Luo, X; Li, X; Song, X and Liu, Q (2023) Convolutional neural network algorithm-based novel automatic text classification framework for construction accident reports. Journal of Construction Engineering and Management, 149(12): 04023128, ISSN 0733-9364
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | accident injury types; construction safety; deep learning; natural language processing; text classification |
| Index terms: | deep learning, multilayer, information retrieval, construction industry, knowledge acquisition, learning algorithm, construction site, neural network, injury, accident prevention, workplace safety, entropy, tacit knowledge, construction safety, construction site accident, construction accident, narrative |
| Subjects: | qualitative and interpretive research, professional development, health risk and incident analysis, algorithms, specialized materials and systems, occupational health, industry analysis, environmental health, student development, thermal systems, occupational health and safety management, data management, health conditions and diseases, artificial intelligence, work location |
| Topics: | Site Management, Education, Digital Applications, Sustainability, Research Practice, Construction Materials, Information Management, Health and Safety |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here