نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسی ارشد، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود
2 دکتری تخصصی، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود
3 استاد، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Summary
This paper presents statistical and intelligent models to predict stability of different surfaces in underground open stope mining method on the basis of the stability graph method. For this purpose, logistic regression and support vector machine were used to predict the stability state of walls and back, separately.
Introduction
The open stope mining method can be considered as the most common method for underground mining of hard rock metal mines around the world. The most common method for the design and evaluation of stopes in the mining technique is the stability graph method which was introduced by Mathews et al. in 1981. The stability graph method is based on data collected from different mines including hydraulic radius, stability number (tunneling modified quality index, rock stress factor, joint adjustment factor, and gravity factor), and related stability conditions. The empirical nature of this method, which results in uncertainties in the values of the parameters, and the existence of different types of charts with different stability regions, interpreting the results becomes a challenge. This increases the risk of error in interpreting the results. In addition to these problems, considering the same conditions for all surfaces, such as walls and back, causes obvious errors in the results of this method. In this regard, this paper incorporated statistical and intelligent methods to predict stability state for a database of open stopes by means of stability graph parameters. The novelty of this paper in addition to the used methods is developing models for evaluation of walls and back separately.
Methodology and Approaches
New models to predict the stability state of walls and surface of underground open stopes were developed by using statistical and intelligent techniques. For this purpose, logistic regression (LR), as the most common statistical method for classification, was used. Besides, support vector machine (SVM), which is based on the machine learning theory, was applied as a powerful intelligent technique for classification problems.
Results and Conclusions
Results of developed models were compared to those of the stability graph method. The following main conclusions were derived from this study:
The accuracy of back stability prediction by using the stability graph method, LR and SVM were 29%, 86%, and 95%, respectively.
The accuracy of wall stability prediction by using the stability graph method, LR and SVM were 71%, 81%, and 90%, respectively.
In comparison with the stability graph method, LR models of back and walls increase the accuracy of predictions by 57% and 10%, respectively.
In comparison with the stability graph method, SCM models of back and walls increase the accuracy of predictions by 66% and 19%, respectively.
It was concluded that SVM models possess higher performance in the prediction of stable states when compared to the LR models.
کلیدواژه‌ها [English]
1- مقدمه
در اوایل دهه 1980 میلادی بخش بزرگی از صنعت معادن فلزی زیرزمینی، روش استخراج خود را از روشهای مرسوم نظیر کندن و پرکردن به سمت روش استخراج کارگاه باز[i] تغییر دادند. به طوری که بر اساس گزارشهای سال 1995 حدود 90 درصد معادن فلزی کانادا با این روش استخراج می‌شدند [1].معدن‌کاری کارگاه باز روش معدن‌کاری زیرزمینی تودهای است که برای استخراج کانسارهای فلزی نسبتاً شیبدار و دارای سنگهای معدنی و دربرگیرنده با کیفیت خوب مناسب است. کارگاههای باز عموماً به کارگاههایی گفته میشوند که به طور کلی نیازی به نگهداری ندارند و در آنها فقط به صورت جزئی از ملزومات نگهداری همچون چوب، بولت و غیره استفاده میشود. همچنین این روش یک روش بدون راهرو است که آن را تبدیل به یکی از ایمنترین روشهای معدن‌کاری زیرزمینی کرده‌است [2]. این روش به دو صورت کارگاه باز با چالهای انفجاری عرضی[ii] (شکل 1-الف) و کارگاه باز طولی[iii] (شکل 1-ب) انجام میشود [3].
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