ارزیابی کارایی مدل های تجربی بر اساس طبقه بندی ژئومکانیکی (RMR) در پیش بینی مدول دگرشکلی توده سنگ و توسعه مدلی بر اساس سیستم فازی | ||
| روش های تحلیلی و عددی در مهندسی معدن | ||
| مقاله 1، دوره 8، شماره 16، آبان 1397، صفحه 1-11 اصل مقاله (832.14 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.29252/anm.8.16.1 | ||
| نویسنده | ||
| مهدی محمدی* | ||
| گروه مهندسی معدن، دانشگاه ولی عصر (عج) رفسنجان | ||
| چکیده | ||
| پیش‌بینی مدول دگرشکلی توده‌سنگ برای تحلیل و طراحی سازه‌های سنگی از اهمیت خاصی برخوردار است. زیرا مدول دگرشکلی بیانگر رفتار توده‌سنگ تحت تاثیر تنش‌ها است. تاکنون مدل‌های تجربی و هوش مصنوعی مختلفی برای پیش‌بینی مدول دگرشکلی توسعه داده شده است. از میان این مدل‌ها مدلی بهینه است که کم‌ترین پارامتر ورودی (زیرا هزینه‌ها و زمان برای تعیین پارامترهای ورودی کاهش پیدا می‌کند) و قابلیت پیش‌بینی مدول دگرشکلی در بهترین حالت را داشته باشد. در این مقاله ابتدا کارایی 19 مدل مختلف، که پارامتر ورودی آنها تنها طبقه‌بندی ژئومکانیکی توده‌سنگ (RMR) است؛ ارزیابی و دو مدل که بهترین تقریب را دارند، تعیین می‌شود. سپس به منظور پیش‌بینی دقیق‌تر مدول دگرشکلی، یک مدل بر اساس سیستم فازی توسعه داده شده (پارامتر ورودی آن RMR است) و نتایج با دو مدل انتخاب شده مقایسه گردید. برای این منظور 33 مجموعه داده از ساختگاه سدهای مختلف در کشور ایران جمع‌آوری شده است. برای ارزیابی مدل‌ها و همچنین اعتبارسنجی مدل توسعه داده شده، از شاخص‌های جذر میانگین مربعات خطا (RMSE) ، ضریب تعیین ((R2 و میانگین درصد خطای مطلق (MAPE) استفاده شده است. نتایج نشان می‌دهد، پیش‌بینی مدول دگرشکلی با استفاده از مدل فازی توسعه داده شده نسبت به مدل‌های تجربی دقیق‌تر است. | ||
| کلیدواژه‌ها | ||
| مدول دگرشکلی توده سنگ؛ مدل تجربی؛ مدل فازی؛ طبقه بندی ژئومکانیکی توده سنگ | ||
| عنوان مقاله [English] | ||
| Efficiency evaluation of empirical models on the basis of rock mass rating for prediction of deformation modulus in rock mass and developing a new models according to the fuzzy system | ||
| نویسندگان [English] | ||
| Mehdi Mohammadi | ||
| Dept. of Mining, Vali-e- Asr University of Rafsanjan | ||
| چکیده [English] | ||
| Summary Prediction of deformation modulus for rock mass in analysis and design of rock structures is very important. Because deformation modulus represents behavior of rock mass subjected to stresses. Up to now, various empirical models for prediction of deformation modulus have been developed. In this research, efficiency of different models for prediction of deformation modulus is evaluated. Then, according to the fuzzy model a sufficient model is developed and results are compared with the empirical models. Results indicated the efficiency of the new proposed model in comparison with the available models. Introduction Determining geo-mechanical parameters for analysis and design of rock structure is inevitable. Deformation modulus of rock mass is one of the most important geo-mechanical parameters. Estimation of deformation modulus for rock mass utilizing empirical models is widely used in civil and mining engineering projects. This is due to its less time requirement , cost and acceptable estimation. But there are differences between results of empirical model and real values of modulus which leads to uncertain decisions for designers in choosing appropriate model. Thus, it is important to investigate and evaluate the experimental models. Fuzzy model is one of the best models that is proposed for determining the deformation modulus in the present study. Methodology and Approaches For efficiency evaluation of 19 available models and also the proposed model, 33 data sets of different dam’s constructions in Iran is collected. Root Mean Squared Error , Mean Absolute Percentage Error and index of higher squared correlation coefficient are applied for evaluation of models’ performance. Results and Conclusions According to the performance indices, two models are selected from different empirical models that indicated the best predictions. In addition, results showed the efficiency of the proposed fuzzy model for prediction of deformation modulus. | ||
| کلیدواژه‌ها [English] | ||
| Rock Mass Rating, Deformation Modulus of Rock Mass, Empirical Models, Fuzzy Models | ||
| مراجع | ||
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