• ثبت نام
    • ورود به سامانه
    مشاهده مورد 
    •   صفحهٔ اصلی
    • نشریات انگلیسی
    • Journal of Mining and Environment
    • Volume 8, Issue 2
    • مشاهده مورد
    •   صفحهٔ اصلی
    • نشریات انگلیسی
    • Journal of Mining and Environment
    • Volume 8, Issue 2
    • مشاهده مورد
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm

    (ندگان)پدیدآور
    Ataei, M.Sereshki, F.
    Thumbnail
    دریافت مدرک مشاهده
    FullText
    اندازه فایل: 
    659.1کیلوبایت
    نوع فايل (MIME): 
    PDF
    نوع مدرک
    Text
    Case Study
    زبان مدرک
    English
    نمایش کامل رکورد
    چکیده
    Like most limestone mines, which produce the raw materials required for cement companies, the transportation cost of the raw materials used in the Shahrood Cement Company is high. It has been tried to build the crushing and grinding plant close to the mine as much as possible. On the other hand, blasting has harmful effects, and the impacts of blast-induced damages on the sensitive machinery, equipment, and buildings are considerable. In such mines, among the blasting effects, blast-induced vibrations have a great deal of importance. This research work was conducted to analyze the blasting effects, and to propose a valid and reliable formula to predict the blast-induced vibration impacts in such regions, especially for the Shahrood Cement Company. Up to the present time, different indices have been introduced to quantify the blast vibration effects, among which peak particle velocity (PPV) has been widely considered by a majority of researchers. In order to establish a relationship between PPV and the blast site properties, different formulas have been proposed till now, and their frequently-used versions have been employed in the general form of , where W and D are the maximum charge per delay and the distance from the blast site, respectively, and , , and describe the site specifications. In this work, a series of tests and field measurements were carried out, and the required parameters were collected. Then in order to generalize the relationship between different limestone mines, and also to increase the prediction precision, the related data for similar limestone mines was gathered from the literature. In order to find the best equation fitting the real data, a simple regression model with genetic algorithm was used, and the best PPV predictor was achieved. At last, the results obtained for the best predictor model were compared with the real measured data by means of a correlation analysis.
    کلید واژگان
    Blasting
    Blast-Induced Vibration
    PPV
    Limestone Mine
    Cement Company
    Genetic Algorithm

    شماره نشریه
    2
    تاریخ نشر
    2017-04-01
    1396-01-12
    ناشر
    Shahrood University of Technology
    سازمان پدید آورنده
    School of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
    School of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

    شاپا
    2251-8592
    2251-8606
    URI
    https://dx.doi.org/10.22044/jme.2016.654
    http://jme.shahroodut.ac.ir/article_654.html
    https://iranjournals.nlai.ir/handle/123456789/242824

    مرور

    همه جای سامانهپایگاه‌ها و مجموعه‌ها بر اساس تاریخ انتشارپدیدآورانعناوینموضوع‌‌هااین مجموعه بر اساس تاریخ انتشارپدیدآورانعناوینموضوع‌‌ها

    حساب من

    ورود به سامانهثبت نام

    آمار

    مشاهده آمار استفاده

    تازه ترین ها

    تازه ترین مدارک
    © کليه حقوق اين سامانه برای سازمان اسناد و کتابخانه ملی ایران محفوظ است
    تماس با ما | ارسال بازخورد
    قدرت یافته توسطسیناوب