نوع مقاله: علمی - پژوهشی

نویسندگان

1 تونلسازی،دانشکده مهندسی معدن، دانشگاه امیرکبیرتهران، ایران

2 استخراج معدن، مهندسی معدن و متالوژی، دانشگاه صنعتی امیرکبیر، تهران، ایران

3 استادیار دانشکده مهندسی معدن و متالورژی، دانشگاه امیرکبیر تهران، ایران

چکیده

ایجادپدیده‌ی حفر بیش از اندازه مقطع تونل یا همان اضافه‌حفاری مقطع تونل در مراحل اجرایی پروژه‌های تونل‌سازی، همواره از مهم‌ترین مسائلی است که ذهن متصدیان فنی و اجرایی این پروژه‌ها را به خود معطوف داشته است. امروزه با توجه به پیشرفت صنعت و ورود فنّاوری‌های نوین به صنعت تونل‌سازی و پذیرفته شدن تدریجی، روش های جدیدی جایگزین روش های سنتی (حفاری و آتشکاری) شده است. اگرچه تا حد زیادی مسئله ایجاد حفاری خارج از طرح و نقشه، به کنترل اجراکنندگان پروژه درآمده، ولی هیچ‌گاه وجود این مسئله مهم و اساسی در پروژ‌ه‌های تونلی به‌طورکلی حذف نشده است. در این تحقیق با استفاده از شبکه‌های هوشمند پیش بینی و بهینه سازی اضافه-حفاری مورد بحث قرار گرفت. پس از انتخاب بهترین مدل براساس امتیاز دهی، مدل منتخب برای بهینه سازی مورد استفاده قرار گرفت. مقادیر شاخص های آماری ضریب تعیین (R2) و مجذور میانگین مربعات خطا (RMSE) مدل منتخب به ترتیب برای آموزش و آزمایش برابر با ۹۲۱/۰، ۴۰۲۸/۰ و ۹۲۳/۰ و ۴۲۷۷/۰ بود. الگوریتم زنبور عسل که یکی از الگوریتم‌های جدید بهینه سازی است برای بهینه سازی این پارامترهای الگوی انفجار استفاده شد. با توجه به اینکه اضافه حفاری از مشکلات اصلی در حفر تونل می‌باشد، کاهش این مقدار می‌تواند کمک به سزایی برای تونل و پایداری آن داشته باشد. پس از ساختن چندین مدل بهینه سازی و تغییرات وزن‌های آن، مقدار بهینه آن برای اضافه حفاری مقطع تونل ۶۳/۱ مترمربع بدست آمد که نسبت به کمترین مقدار تجربه شده در اجرا (۰۵۵/۳ مترمربع) ۴۷ درصد کاهش یافته است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Optimization of Over break in the implementation of tunnels by blasting method using intelligent methods

نویسندگان [English]

  • mohammadreza Koopialipoor 1
  • Ebrahim Noroozi Ghaleini 2
  • Hasan madani 3

1 Tunneling,Mining, Amirkabir University,Iran

2 Mining, Mining & Metallurgy Engineering, Amirkabir university of technology, Tehran, Iran

3 Mining Department, Amirkabir University,

چکیده [English]

Over break phenomenon in the executive process of a tunneling project is always one of the most important issues. Nowadays, according to the progress of industry and entrance of new technologies to tunneling industry and gradual acceptance, the new methods are replaced instead of traditional methods (drilling and blast). Although, largely the issue of over break has been controlled by project implementers, but the existence of this major issue in tunnel projects has never been eliminated. In this research, using intelligent network of prediction and optimization, over break was discussed. After selecting the best model based on scoring, the selected model was used for optimization. The R2 and RMSE values of the selected model were 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively. The Artificial bee colony (ABC) algorithm, which is one of the new optimization algorithms, was used to optimize these parameters of the explosion pattern. Due to the fact that over break is one of the main problems in tunneling, this reduction can contribute to a good tunnel and stability. After creation several models of optimization and variations in its weights, the optimum amount for the over break was 1.63 m2, which is 47% less than the lowest value (3.055 m2). The optimal pattern can be obtained with the least amount of the over break.

کلیدواژه‌ها [English]

  • Tunnel
  • over break
  • Artificial neural networks
  • Artificial bee colony

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