پیش‌بینی مهمترین خصوصیت ژئومکانیکی توده سنگ با استفاده از روش‌های الگوریتم جستجوی هارمونی و الگوریتم مبتنی بر آموزش و یادگیری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده‌ی مهندسی علوم زمین، دانشگاه صنعتی اراک.

2 دانشکده مهندسی علوم زمین، دانشگاه صنعتی اراک

چکیده

 به علت وجود مشکلات در ارزیابی تغییر شکل توده‌سنگ‌های درزه‌دار در مقیاس آزمایشگاهی، می‌توان برای در نظر گرفتن اثر مقیاس و درزه‌ها از روش‌های مختلف آزمایش برجا مانند آزمایش بارگذاری صفحه‌ای و دیلاتومتری استفاده کرد. اگر چه این روش‌ها در حال حاضر بهترین هستند، اما گران، زمان‌بر و دارای مشکلات عملیاتی در حین اجرا هستند. بنابراین در این مقاله برای غلبه بر این مشکلات، از الگوریتم‌های جدید جستجوی هارمونی (HS) و الگوریتم بهینه‌سازی مبتنی بر آموزش و یادگیری (TLBO) برای تخمین غیرمستقیم مدول تغییرشکل‌پذیری توده‌ سنگ استفاده شده‌است. در این مدل‌ها از امتیاز رده‌بندی توده سنگ (RMR)، مقاومت فشاری تک‌محوره سنگ بکر (UCS)، عمق (D) و مدول الاستیسیته سنگ بکر (Ei) به‌عنوان پارامترهای ورودی و از مدول تغییرشکل‌پذیری توده‌سنگ (Em) به‌عنوان پارامتر خروجی استفاده شده‌است. در این مقاله، با استفاده از شاخص‌های آماری مختلف، مدل ایجادشده توسط الگوریتم‌ها، ارزیابی و اعتبارسنجی می‌شود. نتایج ارزیابی نشان داد که دقت رابطه برای الگوریتم جستجوی هارمونی با استفاده از شاخص‌های R2 و VAF حدود 93/0-91/0 و درصد خطا با استفاده از شاخص‌های RMSE وMSE  بین 0042/0-000017/0 است هم‌چنین دقت رابطه برای الگوریتم بهینه‌سازی مبتنی بر آموزش و یادگیری با استفاده از روش R2 و VAF حدود 95/0-92/0 و درصد خطا با استفاده از شاخص‌های RMSE وMSE  بین 0032/0-000010/0 به‌دست آمد.

کلیدواژه‌ها

عنوان مقاله [English]

Predicting the most important geomechanical parameter of rock mass using the harmony search and teaching learning based optimization algorithms

نویسندگان [English]

  • Hadi Fattahi 1
  • Farshad Malekmahmoudi 2
  • Hossein Ghaedi 2
1 Faculty of Earth Sciences Engineering, Arak University of Technology, Iran
2 Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran.
چکیده [English]

Due to the difficulties in assessing the deformation of jointed aggregates at the laboratory scale, various in situ testing methods such as plate loading test and dilatometry can be used to consider the effect of scale and joints. Although these methods are currently the best, they are expensive, time consuming, and have operational difficulties during implementation. Therefore, in this paper, to overcome these problems, new harmony search algorithms (HS) and teaching-learning optimization algorithm (TLBO) are used to indirectly estimate the modulus of rock mass deformation. In these models, the rock mass classification score (RMR), uniaxial compressive strength of virgin rock (UCS), depth (D) and the modulus of elasticity of intact rock (Ei) as input parameters and the modulus of rock mass deformability (Em) as output parameter Used. In this paper, Using different statistical indicators, the model created by the algorithms is evaluated and validated. The evaluation results showed that the relationship accuracy for the harmonic search algorithm using R2 and VAF methods is about 0.91-0.93 and using the RMSE and MSE methods is between 0.000017-0.0042. Also, the relationship accuracy for the optimization algorithm Based on teaching and learning using R2 and VAF methods, about 0.92-0.95 and using RMSE and MSE methods were between 0.00001- 0.0032.

کلیدواژه‌ها [English]

  • Deformation of modulus
  • harmony search algorithm
  • teaching-learning optimization algorithm
  • rock mass

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