دانلود مقاله ISI انگلیسی شماره 24453
ترجمه فارسی عنوان مقاله

برآورد هزینه LHD سنگ سخت با استفاده از رگرسیون واحد و چندگانه بر اساس تجزیه و تحلیل مولفه های اصلی

عنوان انگلیسی
Hard-rock LHD cost estimation using single and multiple regressions based on principal component analysis
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
24453 2012 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Tunnelling and Underground Space Technology, Volume 27, Issue 1, January 2012, Pages 133–141

ترجمه کلمات کلیدی
رگرسیون چند متغیره - تجزیه و تحلیل مولفه های اصلی - هزینه سرمایه - هزینه عملیات -
کلمات کلیدی انگلیسی
LHD, Multivariable regression, Principal component analysis, Capital cost, Operating cost,
پیش نمایش مقاله
پیش نمایش مقاله   برآورد هزینه LHD سنگ سخت با استفاده از رگرسیون واحد و چندگانه بر اساس تجزیه و تحلیل مولفه های اصلی

چکیده انگلیسی

In feasibility studies and mine planning, accurate and effective tools and methods facilitating cost estimation play an important role. Load–Haul–Dump (LHD) machines are a key loading and haulage equipment in most of the underground metal mines and hard rock tunnels. In this paper, a cost estimation model of these vehicles has been presented in the form of single and multivariable functions. These functions have been provided on the basis of costs types (i.e. capital and operating costs) and motor types (diesel and electric). Independent variables, in the single regression analysis is bucket capacity and in Multiple Linear Regression (MLR) analysis include bucket capacity, overall width, overall machine height and horse power (HP). The MLR is conducted in three steps. First, with the help of Principal Component Analysis (PCA), correlation between independent variables is omitted. Thereafter, significant PCs are selected and used as independent variables in the MLR functions. Finally, the cost relationships are established as functions of initial LHD variables. The mean absolute error rates are 11.59% and 6.87% for the single and multiple linear regression functions, respectively.