کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
808985 1468684 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of the uniaxial compressive strength of sandstone using various modeling techniques
ترجمه فارسی عنوان
پیش بینی قدرت فشاری یکسانی ماسه سنگ با استفاده از تکنیک های مختلف مدل سازی
کلمات کلیدی
استحکام فشاری یک طرفه، شبکه های عصبی مصنوعی، الگوریتم رقابت امپریالیست، آزمایشات غیر مخرب، شاخص بارگذاری نقطه
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات مهندسی ژئوتکنیک و زمین شناسی مهندسی
چکیده انگلیسی


• We measured several rock index parameters of sandstone as well as their UCS.
• Several LMR and NLMR models were developed for prediction of UCS.
• Two intelligent systems i.e., ANN and ICA–ANN were developed to predict UCS.
• A comparison between models was made to select the best UCS predictive model.

Sandstone blocks were collected from Dengkil site in Malaysia and brought to laboratory, and then intact samples prepared for testing. Rock tests, including Schmidt hammer rebound number, P-wave velocity, point load index, and UCS were conducted. The established dataset is composed of 108 cases. Consequently, the established dataset was utilized for developing the simple regression, linear, non-linear multiple regressions, artificial neural network, and a hybrid model, developed by integrating imperialist competitive algorithm with ANN. After performing the relevant models, several performance indices i.e. root mean squared error, coefficient of determination, variance account for, and total ranking, are examined for selecting the best model and comparing the obtained results. It is obtained that the ICA–ANN model is superior to the others. It is concluded that the hybrid of ICA–ANN could be used for predicting UCS of similar rock type in practice.

After performing the relevant models, several performance indices including the coefficient of determination (R2), root mean squared error (RMSE) and value account for (VAF) and total ranking are examined for selecting the best model. It is obtained that the ICA–ANN model is superior to others in terms of R2, RMSE, VAF and ranking herein.Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Rock Mechanics and Mining Sciences - Volume 85, May 2016, Pages 174–186
نویسندگان
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