000 | 02891aab a2200241 4500 | ||
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008 | 240216b20232023|||mr||| |||| 00| 0 eng d | ||
022 | _a0733-9364 | ||
100 |
_aLuo, Xixi _9880920 |
||
100 |
_aLi, Xinchun _9880921 |
||
100 |
_aSong, Xuefeng _9880922 |
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100 |
_aLiu, Quanlong _9880923 |
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245 | _aConvolutional Neural Network Algorithm–Based Novel Automatic Text Classification Framework for Construction Accident Reports | ||
300 | _a1-12 p. | ||
520 | _aConstruction 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. | ||
650 |
_aDeep Learning _9166900 |
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650 |
_aNatural Language Processing (NLP) _9880924 |
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650 | _aConstruction Safety | ||
650 |
_aText Classification _9692730 |
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650 |
_aAccident Injury Types _9880925 |
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773 | 0 |
_dReston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute _x07339364 _tASCE: Journal of Construction Engineering and Management |
|
856 | _uhttps://doi.org/10.1061/JCEMD4.COENG-13523 | ||
942 |
_2ddc _n0 _cART _o14993 _pMr. Muhammad Rafique Al Haj Rajab Ali (Late) |
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999 |
_c814982 _d814982 |