پیش‌بینی نرخ نفوذ ماشین‌ TBM در حفر فضاهای زیرزمینی با استفاده از الگوریتم‌های ژنتیک، سیستم ایمنی مصنوعی، پژواک صدای دلفین و گرگ خاکستری-مطالعه موردی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی علوم زمین دانشگاه صنعتی اراک

2 دانشکده مهندسی علوم زمین، دانشگاه صنعتی اراک

چکیده

بعلت تخمین دقیق زمان حفاری و برآورد هزینه‌های اجرایی، پیش‌بینی نرخ نفوذ در حفاری مکانیزه حائز اهمیت است. از طرفی به‌دلیل قیمت بالای ماشین‌ حفاری تمام مقطع (TBM)، ارزیابی عملکرد در حفاری با استفاده از این ماشین بسیار اهمیت دارد. یکی از شاخص‌های ارزیابی عملکرد ماشین TBM، پیش‌بینی نرخ نفوذ این دستگاه می‌باشد. طی سالیان اخیر توسط محققین روش‌ها و روابط متنوعی برای پیش‌بینی نرخ نفوذ پیشنهاد شده که هر کدام ویژگی‌های خاص خود را داشته و براساس پارامترهای مربوط به توده سنگ و مشخصات ماشین ارائه شده‌اند. هدف از نگارش این مقاله توسعه مدل‌های دقیق پیش‌بینی برای تخمین نرخ نفوذ TBM با استفاده از الگوریتم‌های فراابتکاری نظیر الگوریتم ژنتیک، الگوریتم سیستم ایمنی مصنوعی، الگوریتم پژواک صدای دلفین و الگوریتم گرگ خاکستری است. برای ساخت مدل‌های پیش‌بینی از 153 داده که شامل: مقاومت فشاری تک محوره سنگ بکر (UCS)، تردی سنگ بکر(BI)، زاویه بین صفحات ناپیوستگی و جهت حفاری TBM (α) و فاصله بین صفحات ناپیوستگی (DPW) به عنوان پارامترهای ورودی استفاده شده است. همچنین برای ارزیابی مدل‌ها از شاخص‌های آماری نظیر میانگین خطای مربعات (MSE) و ضریب همبستگی مربع (R2) استفاده شده است. نتایج مدلسازی‌ها نشان می‌دهد الگوریتم ژنتیک با مقادیر012/0=MSETrain، 02/0=MSETest ، 9319/0=R2Train و 8473/0=R2Test از دقت قابل قبولی در پیش‌بینی نرخ نفوذ TBM (نسبت به سایر الگوریتم‌ها) برخوردار است.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of TBM penetration rate in excavating underground spaces using genetic, artificial immune system, dolphin echolocation and gray wolf algorithms-A case study

نویسندگان [English]

  • Hadi Fattahi 1
  • Mohammad Ali Shirinzadeh 2
1 Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran.
2 Faculty of Earth Sciences Engineering. Arak University of Technology
چکیده [English]

One of the indicators for evaluating the performance of a tunnel drilling machine is predicting the penetration rate of this machine. There are various methods and relationships for predicting the penetration rate, each of which has its own characteristics and are presented based on the parameters related to the rock mass and the characteristics of the machine. In this study, genetic, artificial immune system, dolphin echolocation and grey wolf algorithms were used to predict the penetration rate of TBM. In this regard, the database consists of 153 data (122 data for train and 31 data for test) including parameters of intact rock such as strength and brittleness and rock mass characteristics such as distance between planes of weakness and orientation of discontinuities along with TBM machine performance in Queens tunnel has been collected. Mean square error (MSE) and square correlation coefficient (R2) have been used to estimate the error rate between the developed methods. Considering the key parameters of rock mass and intact rock and TBM, relationships to predict the penetration rate are presented and based on statistical analysis, the best relationship is selected. The results are compared with the real data and the results of other models show that the values penetration rate predicted by the genetic algorithm with MSETrain=0.012, MSETest=0.02, R2Train=0.9319 and R2Test=0.8473,has acceptable accuracy compared to other methods.

کلیدواژه‌ها [English]

  • Penetration rate of TBM
  • Genetic algorithm
  • Artificial immune system algorithm
  • Dolphin echolocation algorithm
  • Grey wolf algorithm
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