Text mining and association rules-based analysis of 245 cement production accidents in a cement manufacturing plant.
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| Title: | Text mining and association rules-based analysis of 245 cement production accidents in a cement manufacturing plant. |
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| Authors: | Wang, Bing, Wang, Yuanjie, Gong, Yan, Shi, Zhiyong |
| Source: | International Journal of Occupational Safety & Ergonomics; Dec2025, Vol. 31 Issue 4, p1201-1215, 15p |
| Subject Terms: | INDUSTRIAL hygiene standards, WORK-related injuries risk factors, RISK assessment, SOCIAL network analysis, RESEARCH funding, LABOR productivity, SAMPLE size (Statistics), QUANTITATIVE research, DECISION making, DESCRIPTIVE statistics, CONFIDENCE, WORK-related injuries, MANUFACTURING industries, CONCEPTUAL structures, CAUSALITY (Physics), MINERAL industries, DATA analysis software, INDUSTRIAL safety, ALGORITHMS |
| Geographic Terms: | CHINA |
| Abstract: | Accidents such as collapses, fires, explosions and mechanical injuries occur frequently in cement manufacturing plants. Understanding the causes of past accidents is essential to prevent future incidents and reduce safety risks. Hence, this article analyzes cement accident cases based on a unified report analysis framework. By integrating text mining technology, the article identifies patterns in cement production accidents and establishes a cement accident causation analysis model to support safety management decisions. First, 245 accident records were categorized using the latent Dirichlet allocation model to identify causal factors. Subsequently, a systematic accident causal analysis based on the 24Model was proposed to establish a unified report framework. An improved Apriori algorithm was then developed for multidimensional, multilayer correlation rule mining in cement enterprises, enhancing text mining efficiency. By applying this algorithm, the study quantitatively analyzed correlations between accident types, causative factors and their interactions. Finally, targeted safety management recommendations were formulated. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Occupational Safety & Ergonomics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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