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.
|ジャーナル||International Journal of Mining, Reclamation and Environment|
|出版ステータス||出版済み - 3 1 2011|
All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Earth-Surface Processes
- Management of Technology and Innovation