دانلود مقاله انگلیسی رایگان:پیش بینی مبتنی بر هوش مصنوعی از نتایج ایمنی ساخت مستقل از ویژگی های جهانی - 2020
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  • AI-based prediction of independent construction safety outcomes from universal attributes AI-based prediction of independent construction safety outcomes from universal attributes
    AI-based prediction of independent construction safety outcomes from universal attributes

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    AI-based prediction of independent construction safety outcomes from universal attributes


    ترجمه فارسی عنوان مقاله:

    پیش بینی مبتنی بر هوش مصنوعی از نتایج ایمنی ساخت مستقل از ویژگی های جهانی


    منبع:

    Sciencedirect - Elsevier - Automation in Construction, 118 (2020) 103146. doi:10.1016/j.autcon.2020.103146


    نویسنده:

    Henrietta Bakera, Matthew R. Hallowellb,⁎, Antoine J.-P. Tixierc


    چکیده انگلیسی:

    This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted from attributes with machine learning. Like in the original study, we use Natural Language Processing (NLP) to extract fundamental attributes from raw incident reports and machine learning models are trained to predict safety outcomes. The outcomes predicted here are injury severity, injury type, body part impacted, and incident type. However, unlike in the original study, safety outcomes were not extracted via NLP but were provided by independent human annotations, eliminating any potential source of artificial correlation between predictors and predictands. Results show that attributes are still highly predictive, confirming the validity of the original approach. Other improvements brought by the current study include the use of (1) a much larger dataset featuring more than 90,000 reports, (2) two new models, XGBoost and linear SVM (Support Vector Machines), (3) model stacking, (4) a more straightforward experimental setup with more appropriate performance metrics, and (5) an analysis of per-category attribute importance scores. Finally, the injury severity outcome is well predicted, which was not the case in the original study. This is a significant advancement.
    Keywords: Artificial intelligence | Machine learning | Supervised learning | NLP | Reports | Construction | Safety


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 792 کیلوبایت

    قیمت: رایگان


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