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    Human Error Analysis for Hydraulic Engineering: Comprehensive System to Reveal Accident Evolution Process with Text Knowledge

    Source: Journal of Construction Engineering and Management:;2022:;Volume ( 148 ):;issue: 009::page 04022093
    Author:
    Dan Tian
    ,
    Hao Liu
    ,
    Shu Chen
    ,
    Mingchao Li
    ,
    Chengzhao Liu
    DOI: 10.1061/(ASCE)CO.1943-7862.0002366
    Publisher: ASCE
    Abstract: Many human errors occur in hydraulic engineering construction, and these errors may lead to huge financial losses. A systematic and comprehensive accident analysis is required to reduce the probability of human error. Human error analysis is a lengthy and challenging process because the tendency is for accident data to be presented in text format. In addition, construction human error management requires an intelligent and efficient analysis system to ensure the timeliness of accident prevention and control. Thus, this study proposes a human error intelligent analysis system on the basis of text mining to automatically extract text knowledge and reveal the accident evolution process. Using hydraulic engineering construction text, a topic feature extraction model is built to extract words and improve the human factors analysis and classification system (HFACS) model. Then, a human error causation network that integrates text topic features, the improved HFACS model, and Bayesian theory is developed to intelligently identify human factors and quantify the human error evolution process. The analysis system proposed in this paper provides an effective way to mine and apply the experience-based knowledge available in hydraulic engineering construction text for the intelligent analysis and prediction of human error, thus improving the efficiency of human error management.
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      Human Error Analysis for Hydraulic Engineering: Comprehensive System to Reveal Accident Evolution Process with Text Knowledge

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4286168
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    • Journal of Construction Engineering and Management

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    contributor authorDan Tian
    contributor authorHao Liu
    contributor authorShu Chen
    contributor authorMingchao Li
    contributor authorChengzhao Liu
    date accessioned2022-08-18T12:11:27Z
    date available2022-08-18T12:11:27Z
    date issued2022/07/12
    identifier other%28ASCE%29CO.1943-7862.0002366.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286168
    description abstractMany human errors occur in hydraulic engineering construction, and these errors may lead to huge financial losses. A systematic and comprehensive accident analysis is required to reduce the probability of human error. Human error analysis is a lengthy and challenging process because the tendency is for accident data to be presented in text format. In addition, construction human error management requires an intelligent and efficient analysis system to ensure the timeliness of accident prevention and control. Thus, this study proposes a human error intelligent analysis system on the basis of text mining to automatically extract text knowledge and reveal the accident evolution process. Using hydraulic engineering construction text, a topic feature extraction model is built to extract words and improve the human factors analysis and classification system (HFACS) model. Then, a human error causation network that integrates text topic features, the improved HFACS model, and Bayesian theory is developed to intelligently identify human factors and quantify the human error evolution process. The analysis system proposed in this paper provides an effective way to mine and apply the experience-based knowledge available in hydraulic engineering construction text for the intelligent analysis and prediction of human error, thus improving the efficiency of human error management.
    publisherASCE
    titleHuman Error Analysis for Hydraulic Engineering: Comprehensive System to Reveal Accident Evolution Process with Text Knowledge
    typeJournal Article
    journal volume148
    journal issue9
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0002366
    journal fristpage04022093
    journal lastpage04022093-13
    page13
    treeJournal of Construction Engineering and Management:;2022:;Volume ( 148 ):;issue: 009
    contenttypeFulltext
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