این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Engineering، جلد ۳۲، شماره ۴، صفحات ۶۱۷-۶۲۷
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Prediction of Seismic Wave Intensity Generated by Bench Blasting Using Intelligence Committee Machines
چکیده انگلیسی مقاله In large open pit mines prediction of Peak Particle Velocity (PPV) provides useful information for safe blasting. At Sungun Copper Mine (SCM), some unstable rock slopes facing to valuable industrial facilities are both expose to high intensity daily blasting vibrations, threatening their safty. So, controlling PPV by developing accurate predictors is essential. Hence, this study proposes improved strategies for prediction of PPV by maximum charge per delay and distance using the concept of Intelligent Committee Machine (ICM). Besides the Empirical Predictors (EPs) and two Artificial Intelligence (AI) models of ANFIS and ANN, four different ICMs models including Simple and Weighted Averaging ICM (SAICM and WAICM) and First and Second order Polynomial ICM (FPICM and SPICM) in conjunction with genetic algorithm, proposed for the prediction of PPV. Performance of predictors was studied considering R2, RSME and VAF indices. Results indicate that ICM methods have superiority over EPs, ANN and ANFIS, and among the ICM models while SAICM, WAICM and FPICM performing near to each other SPICM overrides all the models. R2 and RSME of the training and testing data for SPICM are 0.8571, 0.8352 and 11.0454, 12.3074, respectively. Finally, ICMs provides more accurate and reliable models rather than individual AIs.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله
نشانی اینترنتی http://www.ije.ir/article_86163_507bf0f6810646fa38283b48b4aaadd7.pdf
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/409/article-409-2061627.pdf
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات