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Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification

Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuabl...

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Detalles Bibliográficos
Autores principales: Chen, Shu, Xi, Junbo, Chen, Yun, Zhao, Jinfan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747904/
https://www.ncbi.nlm.nih.gov/pubmed/35024045
http://dx.doi.org/10.1155/2022/4851615
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author Chen, Shu
Xi, Junbo
Chen, Yun
Zhao, Jinfan
author_facet Chen, Shu
Xi, Junbo
Chen, Yun
Zhao, Jinfan
author_sort Chen, Shu
collection PubMed
description Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between “locations” and “types” by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the “location ⟶ type” strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites.
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spelling pubmed-87479042022-01-11 Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification Chen, Shu Xi, Junbo Chen, Yun Zhao, Jinfan Comput Intell Neurosci Research Article Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between “locations” and “types” by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the “location ⟶ type” strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites. Hindawi 2022-01-03 /pmc/articles/PMC8747904/ /pubmed/35024045 http://dx.doi.org/10.1155/2022/4851615 Text en Copyright © 2022 Shu Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Shu
Xi, Junbo
Chen, Yun
Zhao, Jinfan
Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
title Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
title_full Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
title_fullStr Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
title_full_unstemmed Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
title_short Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification
title_sort association mining of near misses in hydropower engineering construction based on convolutional neural network text classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747904/
https://www.ncbi.nlm.nih.gov/pubmed/35024045
http://dx.doi.org/10.1155/2022/4851615
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