000 02891aab a2200241 4500
008 240216b20232023|||mr||| |||| 00| 0 eng d
022 _a0733-9364
100 _aLuo, Xixi
_9880920
100 _aLi, Xinchun
_9880921
100 _aSong, Xuefeng
_9880922
100 _aLiu, Quanlong
_9880923
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
650 _aNatural Language Processing (NLP)
_9880924
650 _aConstruction Safety
650 _aText Classification
_9692730
650 _aAccident Injury Types
_9880925
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)
999 _c814982
_d814982