Abstract
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.
Highlights
Construction is a high-risk industry, and until recently, construction sites have continued to pose a serious threat to workers’ lives and health [1]
We develop a nearmiss classifier based on a convolutional neural network (CNN), associate the classified results, and visualize them with a network diagram [12] and a Sankey diagram so that safety managers can find the key points of massive near misses [13]
For “dam shoulder slot ⟶ fall from height,” (1) all descriptions of this association rule are collected as shown in Table 4, (2) the Jieba word segmentation package is used to segment the description in Chinese, and (3) words with large word frequency and significance as specific near-miss objects are selected to connect “location” and “type.” A Sankey diagram is drawn to describe the information flow of multiple strong association rules, in which the word frequency is used as the flow size
Summary
Received 22 September 2021; Revised 5 December 2021; Accepted 8 December 2021; Published 3 January 2022. Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Mining near-miss data can provide valuable information on how to mitigate and control hazards. Us, 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. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the “location ⟶ type” strong association rule. E proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semistructured text. 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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