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

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)69-85
Number of pages17
JournalInternational Journal of Mining, Reclamation and Environment
Volume25
Issue number1
DOIs
Publication statusPublished - Mar 1 2011

All Science Journal Classification (ASJC) codes

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

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