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

Behrooz Keshtegar; Jamshid Piri; Rini Asnida Abdullah; Mahdi Hasanipanah; Mohanad Muayad Sabri Sabri; Binh Nguyen Le

doi:10.3389/fpubh.2022.1094771

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

Front Public Health. 2023 Feb 1:10:1094771. doi: 10.3389/fpubh.2022.1094771. eCollection 2022.

Authors

Behrooz Keshtegar¹, Jamshid Piri², Rini Asnida Abdullah³, Mahdi Hasanipanah^{3

4}, Mohanad Muayad Sabri Sabri⁵, Binh Nguyen Le^{4

6}

Affiliations

¹ Department of Civil Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran.
² Department of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol, Iran.
³ Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
⁴ Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
⁵ Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.
⁶ School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam.

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 are 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). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an 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 (R ²) 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.

Keywords: RSM; SVR; blasting; ground vibration; hybrid soft computing method.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Linear Models
Vibration*

Grants and funding

This research was partially funded by the Ministry of Science and Higher Education of the Russian Federation, under the strategic academic leadership program Priority 2030 (Agreement 075-15-2021-1333, dated September 30, 2021).