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مقاله An Investigation on the Rock Fragmentation Prediction Models Based on Rock and Rock Mass Properties تحت pdf دارای 8 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است

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توجه : در صورت  مشاهده  بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی مقاله An Investigation on the Rock Fragmentation Prediction Models Based on Rock and Rock Mass Properties تحت pdf ،به هیچ وجه بهم ریختگی وجود ندارد

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بخشی از متن مقاله An Investigation on the Rock Fragmentation Prediction Models Based on Rock and Rock Mass Properties تحت pdf :

سال انتشار: 1386

محل انتشار: سومین کنفرانس مکانیک سنگ ایران

تعداد صفحات: 8

نویسنده(ها):

Sami – MSc student, Department of mining and metallurgy engineering, Amirkabir University of technology, Tehran, Iran
Ataee-pour – Assistant professor, Department of mining and metallurgy engineering, Amirkabir University of technology, Tehran, Iran
Khoshrou – Assistant professor, Department of mining and metallurgy engineering, Amirkabir University of technology, Tehran, Iran

چکیده:

One of the basic requirements for being able to optimize blasting in mining is the ability to predict rock fragmentation with high accuracy.
Rock fragmentation depends on many variables such as rock and rock mass properties and blasting parameters. Owing to the fact that there is no complete theoretical solution for rock fragmentation prediction, empirical models for its prediction have been developed over the past three decades. In this study, a literature review on the effects of rock and rock mass properties on rock fragmentation has been performed. The most well known rock fragmentation prediction models are explained and scored based on rock and rock mass parameters. Investigations showed that there is a rather unanimous opinion on the importance of effect of various rock and rock mass parameters on blasting results. The literatureeview also revealed that the modified Kuz-Ram fragmentation model take into account more rock and rock mass parameters quantitatively rather than other rock fragmentation prediction models.

 

 

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