A review study of predictive model blast vibration attenuation equation by using neural network as an evaluator

Sugeng Wahyudi, Hideki Shimada, Ganda Marihot Simangunsong, Takashi Sasaoka, Kikuo Matsui, Suseno Kramadibrata, Budi Sulistianto

研究成果: ジャーナルへの寄稿記事

2 引用 (Scopus)

抄録

Over the years, a number of empirical attenuation equations (AEs) have been proposed. However, many established AEs are not accurate enough and sometimes they are confusing to use, particularly when the parameter associated with blasting and geological condition changes. Nowadays an accurate AE is an important requirement for a coal mine such as Kaltim Prima Coal (KPC) whose mining area in the East Kutai regency is located close to a residential area, the Sangatta town. In this study, several important and widely used predictors were used to predict peak particle velocity, while a back propagation artificial neural network is used as a comparator to evaluate the established AEs. Through this study, it is proposed that susceptibility assessment of conventional AEs be employed as tool to evaluate established AEs in a more adaptable way.

元の言語英語
ページ(範囲)69-85
ページ数17
ジャーナルInternational Journal of Mining, Reclamation and Environment
25
発行部数1
DOI
出版物ステータス出版済み - 3 1 2011

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Coal mines
vibration
Neural networks
Blasting
Backpropagation
back propagation
coal mining
blasting
artificial neural network
coal mine
Vibration
Back propagation
Predictors
Evaluator
Artificial neural network
Coal mining
Susceptibility

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology
  • Geology
  • Earth-Surface Processes
  • Management of Technology and Innovation

これを引用

A review study of predictive model blast vibration attenuation equation by using neural network as an evaluator. / Wahyudi, Sugeng; Shimada, Hideki; Simangunsong, Ganda Marihot; Sasaoka, Takashi; Matsui, Kikuo; Kramadibrata, Suseno; Sulistianto, Budi.

:: International Journal of Mining, Reclamation and Environment, 巻 25, 番号 1, 01.03.2011, p. 69-85.

研究成果: ジャーナルへの寄稿記事

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