analytics: A systematic review

Predicting occupational injury causal factors using text-based analytics: A systematic review

Comprehensive factor analysis and risk quantification study of fall from height accidents

Examining the relationship between construction fatalities and the construction economy in China: a three-decade analysis

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