Journal
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
Volume 13, Issue 6, Pages 1438-1451Publisher
SCIENCE PRESS
DOI: 10.1016/j.jrmge.2021.07.007
Keywords
Blasting; Flyrock distance; Kernel extreme learning machine (KELM); Local weighted linear regression (LWLR); Response surface methodology (RSM)
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The study evaluated a novel kernel-based extreme learning machine algorithm for predicting flyrock distance in blasting processes, and developed three other data-driven models to validate the algorithm. Analysis of a database from three quarry sites in Malaysia showed that the KELM model had better predictive capability compared to other models.
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 account of highest correlation coefficient (R) and lowest root mean square error (RMSE) is more computationally efficient, leading to better predictive capability compared to LWLR, RSM and BRT models for all data sets. (C) 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.
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