AUTHOR=Keshtegar Behrooz , Piri Jamshid , Asnida Abdullah Rini , Hasanipanah Mahdi , Muayad Sabri Sabri Mohanad , Nguyen Le Binh 

TITLE=Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1094771

DOI=10.3389/fpubh.2022.1094771

ISSN=2296-2565

ABSTRACT=Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization is of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). RSM-SVR applies RSM in the first calibrating process and SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of RSM, which are obtained using the input-data of problems, are used as the input-dataset for the regression process of SVR. The effectiveness and agreement of RSM-SVR were compared to those of SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR and RSM models, respectively.