Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine

• 作 者: Mehdi Jamei Mahdi Hasanipanah Masoud Karbasi Iman Ahmadianfar Somaye Taherifar
• 机 构: Faculty of Engineering,Shohadaye Hoveizeh Campus of Technology,Shahid Chamran University of Ahvaz,Da
• 出 处: 岩石力学与岩土工程学报（英文版）
• 发表日期: 2021年6期
• 关键词: Blasting Flyrock distance Kernel extreme learning machine (KELM) Local weighted

Blasting is a common method of breaking rock in surface mines. Although the fragmentation with proper size is the main purpose, other undesirable effects such as flyrock are inevitable. This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm, called kernel extreme learning machine (KELM), by which the flyrock distance (FRD) is predicted. Furthermore, the other three data-driven models including local weighted linear regression (LWLR), response surface methodology (RSM) and boosted regression tree (BRT) are also developed to validate the main model. A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing, burden, stemming length and powder factor data as inputs and FRD as target. Afterwards, the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools. Finally, the results verify that the proposed KELM model on ac-count of highest correlation coefficient (R) and lowest root mean square error (RMSE) is more compu-tationally efficient, leading to better predictive capability compared to LWLR, RSM and BRT models for all data sets.