Convolutional Neural Network Algorithm–Based Novel Automatic Text Classification Framework for Construction Accident Reports
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 12
Abstract
Construction sites remain one of the most hazardous workplaces globally. To improve workplace safety in the construction industry and reduce the personal injuries and socioeconomic impacts resulting from workplace accidents, tacit knowledge containing fundamental causes of accidents or specific contextual factors can be extracted from past accident narrative reports. However, manually analyzing unstructured or semistructured textual data stored in records is a daunting task, and requires the use of automated and intelligent technologies to achieve rapid and accurate knowledge acquisition. Therefore, this paper proposes a text self-classification model based on deep learning natural language processing (NLP) technology for automated classification of construction site accident cases by accident type. First, combined with two statistical measures, mutual information and information entropy, the preprocessed text data were subjected to phrase segmentation to identify more complete and accurate accident precursor information without human intervention. Then a complete multilayer and multisize convolutional neural network (CNN) model was constructed using pretrained Word2Vec word embeddings for text self-classification tasks. Finally, the test results of the CNN classification algorithm were compared with the practical application results of three shallow learning algorithms, and the performance of different types of classification algorithms was evaluated. The results showed that the CNN-based deep learning algorithm developed in this paper demonstrated excellent feature extraction and learning abilities in the task of automatic text classification in the field of NLP. This not only demonstrated that reliable accident prevention knowledge could be obtained from the textual descriptions of construction accidents, but also provided a novel model reference for document archiving and information retrieval.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Major Program of Philosophy and Social Science Research in Jiangsu University (Grant No. 2020SJZDA085), the China Postdoctoral Science Foundation (Grant No. 2019M662003), and the Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 2019K171).
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Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 12 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 4, 2023
Accepted: Jul 25, 2023
Published online: Sep 29, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 29, 2024
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