Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Determining Critical Cause Combination of Fatality Accidents on Construction Sites with Machine Learning Techniques
The construction industry is fraught with danger. The investigation of the causes of occupational accidents receives considerable attention. The purpose of this research is to determine the hierarchical relationship and critical combination of the fatal causes of accidents on construction sites. The framework for fatal cause attribute was established. Machine learning technologies were developed to predict the different types of accidents. Using feature importance, the hierarchical relationship of fatal causes was extracted. An iterative analysis algorithm was created to quantify the cause combinations. The F1 prediction score was 92.93%. The results revealed that combinations existed in fatal causes analysis, even if they were hierarchical. Furthermore, this study made recommendations for improving safety management and preventing occupational accidents. The findings of this study guide construction participants in providing early warning signs of fatal and unsafe factors, ultimately assisting in the prevention of fatalities.
Determining Critical Cause Combination of Fatality Accidents on Construction Sites with Machine Learning Techniques
The construction industry is fraught with danger. The investigation of the causes of occupational accidents receives considerable attention. The purpose of this research is to determine the hierarchical relationship and critical combination of the fatal causes of accidents on construction sites. The framework for fatal cause attribute was established. Machine learning technologies were developed to predict the different types of accidents. Using feature importance, the hierarchical relationship of fatal causes was extracted. An iterative analysis algorithm was created to quantify the cause combinations. The F1 prediction score was 92.93%. The results revealed that combinations existed in fatal causes analysis, even if they were hierarchical. Furthermore, this study made recommendations for improving safety management and preventing occupational accidents. The findings of this study guide construction participants in providing early warning signs of fatal and unsafe factors, ultimately assisting in the prevention of fatalities.
Determining Critical Cause Combination of Fatality Accidents on Construction Sites with Machine Learning Techniques
Qing Shuang (Autor:in) / Zerong Zhang (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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